Learning Novelty-Aware Ranking of Answers to Complex Questions

Abstract

Result ranking diversification has become an important issue for web search, summarization, and question answering. For more complex questions with multiple aspects, such as those in community-based question answering (CQA) sites, a retrieval system should provide a diversified set of relevant results, addressing the different aspects of the query, while minimizing redundancy or repetition. We present a new method, DRN , which learns novelty-related features from unlabeled data with minimal social signals, to emphasize diversity in ranking. Specifically, DRN parameterizes question-answer interactions via an LSTM representation, coupled with an extension of neural tensor network, which in turn is combined with a novelty-driven sampling approach to automatically generate training data. DRN provides a novel and general approach to complex question answering diversification and suggests promising directions for search improvements.