Promotion of Answer Value Measurement with Domain Effects in Community Question Answering Systems

TL;DR

This paper introduces EARNN, a neural network model that improves answer selection and ranking in community Q&A by incorporating semantic, topical, and timeliness domain effects, validated on Quora data.

Contribution

The paper presents a unified neural model that explicitly models multi-facet domain effects and timeliness for answer ranking in CQA, enhancing interpretability and effectiveness.

Findings

EARNN outperforms existing models on Quora dataset.

Incorporating domain effects improves answer relevance.

Time-sensitive ranking enhances answer timeliness.

Abstract

In the area of community question answering (CQA), answer selection and answer ranking are two tasks which are applied to help users quickly access valuable answers. Existing solutions mainly exploit the syntactic or semantic correlation between a question and its related answers (Q&A), where the multi-facet domain effects in CQA are still underexplored. In this paper, we propose a unified model, Enhanced Attentive Recurrent Neural Network (EARNN), for both answer selection and answer ranking tasks by taking full advantages of both Q&A semantics and multi-facet domain effects (i.e., topic effects and timeliness). Specifically, we develop a serialized LSTM to learn the unified representations of Q&A, where two attention mechanisms at either sentence-level or word-level are designed for capturing the deep effects of topics. Meanwhile, the emphasis of Q&A can be automatically distinguished. Furthermore, we design a time-sensitive ranking function to model the timeliness in CQA. To effectively train EARNN, a question-dependent pairwise learning strategy is also developed. Finally, we conduct extensive experiments on a real-world dataset from Quora. Experimental results validate the effectiveness and interpretability of our proposed EARNN model.