Fine-Grained Sentence Functions for Short-Text Conversation

Abstract

Sentence function is an important linguistic feature referring to a user's purpose in uttering a specific sentence. The use of sentence function has shown promising results to improve the performance of conversation models. However, there is no large conversation dataset annotated with sentence functions. In this work, we collect a new Short-Text Conversation dataset with manually annotated SEntence FUNctions (STC-Sefun). Classification models are trained on this dataset to (i) recognize the sentence function of new data in a large corpus of short-text conversations; (ii) estimate a proper sentence function of the response given a test query. We later train conversation models conditioned on the sentence functions, including information retrieval-based and neural generative models. Experimental results demonstrate that the use of sentence functions can help improve the quality of the returned responses.

Here is a revised version of our paper accepted by ACL2019

References in corpus (8)

• Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
• Convolutional Neural Networks for Sentence Classification
• Convolutional Neural Network Architectures for Matching Natural Language Sentences
• How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
• Topic Aware Neural Response Generation
• An Information Retrieval Approach to Short Text Conversation
• StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation
• Sentiment Adaptive End-to-End Dialog Systems

Cited by in corpus (2)

• REAM$♯$: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation
• Predict and Use Latent Patterns for Short-Text Conversation