{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:3D4QO7EN7AF5WPBLAGVZULEPU5","short_pith_number":"pith:3D4QO7EN","canonical_record":{"source":{"id":"1708.01809","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-05T19:06:00Z","cross_cats_sorted":[],"title_canon_sha256":"c121f967bd441cdd44be935f3f98d7953bcda4109ae64fbe90b6b1ae16832cd8","abstract_canon_sha256":"2b1035803f0a15d6df8a5b1ccda20f89afe43494a2f56b81884ed1ac6c287bff"},"schema_version":"1.0"},"canonical_sha256":"d8f9077c8df80bdb3c2b01ab9a2c8fa765aef03da2591c1a817fd7fd841232c2","source":{"kind":"arxiv","id":"1708.01809","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.01809","created_at":"2026-05-18T00:38:33Z"},{"alias_kind":"arxiv_version","alias_value":"1708.01809v1","created_at":"2026-05-18T00:38:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.01809","created_at":"2026-05-18T00:38:33Z"},{"alias_kind":"pith_short_12","alias_value":"3D4QO7EN7AF5","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"3D4QO7EN7AF5WPBL","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"3D4QO7EN","created_at":"2026-05-18T12:30:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:3D4QO7EN7AF5WPBLAGVZULEPU5","target":"record","payload":{"canonical_record":{"source":{"id":"1708.01809","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-05T19:06:00Z","cross_cats_sorted":[],"title_canon_sha256":"c121f967bd441cdd44be935f3f98d7953bcda4109ae64fbe90b6b1ae16832cd8","abstract_canon_sha256":"2b1035803f0a15d6df8a5b1ccda20f89afe43494a2f56b81884ed1ac6c287bff"},"schema_version":"1.0"},"canonical_sha256":"d8f9077c8df80bdb3c2b01ab9a2c8fa765aef03da2591c1a817fd7fd841232c2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:33.096784Z","signature_b64":"uId02w7TqPzHBBpLggPgHeaGomqv12kDOSNKxvgKnx0JVoVRe9RT0uewUVB8+fWhcWYXM4PGKMWluq8fXs4vDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8f9077c8df80bdb3c2b01ab9a2c8fa765aef03da2591c1a817fd7fd841232c2","last_reissued_at":"2026-05-18T00:38:33.096269Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:33.096269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.01809","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:38:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VV5bTJCDcOsZLf5JwHUSmiIRvcKLos5pSB33f6iMsF64DsjsU8UWRN9o4L16YQhhO7tiKGc5myAUvHKccBP2AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T23:02:37.344866Z"},"content_sha256":"35d79a7f1bb25d456a47a532763dc92dc7f3423cefa1e222ac84cdf16b64dc96","schema_version":"1.0","event_id":"sha256:35d79a7f1bb25d456a47a532763dc92dc7f3423cefa1e222ac84cdf16b64dc96"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:3D4QO7EN7AF5WPBLAGVZULEPU5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Comparison of Neural Models for Word Ordering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Adri`a de Gispert, Bill Byrne, Eva Hasler, Felix Stahlberg, Marcus Tomalin","submitted_at":"2017-08-05T19:06:00Z","abstract_excerpt":"We compare several language models for the word-ordering task and propose a new bag-to-sequence neural model based on attention-based sequence-to-sequence models. We evaluate the model on a large German WMT data set where it significantly outperforms existing models. We also describe a novel search strategy for LM-based word ordering and report results on the English Penn Treebank. Our best model setup outperforms prior work both in terms of speed and quality."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.01809","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:38:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NbfxoKSIWXIkhjuQiR5L4obXuP8/a8h3phe7uUkmTOPmLZ1w3+jUVEXVPzcQdlpIR7ZJWyNgaQYyKIlYG7CoBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T23:02:37.345528Z"},"content_sha256":"ae649713b1e75aa0f2c2f8604016605a4b98b2c5c75fea4af23e3616822ee753","schema_version":"1.0","event_id":"sha256:ae649713b1e75aa0f2c2f8604016605a4b98b2c5c75fea4af23e3616822ee753"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3D4QO7EN7AF5WPBLAGVZULEPU5/bundle.json","state_url":"https://pith.science