Towards Simple but Efficient Next Utterance Ranking

Abstract

Retrieval-based dialogue systems converse with humans by ranking candidate responses according to their relevance to the history of the conversation (context). Recent studies either match the context with the response on only sequence level or use complex architectures to match them on the word and sequence levels. We show that both information levels are important and that a simple architecture can capture them effectively. We propose an end to endmulti level response retrieval dialogue system. Our model learns to match the context with the best response by computing their semantic similarity on the word and sequence levels. Empirical evaluation on two dialogue datasets shows that our model outperforms several state of the art systems and performs as good as the best system while being conceptually simpler.