Papers by Chenchen Ding

A Three-Parameter Rank-Frequency Relation in Natural Languages (2020.acl-main)

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Chenchen Ding, Masao Utiyama, Eiichiro Sumita

Challenge:	Existing empirical law to form rank-frequency relation in textual data is Zipf's/power law .
Approach:	They propose a rank-frequency relation that follows f r-(r+)- in textual data.
Outcome:	The proposed formulation is the power law when =0 and the Zipf–Mandelbrot law when=1 .

MY-AKKHARA: A Romanization-based Burmese (Myanmar) Input Method (D19-3)

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Chenchen Ding, Masao Utiyama, Eiichiro Sumita

Challenge:	MY-AKKHARA is a method used to input Burmese texts encoded in the Unicode standard, based on commonly accepted Latin transcription.
Approach:	They propose a method to input Burmese texts encoded in the Unicode standard based on 26 lowercase Latin letters and 26 uppercase Latin keys as shortcuts for lowercase letters.
Outcome:	The proposed method can input arbitrary Burmese strings with 26 lowercase Latin letters and 26 uppercase Latin characters on a QWERTY keyboard.

FeatureBART: Feature Based Sequence-to-Sequence Pre-Training for Low-Resource NMT (2022.coling-1)

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Abhisek Chakrabarty, Raj Dabre, Chenchen Ding, Hideki Tanaka, Masao Utiyama, Eiichiro Sumita

Multilinguality

Challenge:	FeatureBART is a linguistically motivated sequence-to-sequence monolingual pre-training strategy . syntactic features such as lemma, part-of-speech and dependency labels are incorporated into the pre-trained model .
Approach:	They propose a linguistically motivated sequence-to-sequence monolingual pre-training strategy that incorporates syntactic features into the framework.
Outcome:	The proposed model improves translation quality in bilingual and multilingual settings over models that do not use features.

SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training (2022.coling-1)

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Dan Qiao, Chenchen Dai, Yuyang Ding, Juntao Li, Qiang Chen, Wenliang Chen, Min Zhang

Information Extraction & Text Mining

Challenge:	Existing methods to handle label noise in text classification tasks are limited to visual data.
Approach:	They propose a method to handle label noise in text classification tasks using a Gaussian Mixture Model.
Outcome:	The proposed method outperforms baselines on three types of text classification tasks on visual and textual data.

Registering Source Tokens to Target Language Spaces in Multilingual Neural Machine Translation (2025.acl-long)

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Zhi Qu, Yiran Wang, Jiannan Mao, Chenchen Ding, Hideki Tanaka, Masao Utiyama, Taro Watanabe

Multilinguality

Challenge:	Multilingual neural machine translation (MNMT) aims for arbitrary translations across multiple languages.
Approach:	They propose a method that inserts a set of tokens specifying the target language into the input sequence between the source and target tokens.
Outcome:	The proposed method outperforms existing models on a large-scale benchmark.

Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English (D19-52)

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Benjamin Marie, Hour Kaing, Aye Myat Mon, Chenchen Ding, Atsushi Fujita, Masao Utiyama, Eiichiro Sumita

Text Generation

Challenge:	Using cleaned and normalized noisy monolingual data, supervised neural and statistical machine translation systems performed among the best for the four translation directions.
Approach:	They present supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks.
Outcome:	The proposed systems performed among the best for the four translation directions.

Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation (2020.coling-main)

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Abhisek Chakrabarty, Raj Dabre, Chenchen Ding, Masao Utiyama, Eiichiro Sumita

Text Generation

Challenge:	Existing studies on incorporating arbitrary syntactic information into neural machine translation (NMT) are lacking.
Approach:	They propose to integrate linguistic knowledge at different levels into neural machine translation framework to improve translation quality for language pairs with extremely limited data.
Outcome:	The proposed methods improve translation quality for all tasks by 3.09 BLEU points . the proposed methods are based on two different approaches .

English-Myanmar Supervised and Unsupervised NMT: NICT’s Machine Translation Systems at WAT-2019 (D19-52)

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Rui Wang, Haipeng Sun, Kehai Chen, Chenchen Ding, Masao Utiyama, Eiichiro Sumita

Text Generation

Challenge:	NICT participated in the 6th Workshop on Asian Translation (WAT-2019) shared translation task, specifically Myanmar (My) - English task in both translation directions.
Approach:	They present the participation of the NICT in the 6th Workshop on Asian Translation (WAT-2019) shared translation task, specifically Myanmar (Burmese) - English task in both translation directions.
Outcome:	The proposed systems perform the third in English-to-Myanmar and the second in Myanmar-to English according to BLEU score.

A Myanmar (Burmese)-English Named Entity Transliteration Dictionary (2020.lrec-1)

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Aye Myat Mon, Chenchen Ding, Hour Kaing, Khin Mar Soe, Masao Utiyama, Eiichiro Sumita

Challenge:	Currently, there are no data available for the transcription of borrowed English words in Myanmar . lack of resources is a problem for many understudied languages .
Approach:	They construct a dictionary of Myanmar-English transliteration instances using a CC BY-NC-SA license.
Outcome:	The proposed model outperforms the statistical model significantly on the character level.

Overview of the 6th Workshop on Asian Translation (D19-52)

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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

Multilinguality

Challenge:	The 6th workshop on Asian translation (WAT2019) was held in hong kong, hongkong, and hong kong.
Approach:	They present the results of the shared tasks from the 6th workshop on Asian translation (WAT2019) 25 teams participated in the shared task and 10 research paper submissions were accepted .
Outcome:	The results of the 6th workshop on Asian translation (WAT2019) include JaEn, JaZh scientific paper translation subtasks, Ja'En, ja'Ko, Ja’En patent translation sub tasks, Hi'En and My'En patent subtask and Ru'Ja news commentary translation task.

Robust Neural Machine Translation for Abugidas by Glyph Perturbation (2024.eacl-short)

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Hour Kaing, Chenchen Ding, Hideki Tanaka, Masao Utiyama

Responsible & Trustworthy NLP

Challenge:	Neural machine translation systems are vulnerable when trained on limited data.
Approach:	They propose to add noise to the training phase to increase robustness of NMT systems trained on limited data.
Outcome:	The proposed training strategy overcomes noise and improves robustness for low-resource tasks for abugida glyphs.

Simplified Abugidas (P18-2)

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Chenchen Ding, Masao Utiyama, Eiichiro Sumita

Challenge:	Abugidas are writing systems where consonant letters represent syllables with a default vowel and other vowels are denoted by diacritics.
Approach:	They investigated the feasibility of recovering the original text written in an abugida after omitting subordinate diacritics and merging consonant letters with similar phonetic values.
Outcome:	The proposed method recovers the original text written in an abugida with 94% - 97% accuracy at the top-1 level and 98% - 99% at the bottom-4 level even after omitting most diacritics and merging the remaining 30 - 50 characters into 21 graphemes.