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Found 29 papers, 6 papers with code
Tohoku-AIP-NTT at WMT 2020 News Translation Task
In this paper, we describe the submission of Tohoku-AIP-NTT to the WMT’20 news translation task.
Findings of the 2021 Conference on Machine Translation (WMT21)
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.
Overview of the 9th Workshop on Asian Translation
This paper presents the results of the shared tasks from the 9th workshop on Asian translation (WAT2022).
MQM-Chat: Multidimensional Quality Metrics for Chat Translation
The complexities of chats pose significant challenges for machine translation models.
An Investigation of Warning Erroneous Chat Translations in Cross-lingual Communication
Machine translation models are still inappropriate for translating chats, despite the popularity of translation software and plug-in applications.
Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMs
In this study, we propose a method that replaces words with high Age of Acquisitions (AoA) in translations with simpler words to match the translations to the user's level.
A Japanese-Chinese Parallel Corpus Using Crowdsourcing for Web Mining
Using crowdsourcing, we collected more than 10, 000 URL pairs (parallel top page pairs) of bilingual websites that contain parallel documents and created a Japanese-Chinese parallel corpus of 4. 6M sentence pairs from these websites.
WikiSplit++: Easy Data Refinement for Split and Rephrase
The task of Split and Rephrase, which splits a complex sentence into multiple simple sentences with the same meaning, improves readability and enhances the performance of downstream tasks in natural language processing (NLP).
Generating Diverse Translation with Perturbed kNN-MT
Generating multiple translation candidates would enable users to choose the one that satisfies their needs.
Refactoring Programs Using Large Language Models with Few-Shot Examples
A less complex and more straightforward program is a crucial factor that enhances its maintainability and makes writing secure and bug-free programs easier.
Chat Translation Error Detection for Assisting Cross-lingual Communications
In this paper, we describe the development of a communication support system that detects erroneous translations to facilitate crosslingual communications due to the limitations of current machine chat translation methods.
Exploring the Robustness of Large Language Models for Solving Programming Problems
Our experimental results show that CodeGen and Codex are sensitive to the superficial modifications of problem descriptions and significantly impact code generation performance.
Domain Adaptation of Machine Translation with Crowdworkers
With the collected parallel data, we can quickly adapt a machine translation model to the target domain.
JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus
Most current machine translation models are mainly trained with parallel corpora, and their translation accuracy largely depends on the quality and quantity of the corpora.
Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021
This paper describes our systems that were submitted to the restricted translation task at WAT 2021.
Context-aware Neural Machine Translation with Mini-batch Embedding
It is crucial to provide an inter-sentence context in Neural Machine Translation (NMT) models for higher-quality translation.
Findings of the 2020 Conference on Machine Translation (WMT20)
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.
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents
Neural Machine Translation (NMT) has shown drastic improvement in its quality when translating clean input, such as text from the news domain.
A Test Set for Discourse Translation from Japanese to English
We improved the translation accuracy using context-aware neural machine translation, and the improvement mainly reflects the betterment of the translation of zero pronouns.
Recovery command generation towards automatic recovery in ICT systems by Seq2Seq learning
With the increase in scale and complexity of ICT systems, their operation increasingly requires automatic recovery from failures.
JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpus
We constructed a parallel corpus for English-Japanese, for which the amount of publicly available parallel corpora is still limited.
NTT Neural Machine Translation Systems at WAT 2019
In this paper, we describe our systems that were submitted to the translation shared tasks at WAT 2019.
NTT's Machine Translation Systems for WMT19 Robustness Task
This paper describes NTT's submission to the WMT19 robustness task.
NTT's Neural Machine Translation Systems for WMT 2018
This paper describes NTT{'}s neural machine translation systems submitted to the WMT 2018 English-German and German-English news translation tasks.
Improving Neural Machine Translation by Incorporating Hierarchical Subword Features
We hypothesize that in the NMT model, the appropriate subword units for the following three modules (layers) can differ: (1) the encoder embedding layer, (2) the decoder embedding layer, and (3) the decoder output layer.
An Empirical Study of Building a Strong Baseline for Constituency Parsing
This paper investigates the construction of a strong baseline based on general purpose sequence-to-sequence models for constituency parsing.
Ranked #18 on
Constituency Parsing
on Penn Treebank
NTT Neural Machine Translation Systems at WAT 2017
In this year, we participated in four translation subtasks at WAT 2017.
An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation
Training of neural machine translation (NMT) models usually uses mini-batches for efficiency purposes.
Neural Reranking Improves Subjective Quality of Machine Translation: NAIST at WAT2015
We further perform a detailed analysis of reasons for this increase, finding that the main contributions of the neural models lie in improvement of the grammatical correctness of the output, as opposed to improvements in lexical choice of content words.
