Cross-language Question Answering Research Articles

Study on Tibetan-Chinese Comparable Corpus Extraction

Tibetan-Chinese comparable corpus extraction is a basis work for Tibetan-Chinese cross language question answering system, information retrieval, machine translation and other researches. This paper is an exploration to solve the scarcity of Tibetan-Chinese comparable corpus. It will promote the knowledge sharing between different languages. In this paper, we propose a method to extract Tibetan-Chinese comparable corpus. The main work is in the following: (1) Tibetan-Chinese comparable corpus extraction model based on multi-feature of bilingual websites. (2) Extraction method based on entity link from naturally annotated resources. Finally, the experimental results show our approach is effective.

Question answering at the cross-language evaluation forum 2003–2010

The paper offers an overview of the key issues raised during the 8 years' activity of the Multilingual Question Answering Track at the Cross Language Evaluation Forum (CLEF). The general aim of the track has been to test both monolingual and cross-language Question Answering (QA) systems that process queries and documents in several European languages, also drawing attention to a number of challenging issues for research in multilingual QA. The paper gives a brief description of how the task has evolved over the years and of the way in which the data sets have been created, presenting also a short summary of the different types of questions developed. The document collections adopted in the competitions are outlined as well, and data about participation is provided. Moreover, the main measures used to evaluate system performances are explained and an overall analysis of the results achieved is presented.

English-to-Japanese Cross-Language Question-Answering System using Weighted Adding with Multiple Answers (pp. 17-22)

We describe a method of using multiple documents with decreasing weights as evidence to improve the performance of a question-answering system. We also describe how it was used in cross-language question answering (CLQA) tasks. Sometimes, the answer to a question may be found in multiple documents. In such cases, using multiple documents for prediction generates better answers than using a single document. Therefore, our method uses information from multiple documents by adding the scores of candidate answers extracted from the various documents. Because simply adding scores degrades the performance of question-answering systems, we add scores with decreasing weights to reduce the negative effect of simply adding. We used this method in the CLQA part of NTCIR-5. It was incorporated into a commercially available translation system that carries out cross-language question-answering tasks. Our method obtained relatively good CLQA results. Index Terms—Machine translation, cross-language question-answering, decreased adding, multiple documents, NTCIR.

A Machine Learning Approach for an Indonesian-English Cross Language Question Answering System

We have built a CLQA (Cross Language Question Answering) system for a source language with limited data resources (e.g. Indonesian) using a machine learning approach. The CLQA system consists of four modules: question analyzer, keyword translator, passage retriever and answer finder. We used machine learning in two modules, the question classifier (part of the question analyzer) and the answer finder. In the question classifier, we classify the EAT (Expected Answer Type) of a question by using SVM (Support Vector Machine) method. Features for the classification module are basically the output of our shallow question parsing module. To improve the classification score, we use statistical information extracted from our Indonesian corpus. In the answer finder module, using an approach different from the common approach in which answer is located by matching the named entity of the word corpus with the EAT of question, we locate the answer by text chunking the word corpus. The features for the SVM based text chunking process consist of question features, word corpus features and similarity scores between the word corpus and the question keyword. In this way, we eliminate the named entity tagging process for the target document. As for the keyword translator module, we use an Indonesian-English dictionary to translate Indonesian keywords into English. We also use some simple patterns to transform some borrowed English words. The keywords are then combined in boolean queries in order to retrieve relevant passages using IDF scores. We first conducted an experiment using 2,837 questions (about 10% are used as the test data) obtained from 18 Indonesian college students. We next conducted a similar experiment using the NTCIR (NII Test Collection for IR Systems) 2005 CLQA task by translating the English questions into Indonesian. Compared to the Japanese-English and Chinese-English CLQA results in the NTCIR 2005, we found that our system is superior to others except for one system that uses a high data resource employing 3 dictionaries. Further, a rough comparison with two other Indonesian-English CLQA systems revealed that our system achieved higher accuracy score.

Interactive question answering: Is Cross-Language harder than monolingual searching?

Is Cross-Language answer finding harder than Monolingual answer finding for users? In this paper we provide initial quantitative and qualitative evidence to answer this question. In our study, which involves 16 users searching questions under four different system conditions, we find that interactive cross-language answer finding is not substantially harder (in terms of accuracy) than its monolingual counterpart, using general purpose Machine Translation systems and standard Information Retrieval machinery, although it takes more time. We have also seen that users need more context to provide accurate answers (full documents) than what is usually considered by systems (paragraphs or passages). Finally, we also discuss the limitations of standard evaluation methodologies for interactive Information Retrieval experiments in the case of cross-language question answering.