Chenchen Ding


2020

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A Three-Parameter Rank-Frequency Relation in Natural Languages
Chenchen Ding | Masao Utiyama | Eiichiro Sumita
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We present that, the rank-frequency relation in textual data follows f ∝ r-𝛼(r+𝛾)-𝛽, where f is the token frequency and r is the rank by frequency, with (𝛼, 𝛽, 𝛾) as parameters. The formulation is derived based on the empirical observation that d2 (x+y)/dx2 is a typical impulse function, where (x,y)=(log r, log f). The formulation is the power law when 𝛽=0 and the Zipf–Mandelbrot law when 𝛼=0. We illustrate that 𝛼 is related to the analytic features of syntax and 𝛽+𝛾 to those of morphology in natural languages from an investigation of multilingual corpora.

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A Myanmar (Burmese)-English Named Entity Transliteration Dictionary
Aye Myat Mon | Chenchen Ding | Hour Kaing | Khin Mar Soe | Masao Utiyama | Eiichiro Sumita
Proceedings of the 12th Language Resources and Evaluation Conference

Transliteration is generally a phonetically based transcription across different writing systems. It is a crucial task for various downstream natural language processing applications. For the Myanmar (Burmese) language, robust automatic transliteration for borrowed English words is a challenging task because of the complex Myanmar writing system and the lack of data. In this study, we constructed a Myanmar-English named entity dictionary containing more than eighty thousand transliteration instances. The data have been released under a CC BY-NC-SA license. We evaluated the automatic transliteration performance using statistical and neural network-based approaches based on the prepared data. The neural network model outperformed the statistical model significantly in terms of the BLEU score on the character level. Different units used in the Myanmar script for processing were also compared and discussed.

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Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation
Abhisek Chakrabarty | Raj Dabre | Chenchen Ding | Masao Utiyama | Eiichiro Sumita
Proceedings of the 28th International Conference on Computational Linguistics

In this study, linguistic knowledge at different levels are incorporated into the neural machine translation (NMT) framework to improve translation quality for language pairs with extremely limited data. Integrating manually designed or automatically extracted features into the NMT framework is known to be beneficial. However, this study emphasizes that the relevance of the features is crucial to the performance. Specifically, we propose two methods, 1) self relevance and 2) word-based relevance, to improve the representation of features for NMT. Experiments are conducted on translation tasks from English to eight Asian languages, with no more than twenty thousand sentences for training. The proposed methods improve translation quality for all tasks by up to 3.09 BLEU points. Discussions with visualization provide the explainability of the proposed methods where we show that the relevance methods provide weights to features thereby enhancing their impact on low-resource machine translation.

2019

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MY-AKKHARA: A Romanization-based Burmese (Myanmar) Input Method
Chenchen Ding | Masao Utiyama | Eiichiro Sumita
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

MY-AKKHARA is a method used to input Burmese texts encoded in the Unicode standard, based on commonly accepted Latin transcription. By using this method, arbitrary Burmese strings can be accurately inputted with 26 lowercase Latin letters. Meanwhile, the 26 uppercase Latin letters are designed as shortcuts of lowercase letter sequences. The frequency of Burmese characters is considered in MY-AKKHARA to realize an efficient keystroke distribution on a QWERTY keyboard. Given that the Unicode standard has not been extensively used in digitization of Burmese, we hope that MY-AKKHARA can contribute to the widespread use of Unicode in Myanmar and can provide a platform for smart input methods for Burmese in the future. An implementation of MY-AKKHARA running in Windows is released at http://www2.nict.go.jp/astrec-att/member/ding/my-akkhara.html

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Proceedings of the 6th Workshop on Asian Translation
Toshiaki Nakazawa | Chenchen Ding | Raj Dabre | Anoop Kunchukuttan | Nobushige Doi | Yusuke Oda | Ondřej Bojar | Shantipriya Parida | Isao Goto | Hidaya Mino
Proceedings of the 6th Workshop on Asian Translation

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Overview of the 6th Workshop on Asian Translation
Toshiaki Nakazawa | Nobushige Doi | Shohei Higashiyama | Chenchen Ding | Raj Dabre | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Yusuke Oda | Shantipriya Parida | Ondřej Bojar | Sadao Kurohashi
Proceedings of the 6th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 6th workshop on Asian translation (WAT2019) including Ja↔En, Ja↔Zh scientific paper translation subtasks, Ja↔En, Ja↔Ko, Ja↔En patent translation subtasks, Hi↔En, My↔En, Km↔En, Ta↔En mixed domain subtasks and Ru↔Ja news commentary translation task. For the WAT2019, 25 teams participated in the shared tasks. We also received 10 research paper submissions out of which 61 were accepted. About 400 translation results were submitted to the automatic evaluation server, and selected submis- sions were manually evaluated.

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Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English
Benjamin Marie | Hour Kaing | Aye Myat Mon | Chenchen Ding | Atsushi Fujita | Masao Utiyama | Eiichiro Sumita
Proceedings of the 6th Workshop on Asian Translation

This paper presents the NICT’s supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks. For all the translation directions, we built state-of-the-art supervised neural (NMT) and statistical (SMT) machine translation systems, using monolingual data cleaned and normalized. Our combination of NMT and SMT performed among the best systems for the four translation directions. We also investigated the feasibility of unsupervised machine translation for low-resource and distant language pairs and confirmed observations of previous work showing that unsupervised MT is still largely unable to deal with them.

