What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA
What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA
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发表时间：
2007-06
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通讯作者：
Mengqiu Wang;Noah A. Smith;T. Mitamura
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作者：
Mengqiu Wang;Noah A. Smith;T. Mitamura
本文提出了一种语法驱动的问题回答方法，特别是简答题的答案-句子选择问题。我们没有使用句法特征来增强现有的统计分类器（就像在以前的工作中一样），而是基于这样的想法：问题及其（正确）答案通过松散但可预测的句法转换相互关联。我们提出了一种概率准同步语法，其灵感来自一种机器翻译语法（D. Smith和Eisner， 2006），并通过鲁棒的非词汇语法/对齐模型和（n个可选的）词汇语义驱动的对数线性模型的混合参数化。我们的模型在判别训练中将软对齐作为一个隐藏变量来学习。使用trecdataset的实验结果显示显着优于强大的最先进的基线。
This paper presents a syntax-driven approach to question answering, specifically the answer-sentence selection problem for short-answer questions. Rather than using syntactic features to augment existing statistical classifiers (as in previous work), we build on the idea that questions and their (correct) answers relate to each other via loose but predictable syntactic transformations. We propose a probabilistic quasi-synchronous grammar, inspired by one proposed for machine translation (D. Smith and Eisner, 2006), and parameterized by mixtures of a robust nonlexical syntax/alignment model with a(n optional) lexical-semantics-driven log-linear model. Our model learns soft alignments as a hidden variable in discriminative training. Experimentalresultsusing theTRECdataset are shown to significantly outperform strong state-of-the-art baselines.