{"as_of":"2026-08-16T10:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b79db3681b5b61beafd337c7d7bcb6ec3df5acab2084005380c3e07288d0c8e2","coverage":[{"denominator":37,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T11:46:42.007981Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.08399/citation-record","integrity":"/paper/1908.08399/integrity","json":"/paper/1908.08399/citation-record.json","paper":"/paper/1908.08399"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.684334Z","title":"Recurrent continuous translation models,","venue":null,"work_id":"14f093b1-0f2e-4135-bf1b-f45660eccd12","year":2013},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.814207Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:dcde184d6521c1d15971ffb4e9bb0e9126c6a42320bb8f44a45bf75806d5e5dc","observation_id":"1de49248-5ad0-4231-b77b-8fb48227a90b","resolution":{"observed_at":"2026-08-14T11:46:42.689360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.667509Z","title":"Sequence to sequence learning with neural networks,","venue":null,"work_id":"4e6fa7f4-f95c-427d-bbbf-c9ee74fe3537","year":2014},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.820176Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:5a7897b2cdf0c3a5194c37283b5b4dbf8445829b456c2d5c543b11a1914b7a48","observation_id":"fb830855-2519-4d5a-a0d7-a2ac175e4770","resolution":{"observed_at":"2026-08-14T11:46:42.673262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.647885Z","title":"Attention is all you need,","venue":null,"work_id":"ed448684-a758-445e-9865-c460f3737840","year":2017},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.825469Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:acc722dd376811d61431fed9aba6cd5bfe01bb9f43cbfb6938e78b87290804a6","observation_id":"7f3e4c6a-0f2c-455e-9634-520514d13075","resolution":{"observed_at":"2026-08-14T11:46:42.653913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.632157Z","title":"Statistical phrase-based translation,","venue":null,"work_id":"05de6fc3-e32f-4e60-b5a5-33045b80ae8f","year":2003},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.830785Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:314d8a0923298c43e45d793685692fda01997d948cac34bbdfc6986314de3a91","observation_id":"feb0c86d-f03c-4750-bc94-db560ec82f54","resolution":{"observed_at":"2026-08-14T11:46:42.637339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.616310Z","title":"Neural machine translation by jointly learning to align and translate,","venue":null,"work_id":"9b53c95a-a65e-4be0-8318-10820efbb116","year":2015},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.836175Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:ed2feb5ce0d61036b4a6936c43545aed33ff21019c715b0fd872a7e003ebe618","observation_id":"a0ff1293-8524-49cd-be6b-85ec38d0a15f","resolution":{"observed_at":"2026-08-14T11:46:42.621561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.599893Z","title":"Effective approaches to attention-based neural machine translation,","venue":null,"work_id":"d20d1cc8-0d4d-4d95-a319-9aa1e747d446","year":2015},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.841970Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:b7ebff2246ac5ce6f8e761247faad10cb1140a4388e6123ed1165936ef3e9981","observation_id":"b3456be3-4f30-4395-b7bb-de68efbfcca6","resolution":{"observed_at":"2026-08-14T11:46:42.605272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.583983Z","title":"Sequence level training with recurrent neural networks,","venue":null,"work_id":"5e8cbb8b-b6b4-4f92-848b-fa4fca3e01cb","year":2016},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.847494Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:11bfa7f660559125019b01e9fe30b217a3902a431d421c02cf4b9425402a2813","observation_id":"2b1d764e-18ea-4544-af1b-8935da7bfbf1","resolution":{"observed_at":"2026-08-14T11:46:42.589080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.566896Z","title":"Bleu: a method for automatic evaluation of machine translation,","venue":null,"work_id":"28732f88-6fd6-4b10-9137-91964be3d95a","year":2002},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.852369Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:901fafcec8c2b88b5874be05de206481037a8f50935f0e3a62118a84e798cf48","observation_id":"9352bb8a-f06b-452b-adf0-ff97c11a9606","resolution":{"observed_at":"2026-08-14T11:46:42.572478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.549896Z","title":"Scheduled sampling for sequence prediction with recurrent neural networks,","venue":null,"work_id":"0f4d0c83-c08a-44a6-963e-a5c8fa0eed92","year":2015},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.857652Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:fa5ab85257927e29055b3bc092eea26d3184a4b231fd1460f92855058b1786a5","observation_id":"c0711cde-783a-4a77-9f42-9b60f1e7820f","resolution":{"observed_at":"2026-08-14T11:46:42.555650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.532642Z","title":"Sequence-to-sequence