Toshiaki Nakazawa

2024

pdf bib
Proceedings of the Eleventh Workshop on Asian Translation (WAT 2024)
Toshiaki Nakazawa | Isao Goto
Proceedings of the Eleventh Workshop on Asian Translation (WAT 2024)

pdf abs
Revisiting Context Choices for Context-aware Machine Translation
Matiss Rikters | Toshiaki Nakazawa
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

One of the most popular methods for context-aware machine translation (MT) is to use separate encoders for the source sentence and context as multiple sources for one target sentence. Recent work has cast doubt on whether these models actually learn useful signals from the context or are improvements in automatic evaluation metrics just a side-effect. We show that multi-source transformer models improve MT over standard transformer-base models even with empty lines provided as context, but the translation quality improves significantly (1.51 - 2.65 BLEU) when a sufficient amount of correct context is provided. We also show that even though randomly shuffling in-domain context can also improve over baselines, the correct context further improves translation quality and random out-of-domain context further degrades it.

2023

This paper presents the results of the shared tasks from the 10th workshop on Asian translation (WAT2023). For the WAT2023, 2 teams submitted their translation results for the human evaluation. We also accepted 1 research paper. About 40 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

This paper presents the results of the General Machine Translation Task organised as part of the 2023 Conference on Machine Translation (WMT). In the general MT task, participants were asked to build machine translation systems for any of 8 language pairs (corresponding to 14 translation directions), to be evaluated on test sets consisting of up to four different domains. We evaluate system outputs with professional human annotators using a combination of source-based Direct Assessment and scalar quality metric (DA+SQM).

2022

This paper presents the results of the shared tasks from the 9th workshop on Asian translation (WAT2022). For the WAT2022, 8 teams submitted their translation results for the human evaluation. We also accepted 4 research papers. About 300 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

This paper presents the results of the General Machine Translation Task organised as part of the Conference on Machine Translation (WMT) 2022. In the general MT task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting of four different domains. We evaluate system outputs with human annotators using two different techniques: reference-based direct assessment and (DA) and a combination of DA and scalar quality metric (DA+SQM).

2021

pdf abs
Modeling Target-side Inflection in Placeholder Translation
Ryokan Ri | Toshiaki Nakazawa | Yoshimasa Tsuruoka
Proceedings of Machine Translation Summit XVIII: Research Track

Placeholder translation systems enable the users to specify how a specific phrase is translated in the output sentence. The system is trained to output special placeholder tokens and the user-specified term is injected into the output through the context-free replacement of the placeholder token. However and this approach could result in ungrammatical sentences because it is often the case that the specified term needs to be inflected according to the context of the output and which is unknown before the translation. To address this problem and we propose a novel method of placeholder translation that can inflect specified terms according to the grammatical construction of the output sentence. We extend the seq2seq architecture with a character-level decoder that takes the lemma of a user-specified term and the words generated from the word-level decoder to output a correct inflected form of the lemma. We evaluate our approach with a Japanese-to-English translation task in the scientific writing domain and and show our model can incorporate specified terms in a correct form more successfully than other comparable models.

This paper presents the results of the shared tasks from the 8th workshop on Asian translation (WAT2021). For the WAT2021, 28 teams participated in the shared tasks and 24 teams submitted their translation results for the human evaluation. We also accepted 5 research papers. About 2,100 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

pdf abs
Zero-pronoun Data Augmentation for Japanese-to-English Translation
Ryokan Ri | Toshiaki Nakazawa | Yoshimasa Tsuruoka
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

For Japanese-to-English translation, zero pronouns in Japanese pose a challenge, since the model needs to infer and produce the corresponding pronoun in the target side of the English sentence. However, although fully resolving zero pronouns often needs discourse context, in some cases, the local context within a sentence gives clues to the inference of the zero pronoun. In this study, we propose a data augmentation method that provides additional training signals for the translation model to learn correlations between local context and zero pronouns. We show that the proposed method significantly improves the accuracy of zero pronoun translation with machine translation experiments in the conversational domain.

This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021.In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories. The taskwas also opened up to additional test suites toprobe specific aspects of translation.

