Incremental LSTM-based Dialog State Tracker
A dialog state tracker is an important component in modern spoken dialog systems. We present an incremental dialog state tracker, based on LSTM networks. It directly uses automatic speech recognition hypotheses to track the state. We also present the key non-standard aspects of the model that bring...
Gespeichert in:
Hauptverfasser: | , |
---|---|
Format: | Artikel |
Sprache: | 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 | Zilka, Lukas Jurcicek, Filip |
description | A dialog state tracker is an important component in modern spoken dialog
systems. We present an incremental dialog state tracker, based on LSTM
networks. It directly uses automatic speech recognition hypotheses to track the
state. We also present the key non-standard aspects of the model that bring its
performance close to the state-of-the-art and experimentally analyze their
contribution: including the ASR confidence scores, abstracting scarcely
represented values, including transcriptions in the training data, and model
averaging. |
doi_str_mv | 10.48550/arxiv.1507.03471 |
format | Article |
fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_1507_03471</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1507_03471</sourcerecordid><originalsourceid>FETCH-LOGICAL-a671-d6b2903cc3f0b1280c8fb6bf39f7c80a3b1c14c831941394225109fb282accb83</originalsourceid><addsrcrecordid>eNotzrFuwjAQgGEvHSroA3RqdpRw53MSe6woUKSgDmSPzoeNIgKtTFSVt68Knf7t16fUM0JhbFnCnNNP_11gCXUBZGp8VLPNWVI4hfPIQ9bs2m3u-RL22VvPw-ch2408hqxNLMeQpuoh8nAJT_-dqHa1bBfvefOx3ixem5yrGvN95bUDEqEIHrUFsdFXPpKLtVhg8ihoxBI6g-SM1iWCi15bzSLe0kS93Lc3bfeV-hOna_en7m5q-gUvXzrf</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Incremental LSTM-based Dialog State Tracker</title><source>arXiv.org</source><creator>Zilka, Lukas ; Jurcicek, Filip</creator><creatorcontrib>Zilka, Lukas ; Jurcicek, Filip</creatorcontrib><description>A dialog state tracker is an important component in modern spoken dialog
systems. We present an incremental dialog state tracker, based on LSTM
networks. It directly uses automatic speech recognition hypotheses to track the
state. We also present the key non-standard aspects of the model that bring its
performance close to the state-of-the-art and experimentally analyze their
contribution: including the ASR confidence scores, abstracting scarcely
represented values, including transcriptions in the training data, and model
averaging.</description><identifier>DOI: 10.48550/arxiv.1507.03471</identifier><language>eng</language><subject>Computer Science - Computation and Language</subject><creationdate>2015-07</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1507.03471$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1507.03471$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Zilka, Lukas</creatorcontrib><creatorcontrib>Jurcicek, Filip</creatorcontrib><title>Incremental LSTM-based Dialog State Tracker</title><description>A dialog state tracker is an important component in modern spoken dialog
systems. We present an incremental dialog state tracker, based on LSTM
networks. It directly uses automatic speech recognition hypotheses to track the
state. We also present the key non-standard aspects of the model that bring its
performance close to the state-of-the-art and experimentally analyze their
contribution: including the ASR confidence scores, abstracting scarcely
represented values, including transcriptions in the training data, and model
averaging.</description><subject>Computer Science - Computation and Language</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzrFuwjAQgGEvHSroA3RqdpRw53MSe6woUKSgDmSPzoeNIgKtTFSVt68Knf7t16fUM0JhbFnCnNNP_11gCXUBZGp8VLPNWVI4hfPIQ9bs2m3u-RL22VvPw-ch2408hqxNLMeQpuoh8nAJT_-dqHa1bBfvefOx3ixem5yrGvN95bUDEqEIHrUFsdFXPpKLtVhg8ihoxBI6g-SM1iWCi15bzSLe0kS93Lc3bfeV-hOna_en7m5q-gUvXzrf</recordid><startdate>20150713</startdate><enddate>20150713</enddate><creator>Zilka, Lukas</creator><creator>Jurcicek, Filip</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20150713</creationdate><title>Incremental LSTM-based Dialog State Tracker</title><author>Zilka, Lukas ; Jurcicek, Filip</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a671-d6b2903cc3f0b1280c8fb6bf39f7c80a3b1c14c831941394225109fb282accb83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Computer Science - Computation and Language</topic><toplevel>online_resources</toplevel><creatorcontrib>Zilka, Lukas</creatorcontrib><creatorcontrib>Jurcicek, Filip</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zilka, Lukas</au><au>Jurcicek, Filip</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Incremental LSTM-based Dialog State Tracker</atitle><date>2015-07-13</date><risdate>2015</risdate><abstract>A dialog state tracker is an important component in modern spoken dialog
systems. We present an incremental dialog state tracker, based on LSTM
networks. It directly uses automatic speech recognition hypotheses to track the
state. We also present the key non-standard aspects of the model that bring its
performance close to the state-of-the-art and experimentally analyze their
contribution: including the ASR confidence scores, abstracting scarcely
represented values, including transcriptions in the training data, and model
averaging.</abstract><doi>10.48550/arxiv.1507.03471</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | DOI: 10.48550/arxiv.1507.03471 |
ispartof | |
issn | |
language | eng |
recordid | cdi_arxiv_primary_1507_03471 |
source | arXiv.org |
subjects | Computer Science - Computation and Language |
title | Incremental LSTM-based Dialog State Tracker |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T15%3A50%3A39IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Incremental%20LSTM-based%20Dialog%20State%20Tracker&rft.au=Zilka,%20Lukas&rft.date=2015-07-13&rft_id=info:doi/10.48550/arxiv.1507.03471&rft_dat=%3Carxiv_GOX%3E1507_03471%3C/arxiv_GOX%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 |