{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:VXXMYNNQQVFY7UNVO34EHUEMQC","short_pith_number":"pith:VXXMYNNQ","canonical_record":{"source":{"id":"1708.06510","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-22T06:48:27Z","cross_cats_sorted":[],"title_canon_sha256":"1e6c1c4342a5acfcb9212d7ef5ec3ae1218a8d946ea508f2d4b6300cd280ff30","abstract_canon_sha256":"2540055a4373128eb058951c3c238e2613be12dcd2bdf06d6a2cb9a6e759c125"},"schema_version":"1.0"},"canonical_sha256":"adeecc35b0854b8fd1b576f843d08c80984a2ebb547685c5aca1f22ebacd35ae","source":{"kind":"arxiv","id":"1708.06510","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.06510","created_at":"2026-05-18T00:19:59Z"},{"alias_kind":"arxiv_version","alias_value":"1708.06510v2","created_at":"2026-05-18T00:19:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.06510","created_at":"2026-05-18T00:19:59Z"},{"alias_kind":"pith_short_12","alias_value":"VXXMYNNQQVFY","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VXXMYNNQQVFY7UNV","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VXXMYNNQ","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:VXXMYNNQQVFY7UNVO34EHUEMQC","target":"record","payload":{"canonical_record":{"source":{"id":"1708.06510","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-22T06:48:27Z","cross_cats_sorted":[],"title_canon_sha256":"1e6c1c4342a5acfcb9212d7ef5ec3ae1218a8d946ea508f2d4b6300cd280ff30","abstract_canon_sha256":"2540055a4373128eb058951c3c238e2613be12dcd2bdf06d6a2cb9a6e759c125"},"schema_version":"1.0"},"canonical_sha256":"adeecc35b0854b8fd1b576f843d08c80984a2ebb547685c5aca1f22ebacd35ae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:59.002899Z","signature_b64":"epNNh+CJt9eLY5ZdwKra+SnzFYSMTAKvntkvAiF5xRGBZvxiRwCsW00BWZfXYW7IYwLuKVRfSQ/qmB6nWtkfBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"adeecc35b0854b8fd1b576f843d08c80984a2ebb547685c5aca1f22ebacd35ae","last_reissued_at":"2026-05-18T00:19:59.002353Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:59.002353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.06510","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:19:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aZPHlBtBJLBlOGe2aMOy8cuga7OctMMeMKN7fqo0LrPqTELTofdzLz7JXFPAVh+XfQ9lxIv/NOQrbMXb2/h5CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T19:28:46.394709Z"},"content_sha256":"bf525e5ba0691efbf55957bed28e725b4fede12bc83179672cb31b3d63d35c1c","schema_version":"1.0","event_id":"sha256:bf525e5ba0691efbf55957bed28e725b4fede12bc83179672cb31b3d63d35c1c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:VXXMYNNQQVFY7UNVO34EHUEMQC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Handling Homographs in Neural Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Frederick Liu, Graham Neubig, Han Lu","submitted_at":"2017-08-22T06:48:27Z","abstract_excerpt":"Homographs, words with different meanings but the same surface form, have long caused difficulty for machine translation systems, as it is difficult to select the correct translation based on the context. However, with the advent of neural machine translation (NMT) systems, which can theoretically take into account global sentential context, one may hypothesize that this problem has been alleviated. In this paper, we first provide empirical evidence that existing NMT systems in fact still have significant problems in properly translating ambiguous words. We then proceed to describe methods, in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.06510","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:19:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WWkoDgkCJfDUHVmSbBgH4dKNqBN7uyv/PjbFvEw08hbC4eDt4H+NdBPWgojjW7TFfCr9ZeTRkHA0cTojTerHBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T19:28:46.395118Z"},"content_sha256":"77cd194f49b39cfe4e659d0a80e8e5a6d8566665bb73ce00cb99f31bb3d5fc44","schema_version":"1.0","event_id":"sha256:77cd194f49b39cfe4e659d0a80e8e5a6d8566665bb73ce00cb99f31bb3d5fc44"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VXXMYNNQQVFY7UNVO34EHUEMQC/bundle.json","state_url":"https://pith.science/pith/VXXMYNNQQVFY7UNVO34EHUEMQC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VXXMYNNQQVFY7UNVO34EHUEMQC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-05T19:28:46Z","links":{"resolver":"https://pith.science/pith/VXXMYNNQQVFY7UNVO34EHUEMQC","bundle":"https://pith.science/pith/VXXMYNNQQVFY7UNVO34EHUEMQC/bundle.json","state":"https://pith.science/pith/VXXMYNNQQVFY7UNVO34EHUEMQC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VXXMYNNQQVFY7UNVO34EHUEMQC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:VXXMYNNQQVFY7UNVO34EHUEMQC","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":"2540055a4373128eb058951c3c238e2613be12dcd2bdf06d6a2cb9a6e759c125","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-22T06:48:27Z","title_canon_sha256":"1e6c1c4342a5acfcb9212d7ef5ec3ae1218a8d946ea508f2d4b6300cd280ff30"},"schema_version":"1.0","source":{"id":"1708.06510","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.06510","created_at":"2026-05-18T00:19:59Z"},{"alias_kind":"arxiv_version","alias_value":"1708.06510v2","created_at":"2026-05-18T00:19:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.06510","created_at":"2026-05-18T00:19:59Z"},{"alias_kind":"pith_short_12","alias_value":"VXXMYNNQQVFY","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VXXMYNNQQVFY7UNV","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VXXMYNNQ","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:77cd194f49b39cfe4e659d0a80e8e5a6d8566665bb73ce00cb99f31bb3d5fc44","target":"graph","created_at":"2026-05-18T00:19:59Z","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":"Homographs, words with different meanings but the same surface form, have long caused difficulty for machine translation systems, as it is difficult to select the correct translation based on the context. However, with the advent of neural machine translation (NMT) systems, which can theoretically take into account global sentential context, one may hypothesize that this problem has been alleviated. In this paper, we first provide empirical evidence that existing NMT systems in fact still have significant problems in properly translating ambiguous words. We then proceed to describe methods, in","authors_text":"Frederick Liu, Graham Neubig, Han Lu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-22T06:48:27Z","title":"Handling Homographs in Neural Machine Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.06510","kind":"arxiv","version":2},"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:bf525e5ba0691efbf55957bed28e725b4fede12bc83179672cb31b3d63d35c1c","target":"record","created_at":"2026-05-18T00:19:59Z","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":"2540055a4373128eb058951c3c238e2613be12dcd2bdf06d6a2cb9a6e759c125","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-22T06:48:27Z","title_canon_sha256":"1e6c1c4342a5acfcb9212d7ef5ec3ae1218a8d946ea508f2d4b6300cd280ff30"},"schema_version":"1.0","source":{"id":"1708.06510","kind":"arxiv","version":2}},"canonical_sha256":"adeecc35b0854b8fd1b576f843d08c80984a2ebb547685c5aca1f22ebacd35ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"adeecc35b0854b8fd1b576f843d08c80984a2ebb547685c5aca1f22ebacd35ae","first_computed_at":"2026-05-18T00:19:59.002353Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:19:59.002353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"epNNh+CJt9eLY5ZdwKra+SnzFYSMTAKvntkvAiF5xRGBZvxiRwCsW00BWZfXYW7IYwLuKVRfSQ/qmB6nWtkfBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:19:59.002899Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.06510","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bf525e5ba0691efbf55957bed28e725b4fede12bc83179672cb31b3d63d35c1c","sha256:77cd194f49b39cfe4e659d0a80e8e5a6d8566665bb73ce00cb99f31bb3d5fc44"],"state_sha256":"a4307d69f6c03cde1892690506bb3e21e31a63672b72369d9b2d86fc661a5078"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bciSWe3VC8cKKbrZf8zoOpEsw7ESdL1GtEhurEUAnPHtBnavsklYnI45dyMW3ZJxAXDO2kRUu6u3ZlrberrlCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T19:28:46.398309Z","bundle_sha256":"81dd753076c6eb19998d5667a08b2b88b7f14790968d84e56267ce6c80668b64"}}