{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PORTAYFNBJI4ZSX2JE4M6VD76K","short_pith_number":"pith:PORTAYFN","schema_version":"1.0","canonical_sha256":"7ba33060ad0a51cccafa4938cf547ff295b758499a485f90782e6d2d73352fca","source":{"kind":"arxiv","id":"1805.02356","version":1},"attestation_state":"computed","paper":{"title":"Multimodal Machine Translation with Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.MA","cs.MM"],"primary_cat":"cs.CL","authors_text":"Jieli Zhou, Xin Qian, Ziyi Zhong","submitted_at":"2018-05-07T06:12:32Z","abstract_excerpt":"Multimodal machine translation is one of the applications that integrates computer vision and language processing. It is a unique task given that in the field of machine translation, many state-of-the-arts algorithms still only employ textual information. In this work, we explore the effectiveness of reinforcement learning in multimodal machine translation. We present a novel algorithm based on the Advantage Actor-Critic (A2C) algorithm that specifically cater to the multimodal machine translation task of the EMNLP 2018 Third Conference on Machine Translation (WMT18). We experiment our propose"},"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":"1805.02356","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-07T06:12:32Z","cross_cats_sorted":["cs.AI","cs.IR","cs.MA","cs.MM"],"title_canon_sha256":"2582a16d580f8893b7391ea26cdd50c705909dfb59593968bdb4ba55156aa184","abstract_canon_sha256":"9dc3fac877009608fa974b7fef48381f8dae8b26beb41f7d65edc972394c2602"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:40.129418Z","signature_b64":"2/TqbsnCE+BfAEtW3sI2q2IFLXnkT8kOa1JzOvXr2+7dCBHivffwyPDo0RXdmCJ2PpenxQ2slGZLtDwj3rsFDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7ba33060ad0a51cccafa4938cf547ff295b758499a485f90782e6d2d73352fca","last_reissued_at":"2026-05-18T00:16:40.128739Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:40.128739Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multimodal Machine Translation with Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.MA","cs.MM"],"primary_cat":"cs.CL","authors_text":"Jieli Zhou, Xin Qian, Ziyi Zhong","submitted_at":"2018-05-07T06:12:32Z","abstract_excerpt":"Multimodal machine translation is one of the applications that integrates computer vision and language processing. It is a unique task given that in the field of machine translation, many state-of-the-arts algorithms still only employ textual information. In this work, we explore the effectiveness of reinforcement learning in multimodal machine translation. We present a novel algorithm based on the Advantage Actor-Critic (A2C) algorithm that specifically cater to the multimodal machine translation task of the EMNLP 2018 Third Conference on Machine Translation (WMT18). We experiment our propose"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.02356","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":"1805.02356","created_at":"2026-05-18T00:16:40.128855+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.02356v1","created_at":"2026-05-18T00:16:40.128855+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.02356","created_at":"2026-05-18T00:16:40.128855+00:00"},{"alias_kind":"pith_short_12","alias_value":"PORTAYFNBJI4","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"PORTAYFNBJI4ZSX2","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"PORTAYFN","created_at":"2026-05-18T12:32:46.962924+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PORTAYFNBJI4ZSX2JE4M6VD76K","json":"https://pith.science/pith/PORTAYFNBJI4ZSX2JE4M6VD76K.json","graph_json":"https://pith.science/api/pith-number/PORTAYFNBJI4ZSX2JE4M6VD76K/graph.json","events_json":"https://pith.science/api/pith-number/PORTAYFNBJI4ZSX2JE4M6VD76K/events.json","paper":"https://pith.science/paper/PORTAYFN"},"agent_actions":{"view_html":"https://pith.science/pith/PORTAYFNBJI4ZSX2JE4M6VD76K","download_json":"https://pith.science/pith/PORTAYFNBJI4ZSX2JE4M6VD76K.json","view_paper":"https://pith.science/paper/PORTAYFN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.02356&json=true","fetch_graph":"https://pith.science/api/pith-number/PORTAYFNBJI4ZSX2JE4M6VD76K/graph.json","fetch_events":"https://pith.science/api/pith-number/PORTAYFNBJI4ZSX2JE4M6VD76K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PORTAYFNBJI4ZSX2JE4M6VD76K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PORTAYFNBJI4ZSX2JE4M6VD76K/action/storage_attestation","attest_author":"https://pith.science/pith/PORTAYFNBJI4ZSX2JE4M6VD76K/action/author_attestation","sign_citation":"https://pith.science/pith/PORTAYFNBJI4ZSX2JE4M6VD76K/action/citation_signature","submit_replication":"https://pith.science/pith/PORTAYFNBJI4ZSX2JE4M6VD76K/action/replication_record"}},"created_at":"2026-05-18T00:16:40.128855+00:00","updated_at":"2026-05-18T00:16:40.128855+00:00"}