Deep Learning for Answer Sentence Selection
Lei Yu1 Karl Moritz Hermann2 Phil Blunsom12 Stephen Pulman1 1Department of Computer Science, University of Oxford 2Google DeepMind {lei.yu, phil.blunsom,
Abstract
Answer sentence selection is the task of identifying sentences that contain the answer to a given question. This is an important problem in its own right as well as in the larger context of open domain question answering. We propose a novel approach to solving this task via means of distributed representations, and learn to match questions with answers by considering their semantic encoding. This contrasts prior work on this task, which typically relies on classifiers with large numbers of hand-crafted syntactic and semantic features and various external resources. Our approach does not require any feature engineering nor does it involve specialist linguistic data, making this model easily applicable to a wide range of domains and languages. Experimental results on a standard benchmark dataset from TREC demonstrate that—despite its simplicity—our model matches state of the art performance on the answer sentence selection task.
中文速览
如何不依赖人工特征工程就能准确判断一个句子是否回答了某个问题，是开放域问答系统中的关键难题。研究者提出用分布式表示（distributed representations）来解决"答案句子选取"（answer sentence selection）任务：先用词向量将问句和候选答案句分别编码成向量，再通过一个学习到的变换矩阵在共享向量空间中度量两者的语义相似度，底层句子编码支持词袋模型和基于卷积神经网络（CNN）的二元组模型两种方式。在TREC问答测试集上的实验表明，这一简洁的方法达到了当时的最优水平，却无需任何句法分析树、WordNet等外部语言资源，也无需手工设计特征。这项工作的意义在于，它证明了轻量级神经语义模型可以替代复杂的特征工程体系，且天然适用于多语言和多领域场景，为后续深度学习在问答任务上的广泛应用奠定了基础。
原文 arXiv:1412.1632;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1412.1632v1