基于注意力的Bi-LSTM网络的端到端答案选择

Yuqi Ren, Tongxuan Zhang, Xikai Liu, Hongfei Lin
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引用次数: 4

摘要

许多人在网上询问医疗问题,从候选答案中找到最合适的答案是医疗保健的一个重要研究领域。IEEE HotICN知识图谱学术竞赛给出一个问题和几个候选答案,然后对候选答案进行排序以获得最佳答案。我们将此子任务作为一个二元分类任务,通过计算问题与每个答案之间的相似度来对答案进行排序。在这项工作中,我们提出了一个在训练数据集上训练的神经选择模型。我们的网络架构基于Bi-LSTM和注意力机制的结合,并扩展了生物医学词嵌入。基于这一事实,我们的模型在医学界的答案选择上取得了最先进的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
End-to-end Answer Selection via Attention-Based Bi-LSTM Network
Many people ask medical questions online, finding the most suitable answer from candidate answers is an important research area in health care. The IEEE HotICN Knowledge Graph Academic Competition given a question and several candidate answers, then sort the candidate answers to get the best answer. We treated this subtask as a binary classification task, sorted the answers by calculating similarity between the question and each answer. In this work, we proposed a neural selection model trained on the training dataset. Our network architecture is based on the combination of Bi-LSTM and Attention mechanism, extended with biomedical word embeddings. Based on this fact, our model achieve state-of-the-art results on answer selection of medical community.
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