{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:ZQKKWZ7XXJPDZ35BNONOIUWDEN","short_pith_number":"pith:ZQKKWZ7X","schema_version":"1.0","canonical_sha256":"cc14ab67f7ba5e3cefa16b9ae452c323615f308e6baf1f914776a042ddc5107d","source":{"kind":"arxiv","id":"2004.01909","version":1},"attestation_state":"computed","paper":{"title":"Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Chuan-Ju Wang, Jheng-Hong Yang, Jimmy Lin, Ming-Feng Tsai, Rodrigo Nogueira, Sheng-Chieh Lin","submitted_at":"2020-04-04T11:07:54Z","abstract_excerpt":"This paper presents an empirical study of conversational question reformulation (CQR) with sequence-to-sequence architectures and pretrained language models (PLMs). We leverage PLMs to address the strong token-to-token independence assumption made in the common objective, maximum likelihood estimation, for the CQR task. In CQR benchmarks of task-oriented dialogue systems, we evaluate fine-tuned PLMs on the recently-introduced CANARD dataset as an in-domain task and validate the models using data from the TREC 2019 CAsT Track as an out-domain task. Examining a variety of architectures with diff"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2004.01909","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-04-04T11:07:54Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"f9ce5a4d33f897a73c24293c28c9691ae8654db228e4b3872ccd078a85e70a21","abstract_canon_sha256":"7553b70a1fd82505e8d1ce72744c6d30606ca7af1ca50b88ae90d2ce05da6a0e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:52:39.446285Z","signature_b64":"Ua9+7zitiaYvJeqkSvpmwWkQlPbkd9nF3LkLek9PYULoAX2FqdZYj3Lw4o7QmmFs5OgosHdK3ufkP4SNQleDAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc14ab67f7ba5e3cefa16b9ae452c323615f308e6baf1f914776a042ddc5107d","last_reissued_at":"2026-07-05T00:52:39.445837Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:52:39.445837Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Chuan-Ju Wang, Jheng-Hong Yang, Jimmy Lin, Ming-Feng Tsai, Rodrigo Nogueira, Sheng-Chieh Lin","submitted_at":"2020-04-04T11:07:54Z","abstract_excerpt":"This paper presents an empirical study of conversational question reformulation (CQR) with sequence-to-sequence architectures and pretrained language models (PLMs). We leverage PLMs to address the strong token-to-token independence assumption made in the common objective, maximum likelihood estimation, for the CQR task. In CQR benchmarks of task-oriented dialogue systems, we evaluate fine-tuned PLMs on the recently-introduced CANARD dataset as an in-domain task and validate the models using data from the TREC 2019 CAsT Track as an out-domain task. Examining a variety of architectures with diff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.01909","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2004.01909/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2004.01909","created_at":"2026-07-05T00:52:39.445890+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.01909v1","created_at":"2026-07-05T00:52:39.445890+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.01909","created_at":"2026-07-05T00:52:39.445890+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZQKKWZ7XXJPD","created_at":"2026-07-05T00:52:39.445890+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZQKKWZ7XXJPDZ35B","created_at":"2026-07-05T00:52:39.445890+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZQKKWZ7X","created_at":"2026-07-05T00:52:39.445890+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.06771","citing_title":"Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZQKKWZ7XXJPDZ35BNONOIUWDEN","json":"https://pith.science/pith/ZQKKWZ7XXJPDZ35BNONOIUWDEN.json","graph_json":"https://pith.science/api/pith-number/ZQKKWZ7XXJPDZ35BNONOIUWDEN/graph.json","events_json":"https://pith.science/api/pith-number/ZQKKWZ7XXJPDZ35BNONOIUWDEN/events.json","paper":"https://pith.science/paper/ZQKKWZ7X"},"agent_actions":{"view_html":"https://pith.science/pith/ZQKKWZ7XXJPDZ35BNONOIUWDEN","download_json":"https://pith.science/pith/ZQKKWZ7XXJPDZ35BNONOIUWDEN.json","view_paper":"https://pith.science/paper/ZQKKWZ7X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.01909&json=true","fetch_graph":"https://pith.science/api/pith-number/ZQKKWZ7XXJPDZ35BNONOIUWDEN/graph.json","fetch_events":"https://pith.science/api/pith-number/ZQKKWZ7XXJPDZ35BNONOIUWDEN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZQKKWZ7XXJPDZ35BNONOIUWDEN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZQKKWZ7XXJPDZ35BNONOIUWDEN/action/storage_attestation","attest_author":"https://pith.science/pith/ZQKKWZ7XXJPDZ35BNONOIUWDEN/action/author_attestation","sign_citation":"https://pith.science/pith/ZQKKWZ7XXJPDZ35BNONOIUWDEN/action/citation_signature","submit_replication":"https://pith.science/pith/ZQKKWZ7XXJPDZ35BNONOIUWDEN/action/replication_record"}},"created_at":"2026-07-05T00:52:39.445890+00:00","updated_at":"2026-07-05T00:52:39.445890+00:00"}