{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:P6KWYBZRSW54BQM6OBAXH37DAF","short_pith_number":"pith:P6KWYBZR","canonical_record":{"source":{"id":"1811.09353","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-11-23T03:39:27Z","cross_cats_sorted":[],"title_canon_sha256":"76a4a0f87e631e9437c74c128b358cd9acf354596ad099c51e4b74c36fe4962f","abstract_canon_sha256":"5a5d9bf8b294cc12a245b08eb7a7edc90d28166579b089fcd84ae572634c98eb"},"schema_version":"1.0"},"canonical_sha256":"7f956c073195bbc0c19e704173efe30147480b18e38ff6241ac2f0a6feb314cd","source":{"kind":"arxiv","id":"1811.09353","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.09353","created_at":"2026-05-17T23:43:09Z"},{"alias_kind":"arxiv_version","alias_value":"1811.09353v2","created_at":"2026-05-17T23:43:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.09353","created_at":"2026-05-17T23:43:09Z"},{"alias_kind":"pith_short_12","alias_value":"P6KWYBZRSW54","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"P6KWYBZRSW54BQM6","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"P6KWYBZR","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:P6KWYBZRSW54BQM6OBAXH37DAF","target":"record","payload":{"canonical_record":{"source":{"id":"1811.09353","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-11-23T03:39:27Z","cross_cats_sorted":[],"title_canon_sha256":"76a4a0f87e631e9437c74c128b358cd9acf354596ad099c51e4b74c36fe4962f","abstract_canon_sha256":"5a5d9bf8b294cc12a245b08eb7a7edc90d28166579b089fcd84ae572634c98eb"},"schema_version":"1.0"},"canonical_sha256":"7f956c073195bbc0c19e704173efe30147480b18e38ff6241ac2f0a6feb314cd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:09.304480Z","signature_b64":"TmZ1cqwYmmd2VizxDTgzV1UruAvZsJ7m7bm5CxsM3ZTTxDJ8fk0JQKgOPEFCd3BCyrpxfEuLPcW0YRuEzleWAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f956c073195bbc0c19e704173efe30147480b18e38ff6241ac2f0a6feb314cd","last_reissued_at":"2026-05-17T23:43:09.304027Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:09.304027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.09353","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":"2026-05-17T23:43:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/8xaE2Ps9zpuX6Y8i7h3H8b6PBm9gxdDs6CF0Km5vGOxkU4XKEsGikbdK7cA/5pMXs7nprHWwZH5gQBI36aNCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T14:52:10.613689Z"},"content_sha256":"e7c3175869d6426f207d32225bfc97d39e70afd05740c8a243d80a36f785abd7","schema_version":"1.0","event_id":"sha256:e7c3175869d6426f207d32225bfc97d39e70afd05740c8a243d80a36f785abd7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:P6KWYBZRSW54BQM6OBAXH37DAF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning to Discover, Ground and Use Words with Segmental Neural Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chris Dyer, Kazuya Kawakami, Phil Blunsom","submitted_at":"2018-11-23T03:39:27Z","abstract_excerpt":"We propose a segmental neural language model that combines the generalization power of neural networks with the ability to discover word-like units that are latent in unsegmented character sequences. In contrast to previous segmentation models that treat word segmentation as an isolated task, our model unifies word discovery, learning how words fit together to form sentences, and, by conditioning the model on visual context, how words' meanings ground in representations of non-linguistic modalities. Experiments show that the unconditional model learns predictive distributions better than chara"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.09353","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":"2026-05-17T23:43:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LVIhv/sqTtCIKwwoqQptMWhcrYxwksY2sy3CbwqwY/HkZH6QyWCurij386SlVVARjmmx+HrfkUWefVEwHQdCAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T14:52:10.614357Z"},"content_sha256":"bf4f69064795b36f2f5511dc27f84eaa170a4d61f61f34616058b6fa913a1489","schema_version":"1.0","event_id":"sha256:bf4f69064795b36f2f5511dc27f84eaa170a4d61f61f34616058b6fa913a1489"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P6KWYBZRSW54BQM6OBAXH37DAF/bundle.json","state_url":"https://pith.science