Chinese semantic matching method based on twinborn interaction and fine tuning representation

The invention discloses a Chinese semantic matching method based on twinborn interaction and fine tuning representation, which comprises the following steps of: firstly, completing vector initialization of a text by using a RoBERTa-WWM-EXT pre-training model, and constructing a twinborn structure em...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: LI BAOLIAN, QIANG BAOHUA, XI GUANGYONG, CHEN JINYONG, WANG YUFENG
Format: Patent
Sprache:chi ; eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator LI BAOLIAN
QIANG BAOHUA
XI GUANGYONG
CHEN JINYONG
WANG YUFENG
description The invention discloses a Chinese semantic matching method based on twinborn interaction and fine tuning representation, which comprises the following steps of: firstly, completing vector initialization of a text by using a RoBERTa-WWM-EXT pre-training model, and constructing a twinborn structure embedded with a soft alignment attention mechanism (SA-attention) and a BiLSTM training layer aiming at an initial feature vector so as to enhance the semantic interaction between sentence pairs; secondly, two texts to be matched are connected to be connected into a RoBERTa-WWM-EXT pre-training model for vectorization, and a connected vectorization result is input into an LSTM-BiLSTM network layer for enhancement training so as to strengthen the upper and lower semantic relation in a sentence; and then a training model capable of finely tuning RoBERTa-WWM-EXT initial vectors is built to generate text vectors subjected to tag supervision fine tuning, so that the expression strength of the vectors on the semantic relat
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN115345175A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN115345175A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN115345175A3</originalsourceid><addsrcrecordid>eNqNjDEKAjEQRdNYiHqH8QAWYQ3WEhQrK1tZZpNZN7CZhGTE65sFD2D14f3_31o97RSYKkGliCzBQURxjb0gkkzJw4CVPCQG-QQeUmEILFTQSWgQ2cPYDCBvXk6Fcmk6FlzqrVqNOFfa_XKj9tfLw94OlFNPNaMjJuntXWvTHY0-mXP3z-YLNvA8zw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Chinese semantic matching method based on twinborn interaction and fine tuning representation</title><source>esp@cenet</source><creator>LI BAOLIAN ; QIANG BAOHUA ; XI GUANGYONG ; CHEN JINYONG ; WANG YUFENG</creator><creatorcontrib>LI BAOLIAN ; QIANG BAOHUA ; XI GUANGYONG ; CHEN JINYONG ; WANG YUFENG</creatorcontrib><description>The invention discloses a Chinese semantic matching method based on twinborn interaction and fine tuning representation, which comprises the following steps of: firstly, completing vector initialization of a text by using a RoBERTa-WWM-EXT pre-training model, and constructing a twinborn structure embedded with a soft alignment attention mechanism (SA-attention) and a BiLSTM training layer aiming at an initial feature vector so as to enhance the semantic interaction between sentence pairs; secondly, two texts to be matched are connected to be connected into a RoBERTa-WWM-EXT pre-training model for vectorization, and a connected vectorization result is input into an LSTM-BiLSTM network layer for enhancement training so as to strengthen the upper and lower semantic relation in a sentence; and then a training model capable of finely tuning RoBERTa-WWM-EXT initial vectors is built to generate text vectors subjected to tag supervision fine tuning, so that the expression strength of the vectors on the semantic relat</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20221115&amp;DB=EPODOC&amp;CC=CN&amp;NR=115345175A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20221115&amp;DB=EPODOC&amp;CC=CN&amp;NR=115345175A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LI BAOLIAN</creatorcontrib><creatorcontrib>QIANG BAOHUA</creatorcontrib><creatorcontrib>XI GUANGYONG</creatorcontrib><creatorcontrib>CHEN JINYONG</creatorcontrib><creatorcontrib>WANG YUFENG</creatorcontrib><title>Chinese semantic matching method based on twinborn interaction and fine tuning representation</title><description>The invention discloses a Chinese semantic matching method based on twinborn interaction and fine tuning representation, which comprises the following steps of: firstly, completing vector initialization of a text by using a RoBERTa-WWM-EXT pre-training model, and constructing a twinborn structure embedded with a soft alignment attention mechanism (SA-attention) and a BiLSTM training layer aiming at an initial feature vector so as to enhance the semantic interaction between sentence pairs; secondly, two texts to be matched are connected to be connected into a RoBERTa-WWM-EXT pre-training model for vectorization, and a connected vectorization result is input into an LSTM-BiLSTM network layer for enhancement training so as to strengthen the upper and lower semantic relation in a sentence; and then a training model capable of finely tuning RoBERTa-WWM-EXT initial vectors is built to generate text vectors subjected to tag supervision fine tuning, so that the expression strength of the vectors on the semantic relat</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjDEKAjEQRdNYiHqH8QAWYQ3WEhQrK1tZZpNZN7CZhGTE65sFD2D14f3_31o97RSYKkGliCzBQURxjb0gkkzJw4CVPCQG-QQeUmEILFTQSWgQ2cPYDCBvXk6Fcmk6FlzqrVqNOFfa_XKj9tfLw94OlFNPNaMjJuntXWvTHY0-mXP3z-YLNvA8zw</recordid><startdate>20221115</startdate><enddate>20221115</enddate><creator>LI BAOLIAN</creator><creator>QIANG BAOHUA</creator><creator>XI GUANGYONG</creator><creator>CHEN JINYONG</creator><creator>WANG YUFENG</creator><scope>EVB</scope></search><sort><creationdate>20221115</creationdate><title>Chinese semantic matching method based on twinborn interaction and fine tuning representation</title><author>LI BAOLIAN ; QIANG BAOHUA ; XI GUANGYONG ; CHEN JINYONG ; WANG YUFENG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN115345175A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>LI BAOLIAN</creatorcontrib><creatorcontrib>QIANG BAOHUA</creatorcontrib><creatorcontrib>XI GUANGYONG</creatorcontrib><creatorcontrib>CHEN JINYONG</creatorcontrib><creatorcontrib>WANG YUFENG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LI BAOLIAN</au><au>QIANG BAOHUA</au><au>XI GUANGYONG</au><au>CHEN JINYONG</au><au>WANG YUFENG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Chinese semantic matching method based on twinborn interaction and fine tuning representation</title><date>2022-11-15</date><risdate>2022</risdate><abstract>The invention discloses a Chinese semantic matching method based on twinborn interaction and fine tuning representation, which comprises the following steps of: firstly, completing vector initialization of a text by using a RoBERTa-WWM-EXT pre-training model, and constructing a twinborn structure embedded with a soft alignment attention mechanism (SA-attention) and a BiLSTM training layer aiming at an initial feature vector so as to enhance the semantic interaction between sentence pairs; secondly, two texts to be matched are connected to be connected into a RoBERTa-WWM-EXT pre-training model for vectorization, and a connected vectorization result is input into an LSTM-BiLSTM network layer for enhancement training so as to strengthen the upper and lower semantic relation in a sentence; and then a training model capable of finely tuning RoBERTa-WWM-EXT initial vectors is built to generate text vectors subjected to tag supervision fine tuning, so that the expression strength of the vectors on the semantic relat</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN115345175A
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
title Chinese semantic matching method based on twinborn interaction and fine tuning representation
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T16%3A29%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=LI%20BAOLIAN&rft.date=2022-11-15&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN115345175A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true