Question answering in conversations: Query refinement using contextual and semantic information
This paper introduces a query refinement method applied to questions asked by users to a system during a meeting or a conversation that they have with other users. To answer the questions, the proposed method leverages the local context of the conversation along with semantic resources, either WordNet or word embeddings from word2vec. The method first represents the local context by extracting keywords from the transcript of the conversation, which is obtained from a real time Automatic Speech Recognition (ASR) system and may contain noise. It then expands the queries with keywords that best represent the topic of the query, i.e. expansion keywords accompanied by weights indicating their topical similarity to the query. Finally, semantically related terms are added, using two options: either synonymous terms drawn from WordNet or similar words based on distributed representations in a low dimensional word embedding space learned using word2vec. To evaluate the system, we introduce a dataset (named AREX for AMI Requests for Explanations) and an evaluation metric based on relevance judgments collected by crowdsourcing. We compare our query expansion approach with other methods, over queries from the AREX dataset, showing the superiority of our method when either manual or automatic transcripts of the AMI Meeting Corpus are used. This paper introduces a query refinement method applied to questions asked by users during a meeting.To answer the questions, our method leverages the local context with external semantic resources, either WordNet or word embeddings.The method first represents the local context by extracting keywords from the transcript of the conversation.The proposed method then expands the queries with keywords that best represent the topic of the query.We compare our query expansion approach, showing its superiority when manual or automatic transcripts are used.
Data Knowl. Eng.