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

06/01/2019
by   Binbin Jin, et al.
0

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.

READ FULL TEXT

page 12

page 13

09/03/2019

Attention-based Pairwise Multi-Perspective Convolutional Neural Network for Answer Selection in Question Answering

Over the past few years, question answering and information retrieval sy...
01/21/2018

Attentive Recurrent Tensor Model for Community Question Answering

A major challenge to the problem of community question answering is the ...
03/05/2021

Graph-Based Tri-Attention Network for Answer Ranking in CQA

In community-based question answering (CQA) platforms, automatic answer ...
10/10/2017

Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering

In this paper, we propose a novel end-to-end neural architecture for ran...
11/22/2019

Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering

Community question answering (CQA) gains increasing popularity in both a...
12/17/2019

Knowledge-Enhanced Attentive Learning for Answer Selection in Community Question Answering Systems

In the community question answering (CQA) system, the answer selection t...
12/06/2019

Machine Translation Evaluation Meets Community Question Answering

We explore the applicability of machine translation evaluation (MTE) met...