{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:QMBPTN72MWTB7K7NIGWKJF27EV","short_pith_number":"pith:QMBPTN72","schema_version":"1.0","canonical_sha256":"8302f9b7fa65a61fabed41aca4975f2561a2337f8899ceb2b6ea4d4c6375a857","source":{"kind":"arxiv","id":"2110.08130","version":2},"attestation_state":"computed","paper":{"title":"Breaking Down Multilingual Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Graham Neubig, Ting-Rui Chiang, Yi-Pei Chen, Yi-Ting Yeh","submitted_at":"2021-10-15T14:57:12Z","abstract_excerpt":"While multilingual training is now an essential ingredient in machine translation (MT) systems, recent work has demonstrated that it has different effects in different multilingual settings, such as many-to-one, one-to-many, and many-to-many learning. These training settings expose the encoder and the decoder in a machine translation model with different data distributions. In this paper, we examine how different varieties of multilingual training contribute to learning these two components of the MT model. Specifically, we compare bilingual models with encoders and/or decoders initialized by "},"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":"2110.08130","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-10-15T14:57:12Z","cross_cats_sorted":[],"title_canon_sha256":"792df776354077f0148f4f584e7a0fbc0ac6ae949e43dd0da838e8fd5589e984","abstract_canon_sha256":"cd7bc19f05230bc79394276cbf60e7587e6240525945994286c0e5147f5d7ae6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:11:27.717409Z","signature_b64":"8eGVr88k3ko3yv4w1NXTuJc++fvzPPCD4Kr+qPW59udumxmZti82jjIhrCcrVDHoujNDnS4UmtEOpSm67hDyCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8302f9b7fa65a61fabed41aca4975f2561a2337f8899ceb2b6ea4d4c6375a857","last_reissued_at":"2026-07-05T04:11:27.716935Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:11:27.716935Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Breaking Down Multilingual Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Graham Neubig, Ting-Rui Chiang, Yi-Pei Chen, Yi-Ting Yeh","submitted_at":"2021-10-15T14:57:12Z","abstract_excerpt":"While multilingual training is now an essential ingredient in machine translation (MT) systems, recent work has demonstrated that it has different effects in different multilingual settings, such as many-to-one, one-to-many, and many-to-many learning. These training settings expose the encoder and the decoder in a machine translation model with different data distributions. In this paper, we examine how different varieties of multilingual training contribute to learning these two components of the MT model. Specifically, we compare bilingual models with encoders and/or decoders initialized by "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.08130","kind":"arxiv","version":2},"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/2110.08130/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":"2110.08130","created_at":"2026-07-05T04:11:27.716995+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.08130v2","created_at":"2026-07-05T04:11:27.716995+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.08130","created_at":"2026-07-05T04:11:27.716995+00:00"},{"alias_kind":"pith_short_12","alias_value":"QMBPTN72MWTB","created_at":"2026-07-05T04:11:27.716995+00:00"},{"alias_kind":"pith_short_16","alias_value":"QMBPTN72MWTB7K7N","created_at":"2026-07-05T04:11:27.716995+00:00"},{"alias_kind":"pith_short_8","alias_value":"QMBPTN72","created_at":"2026-07-05T04:11:27.716995+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.04464","citing_title":"Leveraging Reward Models for Guiding Code Review Comment Generation","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QMBPTN72MWTB7K7NIGWKJF27EV","json":"https://pith.science/pith/QMBPTN72MWTB7K7NIGWKJF27EV.json","graph_json":"https://pith.science/api/pith-number/QMBPTN72MWTB7K7NIGWKJF27EV/graph.json","events_json":"https://pith.science/api/pith-number/QMBPTN72MWTB7K7NIGWKJF27EV/events.json","paper":"https://pith.science/paper/QMBPTN72"},"agent_actions":{"view_html":"https://pith.science/pith/QMBPTN72MWTB7K7NIGWKJF27EV","download_json":"https://pith.science/pith/QMBPTN72MWTB7K7NIGWKJF27EV.json","view_paper":"https://pith.science/paper/QMBPTN72","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.08130&json=true","fetch_graph":"https://pith.science/api/pith-number/QMBPTN72MWTB7K7NIGWKJF27EV/graph.json","fetch_events":"https://pith.science/api/pith-number/QMBPTN72MWTB7K7NIGWKJF27EV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QMBPTN72MWTB7K7NIGWKJF27EV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QMBPTN72MWTB7K7NIGWKJF27EV/action/storage_attestation","attest_author":"https://pith.science/pith/QMBPTN72MWTB7K7NIGWKJF27EV/action/author_attestation","sign_citation":"https://pith.science/pith/QMBPTN72MWTB7K7NIGWKJF27EV/action/citation_signature","submit_replication":"https://pith.science/pith/QMBPTN72MWTB7K7NIGWKJF27EV/action/replication_record"}},"created_at":"2026-07-05T04:11:27.716995+00:00","updated_at":"2026-07-05T04:11:27.716995+00:00"}