2020

This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.

pdf abs
Document-aligned Japanese-English Conversation Parallel Corpus
Matīss Rikters | Ryokan Ri | Tong Li | Toshiaki Nakazawa
Proceedings of the Fifth Conference on Machine Translation

Sentence-level (SL) machine translation (MT) has reached acceptable quality for many high-resourced languages, but not document-level (DL) MT, which is difficult to 1) train with little amount of DL data; and 2) evaluate, as the main methods and data sets focus on SL evaluation. To address the first issue, we present a document-aligned Japanese-English conversation corpus, including balanced, high-quality business conversation data for tuning and testing. As for the second issue, we manually identify the main areas where SL MT fails to produce adequate translations in lack of context. We then create an evaluation set where these phenomena are annotated to alleviate automatic evaluation of DL systems. We train MT models using our corpus to demonstrate how using context leads to improvements.

pdf abs
Evaluation Dataset for Zero Pronoun in Japanese to English Translation
Sho Shimazu | Sho Takase | Toshiaki Nakazawa | Naoaki Okazaki
Proceedings of the Twelfth Language Resources and Evaluation Conference

In natural language, we often omit some words that are easily understandable from the context. In particular, pronouns of subject, object, and possessive cases are often omitted in Japanese; these are known as zero pronouns. In translation from Japanese to other languages, we need to find a correct antecedent for each zero pronoun to generate a correct and coherent translation. However, it is difficult for conventional automatic evaluation metrics (e.g., BLEU) to focus on the success of zero pronoun resolution. Therefore, we present a hand-crafted dataset to evaluate whether translation models can resolve the zero pronoun problems in Japanese to English translations. We manually and statistically validate that our dataset can effectively evaluate the correctness of the antecedents selected in translations. Through the translation experiments using our dataset, we reveal shortcomings of an existing context-aware neural machine translation model.

pdf abs
TDDC: Timely Disclosure Documents Corpus
Nobushige Doi | Yusuke Oda | Toshiaki Nakazawa
Proceedings of the Twelfth Language Resources and Evaluation Conference

In this paper, we describe the details of the Timely Disclosure Documents Corpus (TDDC). TDDC was prepared by manually aligning the sentences from past Japanese and English timely disclosure documents in PDF format published by companies listed on the Tokyo Stock Exchange. TDDC consists of approximately 1.4 million parallel sentences in Japanese and English. TDDC was used as the official dataset for the 6th Workshop on Asian Translation to encourage the development of machine translation.

This paper presents the results of the shared tasks from the 7th workshop on Asian translation (WAT2020). For the WAT2020, 20 teams participated in the shared tasks and 14 teams submitted their translation results for the human evaluation. We also received 12 research paper submissions out of which 7 were accepted. About 500 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

pdf abs
The University of Tokyo’s Submissions to the WAT 2020 Shared Task
Matīss Rikters | Toshiaki Nakazawa | Ryokan Ri
Proceedings of the 7th Workshop on Asian Translation

The paper describes the development process of the The University of Tokyo’s NMT systems that were submitted to the WAT 2020 Document-level Business Scene Dialogue Translation sub-task. We describe the data processing workflow, NMT system training architectures, and automatic evaluation results. For the WAT 2020 shared task, we submitted 12 systems (both constrained and unconstrained) for English-Japanese and Japanese-English translation directions. The submitted systems were trained using Transformer models and one was a SMT baseline.

2019

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.

pdf abs
Designing the Business Conversation Corpus
Matīss Rikters | Ryokan Ri | Tong Li | Toshiaki Nakazawa
Proceedings of the 6th Workshop on Asian Translation

While the progress of machine translation of written text has come far in the past several years thanks to the increasing availability of parallel corpora and corpora-based training technologies, automatic translation of spoken text and dialogues remains challenging even for modern systems. In this paper, we aim to boost the machine translation quality of conversational texts by introducing a newly constructed Japanese-English business conversation parallel corpus. A detailed analysis of the corpus is provided along with challenging examples for automatic translation. We also experiment with adding the corpus in a machine translation training scenario and show how the resulting system benefits from its use.