/pith/P6KWYBZRSW54BQM6OBAXH37DAF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P6KWYBZRSW54BQM6OBAXH37DAF/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":"2026-05-24T14:52:10Z","links":{"resolver":"https://pith.science/pith/P6KWYBZRSW54BQM6OBAXH37DAF","bundle":"https://pith.science/pith/P6KWYBZRSW54BQM6OBAXH37DAF/bundle.json","state":"https://pith.science/pith/P6KWYBZRSW54BQM6OBAXH37DAF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P6KWYBZRSW54BQM6OBAXH37DAF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:P6KWYBZRSW54BQM6OBAXH37DAF","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":"5a5d9bf8b294cc12a245b08eb7a7edc90d28166579b089fcd84ae572634c98eb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-11-23T03:39:27Z","title_canon_sha256":"76a4a0f87e631e9437c74c128b358cd9acf354596ad099c51e4b74c36fe4962f"},"schema_version":"1.0","source":{"id":"1811.09353","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.09353","created_at":"2026-05-17T23:43:09Z"},{"alias_kind":"arxiv_version","alias_value":"1811.09353v2","created_at":"2026-05-17T23:43:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.09353","created_at":"2026-05-17T23:43:09Z"},{"alias_kind":"pith_short_12","alias_value":"P6KWYBZRSW54","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"P6KWYBZRSW54BQM6","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"P6KWYBZR","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:bf4f69064795b36f2f5511dc27f84eaa170a4d61f61f34616058b6fa913a1489","target":"graph","created_at":"2026-05-17T23:43:09Z","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":"We propose a segmental neural language model that combines the generalization power of neural networks with the ability to discover word-like units that are latent in unsegmented character sequences. In contrast to previous segmentation models that treat word segmentation as an isolated task, our model unifies word discovery, learning how words fit together to form sentences, and, by conditioning the model on visual context, how words' meanings ground in representations of non-linguistic modalities. Experiments show that the unconditional model learns predictive distributions better than chara","authors_text":"Chris Dyer, Kazuya Kawakami, Phil Blunsom","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-11-23T03:39:27Z","title":"Learning to Discover, Ground and Use Words with Segmental Neural Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.09353","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:e7c3175869d6426f207d32225bfc97d39e70afd05740c8a243d80a36f785abd7","target":"record","created_at":"2026-05-17T23:43:09Z","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":"5a5d9bf8b294cc12a245b08eb7a7edc90d28166579b089fcd84ae572634c98eb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-11-23T03:39:27Z","title_canon_sha256":"76a4a0f87e631e9437c74c128b358cd9acf354596ad099c51e4b74c36fe4962f"},"schema_version":"1.0","source":{"id":"1811.09353","kind":"arxiv","version":2}},"canonical_sha256":"7f956c073195bbc0c19e704173efe30147480b18e38ff6241ac2f0a6feb314cd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7f956c073195bbc0c19e704173efe30147480b18e38ff6241ac2f0a6feb314cd","first_computed_at":"2026-05-17T23:43:09.304027Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:09.304027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TmZ1cqwYmmd2VizxDTgzV1UruAvZsJ7m7bm5CxsM3ZTTxDJ8fk0JQKgOPEFCd3BCyrpxfEuLPcW0YRuEzleWAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:09.304480Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.09353","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e7c3175869d6426f207d32225bfc97d39e70afd05740c8a243d80a36f785abd7","sha256:bf4f69064795b36f2f5511dc27f84eaa170a4d61f61f34616058b6fa913a1489"],"state_sha256":"46c0eb9d5f3b6ce7238a18034f32dbd423919b686371d7e34d865a8b38197fa4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Eg82V+qTyBOkyeNl9a2a+owusTNfvMI2CZXN2nGtf9DWHrHmtb1WkKFnlrcHfj63p0R+3uIAZlIILh7cw8Y8Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T14:52:10.618052Z","bundle_sha256":"49a42a5df3ea3a40e6179a53c7d6b35b91e528d05175a4fba693b28cb8292062"}}