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English-Myanmar Supervised and Unsupervised NMT: NICT’s Machine Translation Systems at WAT-2019
Rui Wang | Haipeng Sun | Kehai Chen | Chenchen Ding | Masao Utiyama | Eiichiro Sumita
Proceedings of the 6th Workshop on Asian Translation

This paper presents the NICT’s participation (team ID: NICT) in the 6th Workshop on Asian Translation (WAT-2019) shared translation task, specifically Myanmar (Burmese) - English task in both translation directions. We built neural machine translation (NMT) systems for these tasks. Our NMT systems were trained with language model pretraining. Back-translation technology is adopted to NMT. Our NMT systems rank the third in English-to-Myanmar and the second in Myanmar-to-English according to BLEU score.

2018

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Simplified Abugidas
Chenchen Ding | Masao Utiyama | Eiichiro Sumita
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

An abugida is a writing system where the consonant letters represent syllables with a default vowel and other vowels are denoted by diacritics. We investigate the feasibility of recovering the original text written in an abugida after omitting subordinate diacritics and merging consonant letters with similar phonetic values. This is crucial for developing more efficient input methods by reducing the complexity in abugidas. Four abugidas in the southern Brahmic family, i.e., Thai, Burmese, Khmer, and Lao, were studied using a newswire 20,000-sentence dataset. We compared the recovery performance of a support vector machine and an LSTM-based recurrent neural network, finding that the abugida graphemes could be recovered with 94% - 97% accuracy at the top-1 level and 98% - 99% at the top-4 level, even after omitting most diacritics (10 - 30 types) and merging the remaining 30 - 50 characters into 21 graphemes.

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Overview of the 5th Workshop on Asian Translation
Toshiaki Nakazawa | Katsuhito Sudoh | Shohei Higashiyama | Chenchen Ding | Raj Dabre | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Sadao Kurohashi
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation

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English-Myanmar NMT and SMT with Pre-ordering: NICT’s Machine Translation Systems at WAT-2018
Rui Wang | Chenchen Ding | Masao Utiyama | Eiichiro Sumita
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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Overview of the 4th Workshop on Asian Translation
Toshiaki Nakazawa | Shohei Higashiyama | Chenchen Ding | Hideya Mino | Isao Goto | Hideto Kazawa | Yusuke Oda | Graham Neubig | Sadao Kurohashi
Proceedings of the 4th Workshop on Asian Translation (WAT2017)

This paper presents the results of the shared tasks from the 4th workshop on Asian translation (WAT2017) including J↔E, J↔C scientific paper translation subtasks, C↔J, K↔J, E↔J patent translation subtasks, H↔E mixed domain subtasks, J↔E newswire subtasks and J↔E recipe subtasks. For the WAT2017, 12 institutions participated in the shared tasks. About 300 translation results have been submitted to the automatic evaluation server, and selected submissions were manually evaluated.

2016

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Proceedings of the 3rd Workshop on Asian Translation (WAT2016)
Toshiaki Nakazawa | Hideya Mino | Chenchen Ding | Isao Goto | Graham Neubig | Sadao Kurohashi | Ir. Hammam Riza | Pushpak Bhattacharyya
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

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Overview of the 3rd Workshop on Asian Translation
Toshiaki Nakazawa | Chenchen Ding | Hideya Mino | Isao Goto | Graham Neubig | Sadao Kurohashi
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

This paper presents the results of the shared tasks from the 3rd workshop on Asian translation (WAT2016) including J ↔ E, J ↔ C scientific paper translation subtasks, C ↔ J, K ↔ J, E ↔ J patent translation subtasks, I ↔ E newswire subtasks and H ↔ E, H ↔ J mixed domain subtasks. For the WAT2016, 15 institutions participated in the shared tasks. About 500 translation results have been submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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Similar Southeast Asian Languages: Corpus-Based Case Study on Thai-Laotian and Malay-Indonesian
Chenchen Ding | Masao Utiyama | Eiichiro Sumita
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

This paper illustrates the similarity between Thai and Laotian, and between Malay and Indonesian, based on an investigation on raw parallel data from Asian Language Treebank. The cross-lingual similarity is investigated and demonstrated on metrics of correspondence and order of tokens, based on several standard statistical machine translation techniques. The similarity shown in this study suggests a possibility on harmonious annotation and processing of the language pairs in future development.

2015

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Improving fast_align by Reordering
Chenchen Ding | Masao Utiyama | Eiichiro Sumita
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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NICT at WAT 2015
Chenchen Ding | Masao Utiyama | Eiichiro Sumita
Proceedings of the 2nd Workshop on Asian Translation (WAT2015)

2014

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Dependency Tree Abstraction for Long-Distance Reordering in Statistical Machine Translation
Chenchen Ding | Yuki Arase
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Word Order Does NOT Differ Significantly Between Chinese and Japanese
Chenchen Ding | Masao Utiyama | Eiichiro Sumita | Mikio Yamamoto
Proceedings of the 1st Workshop on Asian Translation (WAT2014)

2013

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An Unsupervised Parameter Estimation Algorithm for a Generative Dependency N-gram Language Model
Chenchen Ding | Mikio Yamamoto
Proceedings of the Sixth International Joint Conference on Natural Language Processing