2018

2017

pdf abs
Neural Machine Translation: Basics, Practical Aspects and Recent Trends
Fabien Cromieres | Toshiaki Nakazawa | Raj Dabre
Proceedings of the IJCNLP 2017, Tutorial Abstracts

Machine Translation (MT) is a sub-field of NLP which has experienced a number of paradigm shifts since its inception. Up until 2014, Phrase Based Statistical Machine Translation (PBSMT) approaches used to be the state of the art. In late 2014, Neural Machine Translation (NMT) was introduced and was proven to outperform all PBSMT approaches by a significant margin. Since then, the NMT approaches have undergone several transformations which have pushed the state of the art even further. This tutorial is primarily aimed at researchers who are either interested in or are fairly new to the world of NMT and want to obtain a deep understanding of NMT fundamentals. Because it will also cover the latest developments in NMT, it should also be useful to attendees with some experience in NMT.

pdf bib
Proceedings of the 4th Workshop on Asian Translation (WAT2017)
Toshiaki Nakazawa | Isao Goto
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.

pdf abs
Kyoto University Participation to WAT 2017
Fabien Cromieres | Raj Dabre | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the 4th Workshop on Asian Translation (WAT2017)

We describe here our approaches and results on the WAT 2017 shared translation tasks. Following our good results with Neural Machine Translation in the previous shared task, we continue this approach this year, with incremental improvements in models and training methods. We focused on the ASPEC dataset and could improve the state-of-the-art results for Chinese-to-Japanese and Japanese-to-Chinese translations.

2016

pdf
IRT-based Aggregation Model of Crowdsourced Pairwise Comparison for Evaluating Machine Translations
Naoki Otani | Toshiaki Nakazawa | Daisuke Kawahara | Sadao Kurohashi
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

pdf
Insertion Position Selection Model for Flexible Non-Terminals in Dependency Tree-to-Tree Machine Translation
Toshiaki Nakazawa | John Richardson | Sadao Kurohashi
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

pdf bib
Flexible Non-Terminals for Dependency Tree-to-Tree Reordering
John Richardson | Fabien Cromières | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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.

pdf abs
Kyoto University Participation to WAT 2016
Fabien Cromieres | Chenhui Chu | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

We describe here our approaches and results on the WAT 2016 shared translation tasks. We tried to use both an example-based machine translation (MT) system and a neural MT system. We report very good translation results, especially when using neural MT for Chinese-to-Japanese translation.

pdf abs
SCTB: A Chinese Treebank in Scientific Domain
Chenhui Chu | Toshiaki Nakazawa | Daisuke Kawahara | Sadao Kurohashi
Proceedings of the 12th Workshop on Asian Language Resources (ALR12)

Treebanks are curial for natural language processing (NLP). In this paper, we present our work for annotating a Chinese treebank in scientific domain (SCTB), to address the problem of the lack of Chinese treebanks in this domain. Chinese analysis and machine translation experiments conducted using this treebank indicate that the annotated treebank can significantly improve the performance on both tasks. This treebank is released to promote Chinese NLP research in scientific domain.

pdf abs
Simultaneous Sentence Boundary Detection and Alignment with Pivot-based Machine Translation Generated Lexicons
Antoine Bourlon | Chenhui Chu | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Sentence alignment is a task that consists in aligning the parallel sentences in a translated article pair. This paper describes a method to perform sentence boundary detection and alignment simultaneously, which significantly improves the alignment accuracy on languages like Chinese with uncertain sentence boundaries. It relies on the definition of hard (certain) and soft (uncertain) punctuation delimiters, the latter being possibly ignored to optimize the alignment result. The alignment method is used in combination with lexicons automatically generated from the input article pairs using pivot-based MT, achieving better coverage of the input words with fewer entries than pre-existing dictionaries. Pivot-based MT makes it possible to build dictionaries for language pairs that have scarce parallel data. The alignment method is implemented in a tool that will be freely available in the near future.

