{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:JIDJGOUDCFLLTKSUXI72TYIJAA","short_pith_number":"pith:JIDJGOUD","schema_version":"1.0","canonical_sha256":"4a06933a831156b9aa54ba3fa9e1090023f1ebbdbd8b161ef86ad72deae8c42a","source":{"kind":"arxiv","id":"1608.04207","version":3},"attestation_state":"computed","paper":{"title":"Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Einat Kermany, Ofer Lavi, Yoav Goldberg, Yonatan Belinkov, Yossi Adi","submitted_at":"2016-08-15T08:51:38Z","abstract_excerpt":"There is a lot of research interest in encoding variable length sentences into fixed length vectors, in a way that preserves the sentence meanings. Two common methods include representations based on averaging word vectors, and representations based on the hidden states of recurrent neural networks such as LSTMs. The sentence vectors are used as features for subsequent machine learning tasks or for pre-training in the context of deep learning. However, not much is known about the properties that are encoded in these sentence representations and about the language information they capture. We p"},"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":"1608.04207","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-15T08:51:38Z","cross_cats_sorted":[],"title_canon_sha256":"7902edc6771637d9e40fe31cd7ba2dc2949f191891c3da07083d6c464b587e3f","abstract_canon_sha256":"dd681f42abe24b771c8e89c547dfa953f86b60953b437d887c12e00f63cfcb4b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:51:03.115951Z","signature_b64":"t1tZK7L4f3CVznL8mCqTF1NVt++yUkNXQ/nBDDLEx2Xt7ccIYxYBEjHVtZTFNChSnNPHl3/mArasnfhFjfadDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a06933a831156b9aa54ba3fa9e1090023f1ebbdbd8b161ef86ad72deae8c42a","last_reissued_at":"2026-05-18T00:51:03.115352Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:51:03.115352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Einat Kermany, Ofer Lavi, Yoav Goldberg, Yonatan Belinkov, Yossi Adi","submitted_at":"2016-08-15T08:51:38Z","abstract_excerpt":"There is a lot of research interest in encoding variable length sentences into fixed length vectors, in a way that preserves the sentence meanings. Two common methods include representations based on averaging word vectors, and representations based on the hidden states of recurrent neural networks such as LSTMs. The sentence vectors are used as features for subsequent machine learning tasks or for pre-training in the context of deep learning. However, not much is known about the properties that are encoded in these sentence representations and about the language information they capture. We p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.04207","kind":"arxiv","version":3},"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":"1608.04207","created_at":"2026-05-18T00:51:03.115447+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.04207v3","created_at":"2026-05-18T00:51:03.115447+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.04207","created_at":"2026-05-18T00:51:03.115447+00:00"},{"alias_kind":"pith_short_12","alias_value":"JIDJGOUDCFLL","created_at":"2026-05-18T12:30:25.849896+00:00"},{"alias_kind":"pith_short_16","alias_value":"JIDJGOUDCFLLTKSU","created_at":"2026-05-18T12:30:25.849896+00:00"},{"alias_kind":"pith_short_8","alias_value":"JIDJGOUD","created_at":"2026-05-18T12:30:25.849896+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"1906.08340","citing_title":"Learning Compressed Sentence Representations for On-Device Text Processing","ref_index":1,"is_internal_anchor":true},{"citing_arxiv_id":"2102.12452","citing_title":"Probing Classifiers: Promises, Shortcomings, and Advances","ref_index":63,"is_internal_anchor":true},{"citing_arxiv_id":"2604.08693","citing_title":"Towards Generalizable Representations of Mathematical Strategies","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"1609.07843","citing_title":"Pointer Sentinel Mixture Models","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21555","citing_title":"Finding Meaning in Embeddings: Concept Separation Curves","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JIDJGOUDCFLLTKSUXI72TYIJAA","json":"https://pith.science/pith/JIDJGOUDCFLLTKSUXI72TYIJAA.json","graph_json":"https://pith.science/api/pith-number/JIDJGOUDCFLLTKSUXI72TYIJAA/graph.json","events_json":"https://pith.science/api/pith-number/JIDJGOUDCFLLTKSUXI72TYIJAA/events.json","paper":"https://pith.science/paper/JIDJGOUD"},"agent_actions":{"view_html":"https://pith.science/pith/JIDJGOUDCFLLTKSUXI72TYIJAA","download_json":"https://pith.science/pith/JIDJGOUDCFLLTKSUXI72TYIJAA.json","view_paper":"https://pith.science/paper/JIDJGOUD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.04207&json=true","fetch_graph":"https://pith.science/api/pith-number/JIDJGOUDCFLLTKSUXI72TYIJAA/graph.json","fetch_events":"https://pith.science/api/pith-number/JIDJGOUDCFLLTKSUXI72TYIJAA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JIDJGOUDCFLLTKSUXI72TYIJAA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JIDJGOUDCFLLTKSUXI72TYIJAA/action/storage_attestation","attest_author":"https://pith.science/pith/JIDJGOUDCFLLTKSUXI72TYIJAA/action/author_attestation","sign_citation":"https://pith.science/pith/JIDJGOUDCFLLTKSUXI72TYIJAA/action/citation_signature","submit_replication":"https://pith.science/pith/JIDJGOUDCFLLTKSUXI72TYIJAA/action/replication_record"}},"created_at":"2026-05-18T00:51:03.115447+00:00","updated_at":"2026-05-18T00:51:03.115447+00:00"}