Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks

TLDR: The Noise-Contrastive Estimation approach is extended with a triplet ranking loss function to exploit interactions in triplet inputs over the question paired with positive and negative examples and achieves state-of-the-art effectiveness without the need for external knowledge sources or feature engineering.

Abstract: We study answer selection for question answering, in which given a question and a set of candidate answer sentences, the goal is to identify the subset that contains the answer. Unlike previous work which treats this task as a straightforward pointwise classification problem, we model this problem as a ranking task and propose a pairwise ranking approach that can directly exploit existing pointwise neural network models as base components. We extend the Noise-Contrastive Estimation approach with a triplet ranking loss function to exploit interactions in triplet inputs over the question paired with positive and negative examples. Experiments on TrecQA and WikiQA datasets show that our approach achieves state-of-the-art effectiveness without the need for external knowledge sources or feature engineering.