In this paper, we describe the details of the ASPEC (Asian Scientific Paper Excerpt Corpus), which is the first large-size parallel corpus of scientific paper domain. ASPEC was constructed in the Japanese-Chinese machine translation project conducted between 2006 and 2010 using the Special Coordination Funds for Promoting Science and Technology. It consists of a Japanese-English scientific paper abstract corpus of approximately 3 million parallel sentences (ASPEC-JE) and a Chinese-Japanese scientific paper excerpt corpus of approximately 0.68 million parallel sentences (ASPEC-JC). ASPEC is used as the official dataset for the machine translation evaluation workshop WAT (Workshop on Asian Translation).

pdf bib
Dependency Forest based Word Alignment
Hitoshi Otsuki | Chenhui Chu | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the ACL 2016 Student Research Workshop

2015

pdf
Korean-Chinese word translation using Chinese character knowledge
Yuanmei Lu | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of Machine Translation Summit XV: Papers

pdf
Promoting science and technology exchange using machine translation
Toshiaki Nakazawa
Proceedings of the 6th Workshop on Patent and Scientific Literature Translation

pdf
Enhancing function word translation with syntax-based statistical post-editing
John Richardson | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the 6th Workshop on Patent and Scientific Literature Translation

pdf
Large-scale Dictionary Construction via Pivot-based Statistical Machine Translation with Significance Pruning and Neural Network Features
Raj Dabre | Chenhui Chu | Fabien Cromieres | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

pdf
Pivot-Based Topic Models for Low-Resource Lexicon Extraction
John Richardson | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

2014

pdf abs
Bilingual Dictionary Construction with Transliteration Filtering
John Richardson | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper we present a bilingual transliteration lexicon of 170K Japanese-English technical terms in the scientific domain. Translation pairs are extracted by filtering a large list of transliteration candidates generated automatically from a phrase table trained on parallel corpora. Filtering uses a novel transliteration similarity measure based on a discriminative phrase-based machine translation approach. We demonstrate that the extracted dictionary is accurate and of high recall (F1 score 0.8). Our lexicon contains not only single words but also multi-word expressions, and is freely available. Our experiments focus on Katakana-English lexicon construction, however it would be possible to apply the proposed methods to transliteration extraction for a variety of language pairs.

pdf abs
Constructing a Chinese—Japanese Parallel Corpus from Wikipedia
Chenhui Chu | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Parallel corpora are crucial for statistical machine translation (SMT). However, they are quite scarce for most language pairs, such as Chinese―Japanese. As comparable corpora are far more available, many studies have been conducted to automatically construct parallel corpora from comparable corpora. This paper presents a robust parallel sentence extraction system for constructing a Chinese―Japanese parallel corpus from Wikipedia. The system is inspired by previous studies that mainly consist of a parallel sentence candidate filter and a binary classifier for parallel sentence identification. We improve the system by using the common Chinese characters for filtering and two novel feature sets for classification. Experiments show that our system performs significantly better than the previous studies for both accuracy in parallel sentence extraction and SMT performance. Using the system, we construct a Chinese―Japanese parallel corpus with more than 126k highly accurate parallel sentences from Wikipedia. The constructed parallel corpus is freely available at http://orchid.kuee.kyoto-u.ac.jp/chu/resource/wiki_zh_ja.tgz.

pdf
Improving Statistical Machine Translation Accuracy Using Bilingual Lexicon Extractionwith Paraphrases
Chenhui Chu | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing

pdf bib
Proceedings of the 1st Workshop on Asian Translation (WAT2014)
Toshiaki Nakazawa | Hideya Mino | Isao Goto | Sadao Kurohashi | Eiichiro Sumita
Proceedings of the 1st Workshop on Asian Translation (WAT2014)

pdf bib
Overview of the 1st Workshop on Asian Translation
Toshiaki Nakazawa | Hideya Mino | Isao Goto | Sadao Kurohashi | Eiichiro Sumita
Proceedings of the 1st Workshop on Asian Translation (WAT2014)

pdf
KyotoEBMT System Description for the 1st Workshop on Asian Translation
John Richardson | Fabien Cromières | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the 1st Workshop on Asian Translation (WAT2014)

pdf
KyotoEBMT: An Example-Based Dependency-to-Dependency Translation Framework
John Richardson | Fabien Cromières | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations

pdf abs
Post-editing user interface using visualization of a sentence structure
Yudai Kishimoto | Toshiaki Nakazawa | Daisuke Kawahara | Sadao Kurohashi
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas

