Data-driven response generation in social media

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Proceedings titleEMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
ConferenceConference on Empirical Methods in Natural Language Processing, EMNLP 2011, 27 July 2011 through 31 July 2011, Edinburgh
Pages583593; # of pages: 11
SubjectData-driven; Data-driven approach; Human evaluation; Human response; Phrase-based statistical machine translation; Response generation; Social media; Computational linguistics; Information retrieval; Natural language processing systems; Speech transmission; Translation (languages)
AbstractWe present a data-driven approach to generating responses to Twitter status posts, based on phrase-based Statistical Machine Translation. We find that mapping conversational stimuli onto responses is more difficult than translating between languages, due to the wider range of possible responses, the larger fraction of unaligned words/phrases, and the presence of large phrase pairs whose alignment cannot be further decomposed. After addressing these challenges, we compare approaches based on SMT and Information Retrieval in a human evaluation. We show that SMT outperforms IR on this task, and its output is preferred over actual human responses in 15% of cases. As far as we are aware, this is the first work to investigate the use of phrase-based SMT to directly translate a linguistic stimulus into an appropriate response. © 2011 Association for Computational Linguistics.
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AffiliationNational Research Council Canada (NRC-CNRC)
Peer reviewedYes
NPARC number21271665
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Record identifierb5199211-299f-40e9-b95b-f97b0178d6f2
Record created2014-03-24
Record modified2016-05-09
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