Minh Quang Nhat Pham

Using Machine Translation for Recognizing Textual Entailment in Vietnamese Language

Recognizing Textual Entailment (RTE) is a fundamental task in Natural Language Understanding. The task is to determine whether the meaning of a text can be inferred from the meaning of the other one. This paper explores the use of Machine Translation (MT) in recognizing textual entailment in texts written in Vietnamese. We present two methods of using Machine Translation for Vietnamese RTE. The first method integrates a MT component into front-end of an English RTE system. The second method uses a MT component to produce English translation of Vietnamese RTE data, and both original Vietnamese data and its translation are used to learn an entailment classifier. Experimental results achieve on Vietnamese RTE corpus built from RTE3 data set suggest that Machine Translation can help to improve Vietnamese RTE.

A learning-to-rank method for information updating task

Our paper addresses the information updating task which is to determine the most appropriate location in an existing document to place a new piece of related information. We propose a new learning-to-rank method for the information updating task. The updating task is formalized as a learning-to-rank problem, and in training, a heuristic method of automatically assigning labels for training examples is proposed to exploit structural information of documents. With the proposed formulation, state-of-the-art learning-to-rank algorithms can be applied to the task. We deal with the problem of the lack of semantic information by incorporating semantic features derived from word clusters to further improve the performance of information updating. The proposed method is applied in updating Wikipedia biographical articles and Legal documents. Experimental results achieved on both Wikipedia biographical data set and Legal data set showed that our proposed learning-to-rank method with cluster-based features outperforms previously reported methods for information updating task.

Learning to Recognize Textual Entailment in Japanese Texts with the Utilization of Machine Translation

Recognizing Textual Entailment (RTE) is a fundamental task in Natural Language Understanding. The task is to decide whether the meaning of a text can be inferred from the meaning of another one. In this article, we conduct an empirical study of recognizing textual entailment in Japanese texts, in which we adopt a machine learning-based approach to the task. We quantitatively analyze the effects of various entailment features, machine learning algorithms, and the impact of RTE resources on the performance of an RTE system. This article also investigates the use of machine translation for the RTE task and determines whether machine translation can be used to improve the performance of our RTE system. Experimental results achieved on benchmark data sets show that our machine learning-based RTE system outperforms the baseline methods based on lexical matching and syntactic matching. The results also suggest that the machine translation component can be utilized to improve the performance of the RTE system.