learning as beam-search optimization,","venue":null,"work_id":"853ab66b-730c-4348-86f6-d93761d37fee","year":2016},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.864505Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:7e0099f5bbc7f2e7438bbc8feccb0ee731a5df54e4e8fd08bb78c996e9dab4a6","observation_id":"aba1e022-9cbc-44a0-9d47-06003bb1a17c","resolution":{"observed_at":"2026-08-14T11:46:42.537993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.515579Z","title":"Minimum risk training for neural machine translation,","venue":null,"work_id":"5e69f89d-8589-4c37-8ba1-c9da58c02edd","year":2016},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.869846Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:3aae9154834ee5f1e98b1eb5616f5705fafc54b6a6eacbcd2f5664cc3aee8736","observation_id":"85004e52-9475-44c3-b7f4-e8431f821f33","resolution":{"observed_at":"2026-08-14T11:46:42.521231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.496009Z","title":"An actor-critic algorithm for sequence prediction,","venue":null,"work_id":"c3968573-0815-49ee-b05c-56f5aabdee7f","year":2017},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.875271Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:a5d2f4cf49eba8aa8e009c206fdab3040bc9c4742449544163daa3d0b310f8df","observation_id":"584c7b26-4d47-4275-9e0d-9452e195736f","resolution":{"observed_at":"2026-08-14T11:46:42.503514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.477602Z","title":"On information and sufficiency,","venue":null,"work_id":"9c17e682-c1c6-492f-a5df-49b84aeff689","year":1951},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.880274Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:7f9c6fb423a3371f011ec486ac06a917e446571e851a6f10b35f737b3f549af6","observation_id":"335227eb-f3a3-489f-a0c5-d9a5b208337d","resolution":{"observed_at":"2026-08-14T11:46:42.484284Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05101","last_updated":"2015-11-16T19:43:19Z","snapshot_observed_at":"2026-08-14T22:22:43.562000Z","submitted_at":"2015-11-16T19:43:19Z","title":"How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.05101","snapshot_observed_at":"2026-08-14T11:46:41.885885Z","title":"How (not) to train your generative model: Scheduled sampling, likelihood, adversary?","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.885885Z"},"links":{"cited_paper":"/paper/1511.05101","citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:a47b20ef427827bc01548fc8d44e25e7dfc452beae689991d0082601a1ebf341","observation_id":"d40a1ae4-1810-409e-a7b5-f219340ba5ca","resolution":{"observed_at":"2026-08-14T11:46:41.885885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.03122","last_updated":"2017-07-25T01:40:57Z","snapshot_observed_at":"2026-08-14T21:02:01.058213Z","submitted_at":"2017-05-08T23:25:30Z","title":"Convolutional Sequence to Sequence Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.03122","snapshot_observed_at":"2026-08-14T11:46:41.890917Z","title":"Convolutional sequence to sequence learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.890917Z"},"links":{"cited_paper":"/paper/1705.03122","citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:938a6fa2229f0f2353bdaa462fcea2c59533345f543bd8f61770344d711e4df8","observation_id":"9bfa7ed6-ea05-47f9-8597-e683760790a1","resolution":{"observed_at":"2026-08-14T11:46:41.890917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.458690Z","title":"Learning phrase representations using rnn encoder–decoder for statistical machine translation,","venue":null,"work_id":"3acf6090-9466-4c17-a433-2c97f988b154","year":2014},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.895789Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:70ccb3c4fdf61628ff3a804a68b2045d6f2b85b35a960f631cb6f63633a583e9","observation_id":"be341df2-0020-4650-ba0e-12025336d7cc","resolution":{"observed_at":"2026-08-14T11:46:42.465888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.441218Z","title":"Long short-term memory,","venue":null,"work_id":"7880a338-e684-42c6-9301-61a2f0b50a9e","year":1997},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.901212Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:981164d907fbf65006bd4276439f5a28f21f420ac12a3dbeeeffc0afef3b85f3","observation_id":"37cb3372-fb09-463a-ad5a-9024b2655608","resolution":{"observed_at":"2026-08-14T11:46:42.447328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.424220Z","title":"Measures of distributional similarity,","venue":null,"work_id":"626f0cc7-43b1-4010-a78e-5c09a6e2c329","year":1999},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.905998Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:e0b865af4cc7685b5dc33ed8478cdc1594fb032dc8419e8e0e75de785b0e5606","observation_id":"26b2db81-4e68-4478-b534-9ee438af037e","resolution":{"observed_at":"2026-08-14T11:46:42.429624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.406594Z","title":"An empirical study of