{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:AKBI3U6HHRT66EBYLU36B3T3TR","short_pith_number":"pith:AKBI3U6H","schema_version":"1.0","canonical_sha256":"02828dd3c73c67ef10385d37e0ee7b9c70021422631ebfcaa3847b0d2ced9e53","source":{"kind":"arxiv","id":"1904.08524","version":1},"attestation_state":"computed","paper":{"title":"Towards Open Intent Discovery for Conversational Text","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Nedim Lipka, Nikhita Vedula, Pranav Maneriker, Srinivasan Parthasarathy","submitted_at":"2019-04-17T22:40:01Z","abstract_excerpt":"Detecting and identifying user intent from text, both written and spoken, plays an important role in modelling and understand dialogs. Existing research for intent discovery model it as a classification task with a predefined set of known categories. To generailze beyond these preexisting classes, we define a new task of \\textit{open intent discovery}. We investigate how intent can be generalized to those not seen during training. To this end, we propose a two-stage approach to this task - predicting whether an utterance contains an intent, and then tagging the intent in the input utterance. O"},"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":"1904.08524","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-04-17T22:40:01Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"751e25453d3bd1b040c32f372291658b6b76642363a2862c745767037a41c884","abstract_canon_sha256":"c40f5f37dfb9d41a96aa344417f1daf4b5ad9d217ef727574ac7827470800adc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:13.929156Z","signature_b64":"jOKLy1DIGGSBe7OtTsi2/bmD1jEz/rPLfUBCpGJ0vjb7NP7wVNSXSrT9mNG6vM+/1eWMVPiPWydu4FCBnb1ZAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02828dd3c73c67ef10385d37e0ee7b9c70021422631ebfcaa3847b0d2ced9e53","last_reissued_at":"2026-05-17T23:48:13.928518Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:13.928518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Open Intent Discovery for Conversational Text","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Nedim Lipka, Nikhita Vedula, Pranav Maneriker, Srinivasan Parthasarathy","submitted_at":"2019-04-17T22:40:01Z","abstract_excerpt":"Detecting and identifying user intent from text, both written and spoken, plays an important role in modelling and understand dialogs. Existing research for intent discovery model it as a classification task with a predefined set of known categories. To generailze beyond these preexisting classes, we define a new task of \\textit{open intent discovery}. We investigate how intent can be generalized to those not seen during training. To this end, we propose a two-stage approach to this task - predicting whether an utterance contains an intent, and then tagging the intent in the input utterance. O"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.08524","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1904.08524","created_at":"2026-05-17T23:48:13.928626+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.08524v1","created_at":"2026-05-17T23:48:13.928626+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.08524","created_at":"2026-05-17T23:48:13.928626+00:00"},{"alias_kind":"pith_short_12","alias_value":"AKBI3U6HHRT6","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"AKBI3U6HHRT66EBY","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"AKBI3U6H","created_at":"2026-05-18T12:33:12.712433+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.12645","citing_title":"Training LLMs with Reinforcement Learning for Intent-Aware Personalized Question Answering","ref_index":83,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AKBI3U6HHRT66EBYLU36B3T3TR","json":"https://pith.science/pith/AKBI3U6HHRT66EBYLU36B3T3TR.json","graph_json":"https://pith.science/api/pith-number/AKBI3U6HHRT66EBYLU36B3T3TR/graph.json","events_json":"https://pith.science/api/pith-number/AKBI3U6HHRT66EBYLU36B3T3TR/events.json","paper":"https://pith.science/paper/AKBI3U6H"},"agent_actions":{"view_html":"https://pith.science/pith/AKBI3U6HHRT66EBYLU36B3T3TR","download_json":"https://pith.science/pith/AKBI3U6HHRT66EBYLU36B3T3TR.json","view_paper":"https://pith.science/paper/AKBI3U6H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.08524&json=true","fetch_graph":"https://pith.science/api/pith-number/AKBI3U6HHRT66EBYLU36B3T3TR/graph.json","fetch_events":"https://pith.science/api/pith-number/AKBI3U6HHRT66EBYLU36B3T3TR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AKBI3U6HHRT66EBYLU36B3T3TR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AKBI3U6HHRT66EBYLU36B3T3TR/action/storage_attestation","attest_author":"https://pith.science/pith/AKBI3U6HHRT66EBYLU36B3T3TR/action/author_attestation","sign_citation":"https://pith.science/pith/AKBI3U6HHRT66EBYLU36B3T3TR/action/citation_signature","submit_replication":"https://pith.science/pith/AKBI3U6HHRT66EBYLU36B3T3TR/action/replication_record"}},"created_at":"2026-05-17T23:48:13.928626+00:00","updated_at":"2026-05-17T23:48:13.928626+00:00"}