{"id":"https://openalex.org/W2805858624","doi":null,"title":"Learning Semantic Sentence Embeddings using Pair-wise Discriminator.","display_name":"Learning Semantic Sentence Embeddings using Pair-wise Discriminator.","publication_year":2018,"publication_date":"2018-06-03","ids":{"openalex":"https://openalex.org/W2805858624","mag":"2805858624"},"language":"en","primary_location":{"id":"mag:2805858624","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1806.00807.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1806.00807.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035714586","display_name":"Badri N. Patro","orcid":"https://orcid.org/0000-0002-2585-7014","observed_orcids":["https://orcid.org/0000-0002-2585-7014"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Badri N. Patro","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-2585-7014","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082361685","display_name":"Vinod K Kurmi","orcid":"https://orcid.org/0000-0003-0332-5647","observed_orcids":["https://orcid.org/0000-0003-0332-5647"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vinod K Kurmi","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-0332-5647","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010379237","display_name":"Sandeep Kumar","orcid":"https://orcid.org/0000-0003-4125-4165","observed_orcids":["https://orcid.org/0000-0003-4125-4165"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sandeep Kumar","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-4125-4165","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5007109424","display_name":"Vinay P. Namboodiri","orcid":"https://orcid.org/0000-0001-5262-9722","observed_orcids":["https://orcid.org/0000-0001-5262-9722"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vinay P. Namboodiri","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-5262-9722","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.0026000000070780516,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.0019000000320374966,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sentence-embeddings","display_name":"sentence embeddings","score":0.9390000104904175},{"id":"https://openalex.org/keywords/semantic-embeddings","display_name":"semantic embeddings","score":0.7139999866485596},{"id":"https://openalex.org/keywords/paraphrase-generation","display_name":"paraphrase generation","score":0.9440000057220459},{"id":"https://openalex.org/keywords/encoder-decoder-architecture","display_name":"encoder-decoder architecture","score":0.5879999995231628},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"sentiment analysis","score":0.7910000085830688},{"id":"https://openalex.org/keywords/word-embeddings","display_name":"word embeddings","score":0.49000000953674316}],"concepts":[{"id":"https://openalex.org/C2780922921","wikidata":"https://www.wikidata.org/wiki/Q255189","display_name":"Paraphrase","level":2,"score":0.9669647216796875},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7732628583908081},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.745291531085968},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7314743995666504},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7068822979927063},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6285857558250427},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5350161194801331},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5186680555343628},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4962652325630188},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4736393988132477},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.42410287261009216},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18366411328315735},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2805858624","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1806.00807.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:2805858624","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1806.00807.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W42251416","https://openalex.org/W113586804","https://openalex.org/W1423339008","https://openalex.org/W1565746575","https://openalex.org/W1614298861","https://openalex.org/W1843891098","https://openalex.org/W1965605789","https://openalex.org/W1984052055","https://openalex.org/W2064675550","https://openalex.org/W2100693535","https://openalex.org/W2101105183","https://openalex.org/W2118772635","https://openalex.org/W2120615054","https://openalex.org/W2123301721","https://openalex.org/W2125544673","https://openalex.org/W2130942839","https://openalex.org/W2131571251","https://openalex.org/W2131744502","https://openalex.org/W2142920810","https://openalex.org/W2147152072","https://openalex.org/W2149327368","https://openalex.org/W2153579005","https://openalex.org/W2163455955","https://openalex.org/W2165612380","https://openalex.org/W2168625136","https://openalex.org/W2173681125","https://openalex.org/W2251849926","https://openalex.org/W2251939518","https://openalex.org/W2605045867","https://openalex.org/W2735642330","https://openalex.org/W2882319491","https://openalex.org/W2962717047","https://openalex.org/W2962953307","https://openalex.org/W2963546833","https://openalex.org/W2963963856","https://openalex.org/W2964159778","https://openalex.org/W2964268978"],"related_works":["https://openalex.org/W2815449720","https://openalex.org/W2996891880","https://openalex.org/W2467461598","https://openalex.org/W2175723921","https://openalex.org/W2770626128","https://openalex.org/W2622000134","https://openalex.org/W2794376802","https://openalex.org/W7098367963","https://openalex.org/W7162819847","https://openalex.org/W2963535053"],"abstract_inverted_index":{"In":[0],"this":[1,75],"paper,":[2],"we":[3,22,48,57],"propose":[4,23],"a":[5,24,35,50,101,115,138,153],"method":[6,26,37,146,159],"for":[7,27,54,80,152],"obtaining":[8,28],"sentence-level":[9,29],"embeddings.":[10,30],"While":[11],"the":[12,39,43,60,68,109,149,166,169],"problem":[13],"of":[14,41],"securing":[15],"word-level":[16],"embeddings":[17,83,92,151,163],"is":[18,32,76,97,112,133],"very":[19],"well":[20],"studied,":[21],"novel":[25],"This":[31,96,131],"obtained":[33,150],"by":[34,77,99,147],"simple":[36],"in":[38,135,161],"context":[40],"solving":[42],"paraphrase":[44,62,82,89,123,170],"generation":[45,171],"task.":[46,156],"If":[47],"use":[49],"sequential":[51,102,139],"encoder-decoder":[52,140],"model":[53],"generating":[55],"paraphrase,":[56],"would":[58],"like":[59],"generated":[61],"to":[63,67,73,84,93,184],"be":[64,85,94,185],"semantically":[65],"close":[66,86],"original":[69],"sentence.":[70],"One":[71],"way":[72],"ensure":[74],"adding":[78],"constraints":[79],"true":[81],"and":[87,164,172],"unrelated":[88],"candidate":[90],"sentence":[91,124],"far.":[95],"ensured":[98],"using":[100],"pair-wise":[103],"discriminator":[104],"that":[105,111],"shares":[106],"weights":[107],"with":[108,114,137],"encoder":[110],"trained":[113],"suitable":[116],"loss":[117,120,132],"function.":[118],"Our":[119],"function":[121],"penalizes":[122],"embedding":[125],"distances":[126],"from":[127],"being":[128],"too":[129],"large.":[130],"used":[134],"combination":[136],"network.":[141],"We":[142],"also":[143,182],"validated":[144],"our":[145],"evaluating":[148],"sentiment":[154,173],"analysis":[155,174],"The":[157],"proposed":[158],"results":[160,180],"semantic":[162],"outperforms":[165],"state-of-the-art":[167],"on":[168,176],"task":[175],"standard":[177],"datasets.":[178],"These":[179],"are":[181],"shown":[183],"statistically":[186],"significant.":[187]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2018-06-13T00:00:00"}
