Attentive Recurrent Tensor Model for Community Question Answering

TL;DR

This paper introduces an attentive recurrent tensor network that effectively bridges lexical and semantic gaps in community question answering, achieving state-of-the-art results across multiple datasets.

Contribution

It presents a novel model combining token and phrase-level attention with tensor interactions, improving answer selection and triggering tasks.

Findings

Achieves state-of-the-art performance on TrecQA and WikiQA datasets.

Outperforms current methods on Yahoo! L4 and WikiQA tasks.

Introduces simplified tensor matrices with L2 regularization for smooth training.

Abstract

A major challenge to the problem of community question answering is the lexical and semantic gap between the sentence representations. Some solutions to minimize this gap includes the introduction of extra parameters to deep models or augmenting the external handcrafted features. In this paper, we propose a novel attentive recurrent tensor network for solving the lexical and semantic gap in community question answering. We introduce token-level and phrase-level attention strategy that maps input sequences to the output using trainable parameters. Further, we use the tensor parameters to introduce a 3-way interaction between question, answer and external features in vector space. We introduce simplified tensor matrices with L2 regularization that results in smooth optimization during training. The proposed model achieves state-of-the-art performance on the task of answer sentence selection (TrecQA and WikiQA datasets) while outperforming the current state-of-the-art on the tasks of best answer selection (Yahoo! L4) and answer triggering task (WikiQA).