/pith/3D4QO7EN7AF5WPBLAGVZULEPU5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3D4QO7EN7AF5WPBLAGVZULEPU5/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T23:02:37Z","links":{"resolver":"https://pith.science/pith/3D4QO7EN7AF5WPBLAGVZULEPU5","bundle":"https://pith.science/pith/3D4QO7EN7AF5WPBLAGVZULEPU5/bundle.json","state":"https://pith.science/pith/3D4QO7EN7AF5WPBLAGVZULEPU5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3D4QO7EN7AF5WPBLAGVZULEPU5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:3D4QO7EN7AF5WPBLAGVZULEPU5","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2b1035803f0a15d6df8a5b1ccda20f89afe43494a2f56b81884ed1ac6c287bff","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-05T19:06:00Z","title_canon_sha256":"c121f967bd441cdd44be935f3f98d7953bcda4109ae64fbe90b6b1ae16832cd8"},"schema_version":"1.0","source":{"id":"1708.01809","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.01809","created_at":"2026-05-18T00:38:33Z"},{"alias_kind":"arxiv_version","alias_value":"1708.01809v1","created_at":"2026-05-18T00:38:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.01809","created_at":"2026-05-18T00:38:33Z"},{"alias_kind":"pith_short_12","alias_value":"3D4QO7EN7AF5","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"3D4QO7EN7AF5WPBL","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"3D4QO7EN","created_at":"2026-05-18T12:30:58Z"}],"graph_snapshots":[{"event_id":"sha256:ae649713b1e75aa0f2c2f8604016605a4b98b2c5c75fea4af23e3616822ee753","target":"graph","created_at":"2026-05-18T00:38:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We compare several language models for the word-ordering task and propose a new bag-to-sequence neural model based on attention-based sequence-to-sequence models. We evaluate the model on a large German WMT data set where it significantly outperforms existing models. We also describe a novel search strategy for LM-based word ordering and report results on the English Penn Treebank. Our best model setup outperforms prior work both in terms of speed and quality.","authors_text":"Adri`a de Gispert, Bill Byrne, Eva Hasler, Felix Stahlberg, Marcus Tomalin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-05T19:06:00Z","title":"A Comparison of Neural Models for Word Ordering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.01809","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:35d79a7f1bb25d456a47a532763dc92dc7f3423cefa1e222ac84cdf16b64dc96","target":"record","created_at":"2026-05-18T00:38:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2b1035803f0a15d6df8a5b1ccda20f89afe43494a2f56b81884ed1ac6c287bff","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-05T19:06:00Z","title_canon_sha256":"c121f967bd441cdd44be935f3f98d7953bcda4109ae64fbe90b6b1ae16832cd8"},"schema_version":"1.0","source":{"id":"1708.01809","kind":"arxiv","version":1}},"canonical_sha256":"d8f9077c8df80bdb3c2b01ab9a2c8fa765aef03da2591c1a817fd7fd841232c2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d8f9077c8df80bdb3c2b01ab9a2c8fa765aef03da2591c1a817fd7fd841232c2","first_computed_at":"2026-05-18T00:38:33.096269Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:38:33.096269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uId02w7TqPzHBBpLggPgHeaGomqv12kDOSNKxvgKnx0JVoVRe9RT0uewUVB8+fWhcWYXM4PGKMWluq8fXs4vDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:38:33.096784Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.01809","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:35d79a7f1bb25d456a47a532763dc92dc7f3423cefa1e222ac84cdf16b64dc96","sha256:ae649713b1e75aa0f2c2f8604016605a4b98b2c5c75fea4af23e3616822ee753"],"state_sha256":"ba278c07f8f824c519b2c8b5accf28593707a8a5767a54e0553c30c4797adcd6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"goZRVZ+Ni4bFrGfCeU4D3+Ka3uWj53xiwFDlTOYGFkRJkwdrUYuPUMHo6IduaISACE3vCxO/fD+nyPG+q9fVDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T23:02:37.348606Z","bundle_sha256":"ac7e5dcd92ded6d7bc377dd36b196f99d6da6a8db6f5ef6998ec753b4c970e0c"}}