基于双语信息的问题分类方法研究

问题分类是问答系统研究的一项基本任务。先前的研究仅仅是在单语语料上训练得到问题分类模型,存在语料不足和问题文本较短的问题。为了解决这些问题,该文提出了融合双语语料的双通道LSTM问题分类方法。首先,利用翻译语料分别扩充中文和英文语料;其次,将两种语言语料中的样本都分别用问题文本和翻译文本表示;最后,提出了双通道LSTM分类方法用于充分利用这两组特征,构建问题分类器。实验结果表明,该文提出的方法能有效提高问题分类的性能。

Research on Question Classification via Bilingual Information

Question classification is a basic task in question answering system. Previous studies only employ the monolingual corpus to train the question classification model, suffering from problems such as lack of corpus and short length of question text. To solve these problems, we propose a new approach named dual-channel LSTM model with bilingual information. Firstly, we extend the Chinese corpus and English corpus with the corresponding translated corpus. Secondly, the samples are represented by the question text and translation word vector. Finally, we build an question classifier using dual-channel LSTM model. The experimental result demonstrates that our approach improves the performance of question classification.