{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:6YGHV4NMEZJ5U3NFZY4O5BK3G7","short_pith_number":"pith:6YGHV4NM","canonical_record":{"source":{"id":"1709.10191","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-09-28T22:40:07Z","cross_cats_sorted":[],"title_canon_sha256":"11a4f143d4503ba9d34c3b45087d6f96435b9f5f4f2fe4b47999b235d8c95977","abstract_canon_sha256":"cafc3106b802f4affdcfeaab15ab8ea0cd40974b6571cf77986011c8d523d7fe"},"schema_version":"1.0"},"canonical_sha256":"f60c7af1ac2653da6da5ce38ee855b37c0787853a85778d611caf9fcb3947180","source":{"kind":"arxiv","id":"1709.10191","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.10191","created_at":"2026-05-18T00:34:04Z"},{"alias_kind":"arxiv_version","alias_value":"1709.10191v1","created_at":"2026-05-18T00:34:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.10191","created_at":"2026-05-18T00:34:04Z"},{"alias_kind":"pith_short_12","alias_value":"6YGHV4NMEZJ5","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6YGHV4NMEZJ5U3NF","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6YGHV4NM","created_at":"2026-05-18T12:31:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:6YGHV4NMEZJ5U3NFZY4O5BK3G7","target":"record","payload":{"canonical_record":{"source":{"id":"1709.10191","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-09-28T22:40:07Z","cross_cats_sorted":[],"title_canon_sha256":"11a4f143d4503ba9d34c3b45087d6f96435b9f5f4f2fe4b47999b235d8c95977","abstract_canon_sha256":"cafc3106b802f4affdcfeaab15ab8ea0cd40974b6571cf77986011c8d523d7fe"},"schema_version":"1.0"},"canonical_sha256":"f60c7af1ac2653da6da5ce38ee855b37c0787853a85778d611caf9fcb3947180","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:04.046697Z","signature_b64":"S6fDR9wGnzwDkk4ps1opU70GIgDqWu4CegLrdBwMTPWMV2xyRM8q6BXa5QRATZ2I/QT+jjD9xCumwW62DnJnCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f60c7af1ac2653da6da5ce38ee855b37c0787853a85778d611caf9fcb3947180","last_reissued_at":"2026-05-18T00:34:04.046037Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:04.046037Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.10191","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":"2026-05-18T00:34:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XfgKGXswwKUlMvv6fnLlDoQhjXkYyKBDQP0idH/gPrbkfJXVvEiznBDpJdkqxnJ/d9eX1H+L371lRuYst7yeAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T22:02:39.275932Z"},"content_sha256":"46e73b6571c0fb1fe13747c44abffe336f291fd44fc225e3a5b4d099a1b1bf1d","schema_version":"1.0","event_id":"sha256:46e73b6571c0fb1fe13747c44abffe336f291fd44fc225e3a5b4d099a1b1bf1d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:6YGHV4NMEZJ5U3NFZY4O5BK3G7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bing Xiang, Bowen Zhou, Kai Zhao, Liang Huang, Mingbo Ma","submitted_at":"2017-09-28T22:40:07Z","abstract_excerpt":"Sentence-level classification and sequential labeling are two fundamental tasks in language understanding. While these two tasks are usually modeled separately, in reality, they are often correlated, for example in intent classification and slot filling, or in topic classification and named-entity recognition. In order to utilize the potential benefits from their correlations, we propose a jointly trained model for learning the two tasks simultaneously via Long Short-Term Memory (LSTM) networks. This model predicts the sentence-level category and the word-level label sequence from the stepwise"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.10191","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":"2026-05-18T00:34:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QwAmCa1JUEoN0Q/lBXcKK/lMuJyuAl+4pDNM+vB8ZjnjmLqPIEdC+WmRJvzKgWORMqbAhvZgRwjO1DqmOoamCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T22:02:39.276279Z"},"content_sha256":"e5365daafb7cebfbad2cb92abf4cff99ec5102ae336cc95ecc98ae3bffb0c798","schema_version":"1.0","event_id":"sha256:e5365daafb7cebfbad2cb92abf4cff99ec5102ae336cc95ecc98ae3bffb0c798"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6YGHV4NMEZJ5U3NFZY4O5BK3G7/bundle.json","state_url":"https://pith.science/pith/6YGHV4NMEZJ5U3NFZY4O5BK3G7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6YGHV4NMEZJ5U3NFZY4O5BK3G7/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":"2026-05-31T22:02:39Z","links":{"resolver":"https://pith.science/pith/6YGHV4NMEZJ5U3NFZY4O5BK3G7","bundle":"https://pith.science/pith/6YGHV4NMEZJ5U3NFZY4O5BK3G7/bundle.json","state":"https://pith.science/pith/6YGHV4NMEZJ5U3NFZY4O5BK3G7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6YGHV4NMEZJ5U3NFZY4O5BK3G7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:6YGHV4NMEZJ5U3NFZY4O5BK3G7","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":"cafc3106b802f4affdcfeaab15ab8ea0cd40974b6571cf77986011c8d523d7fe","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-09-28T22:40:07Z","title_canon_sha256":"11a4f143d4503ba9d34c3b45087d6f96435b9f5f4f2fe4b47999b235d8c95977"},"schema_version":"1.0","source":{"id":"1709.10191","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.10191","created_at":"2026-05-18T00:34:04Z"},{"alias_kind":"arxiv_version","alias_value":"1709.10191v1","created_at":"2026-05-18T00:34:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.10191","created_at":"2026-05-18T00:34:04Z"},{"alias_kind":"pith_short_12","alias_value":"6YGHV4NMEZJ5","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6YGHV4NMEZJ5U3NF","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6YGHV4NM","created_at":"2026-05-18T12:31:03Z"}],"graph_snapshots":[{"event_id":"sha256:e5365daafb7cebfbad2cb92abf4cff99ec5102ae336cc95ecc98ae3bffb0c798","target":"graph","created_at":"2026-05-18T00:34:04Z","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":"Sentence-level classification and sequential labeling are two fundamental tasks in language understanding. While these two tasks are usually modeled separately, in reality, they are often correlated, for example in intent classification and slot filling, or in topic classification and named-entity recognition. In order to utilize the potential benefits from their correlations, we propose a jointly trained model for learning the two tasks simultaneously via Long Short-Term Memory (LSTM) networks. This model predicts the sentence-level category and the word-level label sequence from the stepwise","authors_text":"Bing Xiang, Bowen Zhou, Kai Zhao, Liang Huang, Mingbo Ma","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-09-28T22:40:07Z","title":"Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.10191","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":"sha256:46e73b6571c0fb1fe13747c44abffe336f291fd44fc225e3a5b4d099a1b1bf1d","target":"record","created_at":"2026-05-18T00:34:04Z","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":"cafc3106b802f4affdcfeaab15ab8ea0cd40974b6571cf77986011c8d523d7fe","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-09-28T22:40:07Z","title_canon_sha256":"11a4f143d4503ba9d34c3b45087d6f96435b9f5f4f2fe4b47999b235d8c95977"},"schema_version":"1.0","source":{"id":"1709.10191","kind":"arxiv","version":1}},"canonical_sha256":"f60c7af1ac2653da6da5ce38ee855b37c0787853a85778d611caf9fcb3947180","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f60c7af1ac2653da6da5ce38ee855b37c0787853a85778d611caf9fcb3947180","first_computed_at":"2026-05-18T00:34:04.046037Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:34:04.046037Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"S6fDR9wGnzwDkk4ps1opU70GIgDqWu4CegLrdBwMTPWMV2xyRM8q6BXa5QRATZ2I/QT+jjD9xCumwW62DnJnCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:34:04.046697Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.10191","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:46e73b6571c0fb1fe13747c44abffe336f291fd44fc225e3a5b4d099a1b1bf1d","sha256:e5365daafb7cebfbad2cb92abf4cff99ec5102ae336cc95ecc98ae3bffb0c798"],"state_sha256":"b69a9d5e6f8063d2da2eb2a3a22bd08fb26640a496673471b70d3ff1e13ea0c1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tuw3N+0Q+RdbObMSwfj1/iIQdrP37wNd7jqNAmLWUuvbMdQRhTmCo6wEL0iBatGApUxcFgS7+VYfqgMkx3WVDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T22:02:39.278548Z","bundle_sha256":"fb452a161abed8a1cd22dd059b0d0f944fb2dbce7c4e2682961da5da2fbe13fa"}}