smoothing techniques for language modeling,","venue":null,"work_id":"0eb5f13b-bb6f-4962-9e60-5b4ebe26bf7b","year":1996},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.910905Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:1fb1714442404accd1d59ee7a2385fa48e9d15514b61fed4d0545bc9c479f250","observation_id":"71fa2a58-e427-4766-9148-a3abaab3a18d","resolution":{"observed_at":"2026-08-14T11:46:42.412892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.388815Z","title":"Data-dependent gaussian prior objective for language generation,","venue":null,"work_id":"dd60aef1-a966-411c-9d0a-6400c8b15969","year":2020},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.916063Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:f044398dceb965a300f05b619db5de7e4c6de8dca8a593ca2743ba897734f404","observation_id":"c2c2cf49-176e-4b6f-9384-db67bc4a78e0","resolution":{"observed_at":"2026-08-14T11:46:42.394419Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.369894Z","title":"Controlvae: Controllable variational autoencoder,","venue":null,"work_id":"18743a00-8e66-47b7-92e6-3e0007b218be","year":2020},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.922054Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:584dd952b509ff08daf6c9401cb97caefbaa575b753bca5253d18f36dab0d1a6","observation_id":"bf620d05-eb5c-46a9-bc56-c198d46aa7fd","resolution":{"observed_at":"2026-08-14T11:46:42.375684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.350939Z","title":null,"venue":null,"work_id":"dc7d242d-71d6-4c0e-b24c-5280df2de2bf","year":2006},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.927046Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:59b38281faee63ba58b3978ec9df0986588494f1a8438a65e6cf71be43e5c960","observation_id":"6293d0ae-6f01-4b5a-958c-6f416260c349","resolution":{"observed_at":"2026-08-14T11:46:42.356594Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.331502Z","title":"Generative adversarial nets,","venue":null,"work_id":"da8bac88-c0bc-4872-9bd1-94e49b3f2e0a","year":2014},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.931903Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:c0a4d2cb00f05b737d3bbba8bfbee0d7d234b402f9f4351173aeb543f0017a1f","observation_id":"d082f8a3-55aa-4316-9b46-f2cdb2c67e0e","resolution":{"observed_at":"2026-08-14T11:46:42.337936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.314519Z","title":"Clause restructuring for statistical machine translation,","venue":null,"work_id":"30f2e9a7-7710-4cf6-aa40-867adb1e022f","year":2005},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.937435Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:0b217e6bd528764e4ec012f5d517ec669c616279288704aee51b583686668c2c","observation_id":"1c4bae45-c8de-4e18-987c-34d60b6ab684","resolution":{"observed_at":"2026-08-14T11:46:42.319629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.297233Z","title":"Neural machine translation of rare words with subword units,","venue":null,"work_id":"39ef1873-0f75-41c3-b8c5-3ec30d473b92","year":2016},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.943798Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:3e88ede58cb99d3f012c4090f5c7f191bb38a7083bb9985ed68d1cb1af06fe61","observation_id":"efa9a791-745c-44cb-8300-2033ea05a36a","resolution":{"observed_at":"2026-08-14T11:46:42.302801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.280883Z","title":"Moses: open source toolkit for statistical machine translation,","venue":null,"work_id":"fb3659dc-14ff-411e-9911-c2999d377285","year":2007},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.948934Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:ccc062a4b7441a36c1e55c5c7a00583d75f051b2e016afff2d42a63444d23514","observation_id":"342ab8ed-e9a4-44f6-bd5d-d618a1928ef9","resolution":{"observed_at":"2026-08-14T11:46:42.286045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.262762Z","title":"Minimum error rate training in statistical machine trans- lation,","venue":null,"work_id":"997d5c8a-54d1-477d-ac41-18d04c1f69ee","year":2003},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.955355Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:938f831fde86074761883efcefc75fce5c002a7474e850ef9150884ff569a307","observation_id":"4e13d20b-217d-4a08-ac26-cbb02c9597f4","resolution":{"observed_at":"2026-08-14T11:46:42.269781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.242786Z","title":"Srilm — an extensible language modeling toolkit,","venue":null,"work_id":"58123bce-1ead-4848-b4d0-33d02da299ec","year":2002},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.960343Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:be8b2b141c9305c186f9656345481ce57a2dd2f2b4a261507c0a1ddc8bb03827","observation_id":"b7d62229-7cdc-464d-a0e3-191408cb3230","resolution":{"observed_at":"2026-08-14T11:46:42.248957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.223700Z","title":"On using very large target vocabulary for neural machine