Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI最新文献

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CopyBERT: A Unified Approach to Question Generation with Self-Attention 一种统一的自我关注问题生成方法
Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI Pub Date : 2020-07-01 DOI: 10.18653/v1/2020.nlp4convai-1.3
Stalin Varanasi, Saadullah Amin, G. Neumann
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引用次数: 11
Automating Template Creation for Ranking-Based Dialogue Models 为基于排名的对话模型自动创建模板
Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI Pub Date : 2020-07-01 DOI: 10.18653/v1/2020.nlp4convai-1.9
Jingxiang Chen, Heba Elfardy, Simi Wang, Andrea Kahn, Jared Kramer
{"title":"Automating Template Creation for Ranking-Based Dialogue Models","authors":"Jingxiang Chen, Heba Elfardy, Simi Wang, Andrea Kahn, Jared Kramer","doi":"10.18653/v1/2020.nlp4convai-1.9","DOIUrl":"https://doi.org/10.18653/v1/2020.nlp4convai-1.9","url":null,"abstract":"Dialogue response generation models that use template ranking rather than direct sequence generation allow model developers to limit generated responses to pre-approved messages. However, manually creating templates is time-consuming and requires domain expertise. To alleviate this problem, we explore automating the process of creating dialogue templates by using unsupervised methods to cluster historical utterances and selecting representative utterances from each cluster. Specifically, we propose an end-to-end model called Deep Sentence Encoder Clustering (DSEC) that uses an auto-encoder structure to jointly learn the utterance representation and construct template clusters. We compare this method to a random baseline that randomly assigns templates to clusters as well as a strong baseline that performs the sentence encoding and the utterance clustering sequentially. To evaluate the performance of the proposed method, we perform an automatic evaluation with two annotated customer service datasets to assess clustering effectiveness, and a human-in-the-loop experiment using a live customer service application to measure the acceptance rate of the generated templates. DSEC performs best in the automatic evaluation, beats both the sequential and random baselines on most metrics in the human-in-the-loop experiment, and shows promising results when compared to gold/manually created templates.","PeriodicalId":407342,"journal":{"name":"Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114256519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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