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Advanced community question answering by leveraging external knowledge and multi-task learning

机译：通过利用外部知识和多任务学习来高级社区问答

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摘要

Community question answering (CQA) is an important but challenging task. Meantime, as the theory of deep learning develops, remarkable progress has been made by deep neural networks. This paper studies an advanced deep neural network that not only uses external knowledge to learn better representations of questions and answers but also improves representation learning by considering question categorization as an auxiliary task. Specifically, we propose a novel Multi-task and Knowledge enhanced Multi-head Interactive Attention network for Community Question Answering (MKMIA-CQA). It contains a document modeling module responsible for utilizing external commonsense knowledge to help identify background information (entity mentions and their relations) and filter out noise information from the long text which has complicated semantic and syntactic structures. Moreover, the model is trained in a multi-task manner. It regards community question answering as the primary task and question categorization as the auxiliary task, which aims to learn a category-aware encoder and improve the quality of locating the salient information of a long question. The experimental results on three widely used CQA datasets demonstrate that our model achieves impressive results compared to other strong competitors. (C) 2019 Elsevier B.V. All rights reserved.
机译：社区问答（CQA）是一项重要但具有挑战性的任务。同时，随着深度学习理论的发展，深度神经网络已经取得了令人瞩目的进步。本文研究了一种先进的深度神经网络，该网络不仅使用外部知识来学习问题和答案的更好表示，而且通过将问题分类作为辅助任务来改进表示学习。具体来说，我们提出了一种新颖的多任务和知识增强的多头互动注意力网络，用于社区问答（MKMIA-CQA）。它包含一个文档建模模块，该模块负责利用外部常识知识来帮助识别背景信息（实体提及及其关系），并从具有复杂语义和句法结构的长文本中过滤掉噪音信息。此外，以多任务方式训练模型。它以社区问题解答为主要任务，问题分类为辅助任务，旨在学习类别识别编码器并提高查找长问题的重要信息的质量。在三个广泛使用的CQA数据集上的实验结果表明，与其他强大的竞争对手相比，我们的模型取得了令人印象深刻的结果。 （C）2019 Elsevier B.V.保留所有权利。

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