{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:HDIV5SFIZSKXRS7RLOLDZAYYJ4","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":"3a8e8e84f4afa008b1233208c13b8c82dd7844d1c09b9e70b3ef33c4f245c63f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-09-14T16:06:45Z","title_canon_sha256":"1c2943ec2ac2c3b43a483e2f772f46915638f5425d5eb579d1d4a6dc919b3037"},"schema_version":"1.0","source":{"id":"1709.04849","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.04849","created_at":"2026-05-18T00:04:32Z"},{"alias_kind":"arxiv_version","alias_value":"1709.04849v5","created_at":"2026-05-18T00:04:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.04849","created_at":"2026-05-18T00:04:32Z"},{"alias_kind":"pith_short_12","alias_value":"HDIV5SFIZSKX","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"HDIV5SFIZSKXRS7R","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"HDIV5SFI","created_at":"2026-05-18T12:31:18Z"}],"graph_snapshots":[{"event_id":"sha256:462017845f8fa0a76d3590f3996e59b887401e617aeb87f8996611afd12304ba","target":"graph","created_at":"2026-05-18T00:04:32Z","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":"Neural sequence-to-sequence networks with attention have achieved remarkable performance for machine translation. One of the reasons for their effectiveness is their ability to capture relevant source-side contextual information at each time-step prediction through an attention mechanism. However, the target-side context is solely based on the sequence model which, in practice, is prone to a recency bias and lacks the ability to capture effectively non-sequential dependencies among words. To address this limitation, we propose a target-side-attentive residual recurrent network for decoding, wh","authors_text":"Andrei Popescu-Belis, Dhananjay Ram, Lesly Miculicich Werlen, Nikolaos Pappas","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-09-14T16:06:45Z","title":"Self-Attentive Residual Decoder for Neural Machine Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.04849","kind":"arxiv","version":5},"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":"sha256:5fc223c7587bc1f6f78294eb42d7e8734d1bc0861b81aebc2b94321a90d909e3","target":"record","created_at":"2026-05-18T00:04:32Z","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":"3a8e8e84f4afa008b1233208c13b8c82dd7844d1c09b9e70b3ef33c4f245c63f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-09-14T16:06:45Z","title_canon_sha256":"1c2943ec2ac2c3b43a483e2f772f46915638f5425d5eb579d1d4a6dc919b3037"},"schema_version":"1.0","source":{"id":"1709.04849","kind":"arxiv","version":5}},"canonical_sha256":"38d15ec8a8cc9578cbf15b963c83184f23d2da3da145ad5cf5b86d413128de42","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"38d15ec8a8cc9578cbf15b963c83184f23d2da3da145ad5cf5b86d413128de42","first_computed_at":"2026-05-18T00:04:32.393073Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:04:32.393073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zethRT83++1/ubqP4fNAIgSBsT1WHBuQIlXgXo2cavMywBKnMUbRux7KEDZvjhT4N3qtVJxU6A9JaVqj7yxYBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:04:32.393738Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.04849","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5fc223c7587bc1f6f78294eb42d7e8734d1bc0861b81aebc2b94321a90d909e3","sha256:462017845f8fa0a76d3590f3996e59b887401e617aeb87f8996611afd12304ba"],"state_sha256":"09b6b3dae9e5b739e22a32e417cedfff86f3077a4fb86d1a00324ba116be1065"}