Translation has become increasingly important by virtue of globalization. To reduce the cost of translation, it is necessary to use machine translation and further to take advantage of post-editing based on the result of a machine translation for accurate information dissemination. Such post-editing (e.g., PET [Aziz et al., 2012]) can be used practically for translation between European languages, which has a high performance in statistical machine translation. However, due to the low accuracy of machine translation between languages with different word order, such as Japanese-English and Japanese-Chinese, post-editing has not been used actively.

2013

pdf
Chinese–Japanese Parallel Sentence Extraction from Quasi–Comparable Corpora
Chenhui Chu | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the Sixth Workshop on Building and Using Comparable Corpora

pdf
Robust Transliteration Mining from Comparable Corpora with Bilingual Topic Models
John Richardson | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the Sixth International Joint Conference on Natural Language Processing

pdf
Accurate Parallel Fragment Extraction from Quasi–Comparable Corpora using Alignment Model and Translation Lexicon
Chenhui Chu | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

pdf abs
EBMT system of Kyoto University in OLYMPICS task at IWSLT 2012
Chenhui Chu | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the EBMT system of Kyoto University that participated in the OLYMPICS task at IWSLT 2012. When translating very different language pairs such as Chinese-English, it is very important to handle sentences in tree structures to overcome the difference. Many recent studies incorporate tree structures in some parts of translation process, but not all the way from model training (alignment) to decoding. Our system is a fully tree-based translation system where we use the Bayesian phrase alignment model on dependency trees and example-based translation. To improve the translation quality, we conduct some special processing for the IWSLT 2012 OLYMPICS task, including sub-sentence splitting, non-parallel sentence filtering, adoption of an optimized Chinese segmenter and rule-based decoding constraints.

pdf
Alignment by Bilingual Generation and Monolingual Derivation
Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of COLING 2012

pdf abs
Chinese Characters Mapping Table of Japanese, Traditional Chinese and Simplified Chinese
Chenhui Chu | Toshiaki Nakazawa | Sadao Kurohashi
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Chinese characters are used both in Japanese and Chinese, which are called Kanji and Hanzi respectively. Chinese characters contain significant semantic information, a mapping table between Kanji and Hanzi can be very useful for many Japanese-Chinese bilingual applications, such as machine translation and cross-lingual information retrieval. Because Kanji characters are originated from ancient China, most Kanji have corresponding Chinese characters in Hanzi. However, the relation between Kanji and Hanzi is quite complicated. In this paper, we propose a method of making a Chinese characters mapping table of Japanese, Traditional Chinese and Simplified Chinese automatically by means of freely available resources. We define seven categories for Kanji based on the relation between Kanji and Hanzi, and classify mappings of Chinese characters into these categories. We use a resource from Wiktionary to show the completeness of the mapping table we made. Statistical comparison shows that our proposed method makes a more complete mapping table than the current version of Wiktionary.

pdf
Exploiting Shared Chinese Characters in Chinese Word Segmentation Optimization for Chinese-Japanese Machine Translation
Chenhui Chu | Toshiaki Nakazawa | Daisuke Kawahara | Sadao Kurohashi
Proceedings of the 16th Annual Conference of the European Association for Machine Translation

In this paper, we propose a probabilistic phrase alignment model based on dependency trees. This model is linguistically-motivated, using syntactic information during alignment process. The main advantage of this model is that the linguistic difference between source and target languages is successfully absorbed. It is composed of two models: Model1 is using content word translation probability and function word translation probability; Model2 uses dependency relation probability which is defined for a pair of positional relations on dependency trees. Relation probability acts as tree-based phrase reordering model. Since this model is directed, we combine two alignment results from bi-directional training by symmetrization heuristics to get definitive alignment. We conduct experiments on a Japanese-English corpus, and achieve reasonably high quality of alignment compared with word-based alignment model.