Chao-Hong Liu


2020

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Multiple Segmentations of Thai Sentences for Neural Machine Translation
Alberto Poncelas | Wichaya Pidchamook | Chao-Hong Liu | James Hadley | Andy Way
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality. In addition, the Thai script does not use white spaces to delimit the boundaries between words, which adds more complexity when building sequence to sequence models. In this work, we explore how to augment a set of English–Thai parallel data by replicating sentence-pairs with different word segmentation methods on Thai, as training data for NMT model training. Using different merge operations of Byte Pair Encoding, different segmentations of Thai sentences can be obtained. The experiments show that combining these datasets, performance is improved for NMT models trained with a dataset that has been split using a supervised splitting tool.

2019

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Pivot Machine Translation in INTERACT Project
Chao-Hong Liu | Andy Way | Catarina Silva | André Martins
Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks

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Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages
Alina Karakanta | Atul Kr. Ojha | Chao-Hong Liu | Jonathan Washington | Nathaniel Oco | Surafel Melaku Lakew | Valentin Malykh | Xiaobing Zhao
Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages

2018

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Chinese-Portuguese Machine Translation: A Study on Building Parallel Corpora from Comparable Texts
Siyou Liu | Longyue Wang | Chao-Hong Liu
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Proceedings of the AMTA 2018 Workshop on Technologies for MT of Low Resource Languages (LoResMT 2018)
Chao-Hong Liu
Proceedings of the AMTA 2018 Workshop on Technologies for MT of Low Resource Languages (LoResMT 2018)

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Extracting In-domain Training Corpora for Neural Machine Translation Using Data Selection Methods
Catarina Cruz Silva | Chao-Hong Liu | Alberto Poncelas | Andy Way
Proceedings of the Third Conference on Machine Translation: Research Papers

Data selection is a process used in selecting a subset of parallel data for the training of machine translation (MT) systems, so that 1) resources for training might be reduced, 2) trained models could perform better than those trained with the whole corpus, and/or 3) trained models are more tailored to specific domains. It has been shown that for statistical MT (SMT), the use of data selection helps improve the MT performance significantly. In this study, we reviewed three data selection approaches for MT, namely Term Frequency– Inverse Document Frequency, Cross-Entropy Difference and Feature Decay Algorithm, and conducted experiments on Neural Machine Translation (NMT) with the selected data using the three approaches. The results showed that for NMT systems, using data selection also improved the performance, though the gain is not as much as for SMT systems.

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The RGNLP Machine Translation Systems for WAT 2018
Atul Kr. Ojha | Koel Dutta Chowdhury | Chao-Hong Liu | Karan Saxena
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation

2017

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Proceedings of the IJCNLP 2017, Shared Tasks
Chao-Hong Liu | Preslav Nakov | Nianwen Xue
Proceedings of the IJCNLP 2017, Shared Tasks

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IJCNLP-2017 Task 4: Customer Feedback Analysis
Chao-Hong Liu | Yasufumi Moriya | Alberto Poncelas | Declan Groves
Proceedings of the IJCNLP 2017, Shared Tasks

This document introduces the IJCNLP 2017 Shared Task on Customer Feedback Analysis. In this shared task we have prepared corpora of customer feedback in four languages, i.e. English, French, Spanish and Japanese. They were annotated in a common meanings categorization, which was improved from an ADAPT-Microsoft pivot study on customer feedback. Twenty teams participated in the shared task and twelve of them have submitted prediction results. The results show that performance of prediction meanings of customer feedback is reasonable well in four languages. Nine system description papers are archived in the shared tasks proceeding.

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Ethical Considerations in NLP Shared Tasks
Carla Parra Escartín | Wessel Reijers | Teresa Lynn | Joss Moorkens | Andy Way | Chao-Hong Liu
Proceedings of the First ACL Workshop on Ethics in Natural Language Processing

Shared tasks are increasingly common in our field, and new challenges are suggested at almost every conference and workshop. However, as this has become an established way of pushing research forward, it is important to discuss how we researchers organise and participate in shared tasks, and make that information available to the community to allow further research improvements. In this paper, we present a number of ethical issues along with other areas of concern that are related to the competitive nature of shared tasks. As such issues could potentially impact on research ethics in the Natural Language Processing community, we also propose the development of a framework for the organisation of and participation in shared tasks that can help mitigate against these issues arising.

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Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora
Haithem Afli | Chao-Hong Liu
Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora

2016

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The ADAPT Bilingual Document Alignment system at WMT16
Pintu Lohar | Haithem Afli | Chao-Hong Liu | Andy Way
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

2013

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Candidate Scoring Using Web-Based Measure for Chinese Spelling Error Correction
Liang-Chih Yu | Chao-Hong Liu | Chung-Hsien Wu
Proceedings of the Seventh SIGHAN Workshop on Chinese Language Processing