• MALSORI
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• v.68
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• pp.95-114
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• 2008

The recent growing popularity of statistical methods in machine translation requires much more large parallel corpora. A Korean-English parallel corpus, however, is not yet enoughly available, little research on this subject is being conducted. In this paper we present a hybrid method of aligning sentences for Korean-English parallel corpora. We use bilingual news wire web pages, reading comprehension materials for English learners, computer-related technical documents and help files of localized software for building a Korean-English parallel corpus. Our hybrid method combines sentence-length based and word-correspondence based methods. We show the results of experimentation and evaluate them. Alignment results from using a full translation model are very encouraging, especially when we apply alignment results to an SMT system: 0.66% for BLEU score and 9.94% for NIST score improvement compared to the previous method.

• ETRI Journal
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• v.45 no.1
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• pp.18-27
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• 2023

We present an English-Korean speech translation corpus, named EnKoST-C. End-to-end model training for speech translation tasks often suffers from a lack of parallel data, such as speech data in the source language and equivalent text data in the target language. Most available public speech translation corpora were developed for European languages, and there is currently no public corpus for English-Korean end-to-end speech translation. Thus, we created an EnKoST-C centered on TED Talks. In this process, we enhance the sentence alignment approach using the subtitle time information and bilingual sentence embedding information. As a result, we built a 559-h English-Korean speech translation corpus. The proposed sentence alignment approach showed excellent performance of 0.96 f-measure score. We also show the baseline performance of an English-Korean speech translation model trained with EnKoST-C. The EnKoST-C is freely available on a Korean government open data hub site.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.6
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• pp.433-442
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• 2013

A set of bilingual terms is one of the most important factors in building language-related applications such as a machine translation system and a cross-lingual information system. In this paper, we introduce a new approach that automatically extracts candidates of English-Korean bilingual terms by using a bilingual parallel corpus and a basic English-Korean lexicon. This approach can be useful even though the size of the parallel corpus is small. A sentence alignment is achieved first for the document-level parallel corpus. We can align words between a pair of aligned sentences by referencing a basic bilingual lexicon. For unaligned words between a pair of aligned sentences, several assumptions are applied in order to align bilingual term candidates of two languages. A location of a sentence, a relation between words, and linguistic information between two languages are examples of the assumptions. An experimental result shows approximately 71.7% accuracy for the English-Korean bilingual term candidates which are automatically extracted from 1,000 bilingual parallel corpus.

• Korean Journal of Cognitive Science
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• v.7 no.4
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• pp.179-202
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• 1996

• Annual Conference on Human and Language Technology
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• 2001.10d
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• pp.302-309
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• 2001

말뭉치를 통한 통계적인 자연 언어 처리에 관한 연구가 다국어 처리 분야에서도 활발히 진행되고 있는 가운데, 본 논문에서는 병렬 말뭉치 구축 및 활용의 기본이 되는 문장 정렬을 위한 효과적인 방법을 제안한다. 먼저, 기존의 문장 길이를 이용한 방법을 한-영 문장 정렬에 적용해 보고, 길이 정보만을 이용했을 때의 한계점을 지적한다. 그리고, 사전과 품사 대응 확률을 이용한 단어 정렬을 통하여, 길이 기반의 정렬 방식이 갖는 문제점을 보완할 수 있는 방법을 제시한다. 실험을 통하여 제안한 방법이 길이에 기반한 방법에 비하여 높은 성능을 나타냄을 알 수 있었다. 또한 한-영 문장 정렬에의 어휘 정보 활용에 있어서 문제가 될 수 있는 요소가 어떤 것들이 있는지 알아본다.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.285-292
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• 2007

The parallel corpus is an important resource in the research field of data-driven natural language processing, but there are only a few parallel corpora publicly available nowadays, mostly due to the high labor force needed to construct this kind of resource. A novel strategy is brought out to automatically fetch parallel text from the web in this paper, which may help to solve the problem of the lack of parallel corpora with high quality. The system we develop first downloads the web pages from certain hosts. Then candidate parallel page pairs are prepared from the page set based on the outer features of the web pages. The candidate page pairs are evaluated in the last step in which the sentences in the candidate web page pairs are extracted and aligned first, and then the similarity of the two web pages is evaluate based on the similarities of the aligned sentences. The experiments towards a multilingual web site show the satisfactory performance of the system.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.301-314
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• 2012

In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on the Multi-Class Support Vector Machine (MSVM) and the Hidden Markov Model (HMM) classifiers are presented. A feature vector is extracted from the text pair that is under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the Multi-Class Support Vector Machine and Hidden Markov Model. Another set of data was used for testing. The results of the MSVM and HMM outperform the results of the length based approach. Moreover these new approaches are valid for any language pairs and are quite flexible since the feature vector may contain less, more, or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.787-789
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• 2004

People frequently feel the need of a general searching tool that frees from language barrier when they find information through the internet. Therefore, it is necessary to have a multilingual parallel corpus to search with a word that includes a search keyword and has a corresponding word in another language, Multilingual parallel corpus can be built and reused effectively through the several processes which are judgment of the web documents, sentence alignment and word alignment. To build a multilingual parallel corpus, multi-lingual dictionary should be constructed in each language and HTML should be simplified. And by understanding the meaning and the statistics of document structure, judgment on translated web documents will be made and the searched web pages will be aligned in sentence unit.

• KSCI Review
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• v.14 no.1
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• pp.197-204
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• 2006

This paper proposes an similar sentence searching algorithm for the computer-aided translation. The Proposed algorithm, which is based on the Needleman-Wunsch algorithm, measures the similarity between the input sentence and the example sentences through combining surface. lemma, part-of-speech information of words with the multi-layered information. It also carries out the alignment between them. The accuracy of the proposed algorithm was very high in the experiment for the example sentences of the area of electricity and communication.

• ETRI Journal
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• v.43 no.5
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• pp.775-786
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• 2021

To translate in real time, a simultaneous translation system should determine when to stop reading source tokens and generate target tokens corresponding to a partial source sentence read up to that point. However, conventional attention-based neural machine translation (NMT) models cannot produce translations with adequate latency in online scenarios because they wait until a source sentence is completed to compute alignment between the source and target tokens. To address this issue, we propose a reinforced learning (RL)-based attention mechanism, the reinforced attention mechanism, which allows a neural translation model to jointly train the stopping criterion and a partial translation model. The proposed attention mechanism comprises two modules, one to ensure translation quality and the other to address latency. Different from previous RL-based simultaneous translation systems, which learn the stopping criterion from a fixed NMT model, the modules can be trained jointly with a novel reward function. In our experiments, the proposed model has better translation quality and comparable latency compared to previous models.