translation,","venue":null,"work_id":"88d66042-3d48-4262-8d33-885d20b51c52","year":2015},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.965872Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:c873d18d07e31222acbf2927908e3951b7f2f08f1736b3cb6a925debc43f6d2b","observation_id":"205f0740-b08d-4239-a792-fb041058047a","resolution":{"observed_at":"2026-08-14T11:46:42.230953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.201967Z","title":"Six challenges for neural machine translation,","venue":null,"work_id":"c37f093a-56e0-4df2-a23f-fcb9e733f5a5","year":2017},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.971622Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:6a652321c390d62fcbfa79afb573ba0f74d4f7cb72b7c7c08478b7e6b11f655b","observation_id":"b179dd9e-ba19-45cc-a0a5-2fa7ac7f82d3","resolution":{"observed_at":"2026-08-14T11:46:42.208595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.182744Z","title":"Analyzing uncer- tainty in neural machine translation,","venue":null,"work_id":"4c7fa812-9b72-448e-a125-cf9acc7cdb1e","year":2018},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.976322Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:70d7ff861691e4d96b85ea637a2360e4b93f2258fb63b4bfd01550a0b131b595","observation_id":"9ce4fb83-65ad-4e39-872c-5a5f620488f6","resolution":{"observed_at":"2026-08-14T11:46:42.188557Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.164715Z","title":"Empirical analysis of beam search perfor- mance degradation in neural sequence models,","venue":null,"work_id":"b0913d4b-b9d1-46c9-9a08-4626b747965c","year":2019},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.981146Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:3c866f5e439ff62f64f888a484d9f34d6a61c10ae68964c16653b35e2bfe5e57","observation_id":"611082d5-462c-4012-9066-56c5ef5d6b92","resolution":{"observed_at":"2026-08-14T11:46:42.170313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.145392Z","title":"Classical structured prediction losses for sequence to sequence learning,","venue":null,"work_id":"b93cb1b9-d1c0-43b4-abb0-ca100799104e","year":2018},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.986982Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:d84cace3f1294c60a969d7890a6ba939785ad13f460ed10753e3b30533dff360","observation_id":"1b04eb92-d488-4267-83d7-7a50b07ccefb","resolution":{"observed_at":"2026-08-14T11:46:42.150524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.128782Z","title":"SMART: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization,","venue":null,"work_id":"46052ca2-3bc3-45b5-abea-618367b0b802","year":2020},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.991688Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:93558e28331a0b352ac7d746bc9bf456de73129c27fc1a9db947f0421c7c863a","observation_id":"0cc15aca-1076-480a-ab21-46e01604a991","resolution":{"observed_at":"2026-08-14T11:46:42.134254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.112645Z","title":"Better fine-tuning by reducing representational collapse,","venue":null,"work_id":"a1685cb9-bd76-4bba-98fb-5b3ee3072548","year":null},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:41.996649Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:912a19056fac8f4595686914f4c7f2b5d87f8bbf51d3bc9091aab775695c390e","observation_id":"529e47e1-f053-46cc-bb56-1108ad271807","resolution":{"observed_at":"2026-08-14T11:46:42.117835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.075906Z","title":"Regularizing neural machine translation by target-bidirectional agreement,","venue":null,"work_id":"eb92aec7-f962-4932-90e2-3860674edcc7","year":2019},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:42.007981Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:dd181cbb01723ada505b3cf0f32b8474c3ffbb6f675870d5bd84e5d629c493ba","observation_id":"28d92acd-dee1-4a85-aef0-273e82e0174e","resolution":{"observed_at":"2026-08-14T11:46:42.082855Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:46:42.095663Z","title":"Available: https://openreview.net/forum?id= OQ08SN70M1V","venue":null,"work_id":"41e45540-35e0-4cde-8f29-8f7fa6270677","year":null},"citing_paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-14T11:46:42.002525Z"},"links":{"citing_paper":"/paper/1908.08399"},"observation_digest":"sha256:72e8a069ec193aa63003293e682018e609d9c7fe328f9f0e3f2ac03c3997978c","observation_id":"65a8e5b4-fec2-4ba3-aae0-173e830788d8","resolution":{"observed_at":"2026-08-14T11:46:42.101596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.08399","last_updated":"2021-04-17T06:21:13Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T05:22:33.676016Z","submitted_at":"2019-08-22T14:16:20Z","title":"Controllable Dual Skew Divergence Loss for Neural Machine Translation"},"reference_resolution":{"displayed":37,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":34},"total_outbound_references":37},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:1908.08399."}