66 citations · 156 across the 18 of their papers we have counts for
20 papers
WikiOriginQA: Automated Construction of a QA Dataset with Knowledge Origins for Analyzing Cultural Bias
Kento Haneda, Yosuke Kishinami, Ryo Fujii +2
Existing datasets on cultural knowledge have been typically created by human annotators familiar with each culture. However, the process requires considerable human effort, present…
Revisiting Non-Verbatim Memorization in Large Language Models: The Role of Entity Surface Forms
Yuto Nishida, Naoki Shikoda, Yosuke Kishinami +4
Understanding what kinds of factual knowledge large language models (LLMs) memorize is essential for evaluating their reliability and limitations. Entity-based QA is a common frame…
TimeMachine-bench: A Benchmark for Evaluating Model Capabilities in Repository-Level Migration Tasks
Ryo Fujii, Makoto Morishita, Kazuki Yano +1
With the advancement of automated software engineering, research focus is increasingly shifting toward practical tasks reflecting the day-to-day work of software engineers. Among t…
JaParaPat: A Large-Scale Japanese-English Parallel Patent Application Corpus
Masaaki Nagata, Katsuki Chousa, Norihito Yasuda
We constructed JaParaPat (Japanese-English Parallel Patent Application Corpus), a bilingual corpus of more than 300 million Japanese-English sentence pairs from patent applications…
MQM-Chat: Multidimensional Quality Metrics for Chat Translation
Yunmeng Li, Jun Suzuki, Makoto Morishita +2
The complexities of chats pose significant challenges for machine translation models. Recognizing the need for a precise evaluation metric to address the issues of chat translation…
An Investigation of Warning Erroneous Chat Translations in Cross-lingual Communication
Yunmeng Li, Jun Suzuki, Makoto Morishita +2
Machine translation models are still inappropriate for translating chats, despite the popularity of translation software and plug-in applications. The complexity of dialogues poses…
A Japanese-Chinese Parallel Corpus Using Crowdsourcing for Web Mining
Masaaki Nagata, Makoto Morishita, Katsuki Chousa +1
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…
WikiSplit++: Easy Data Refinement for Split and Rephrase
Hayato Tsukagoshi, Tsutomu Hirao, Makoto Morishita +3
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 downstrea…
Refactoring Programs Using Large Language Models with Few-Shot Examples
Atsushi Shirafuji, Yusuke Oda, Jun Suzuki +2
A less complex and more straightforward program is a crucial factor that enhances its maintainability and makes writing secure and bug-free programs easier. However, due to its hea…
Chat Translation Error Detection for Assisting Cross-lingual Communications
Yunmeng Li, Jun Suzuki, Makoto Morishita +4
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…
Exploring the Robustness of Large Language Models for Solving Programming Problems
Atsushi Shirafuji, Yutaka Watanobe, Takumi Ito +4
Using large language models (LLMs) for source code has recently gained attention. LLMs, such as Transformer-based models like Codex and ChatGPT, have been shown to be highly capabl…
Domain Adaptation of Machine Translation with Crowdworkers
Makoto Morishita, Jun Suzuki, Masaaki Nagata
Although a machine translation model trained with a large in-domain parallel corpus achieves remarkable results, it still works poorly when no in-domain data are available. This si…
JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus
Makoto Morishita, Katsuki Chousa, Jun Suzuki +1
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. Althoug…
Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021
Katsuki Chousa, Makoto Morishita
This paper describes our systems that were submitted to the restricted translation task at WAT 2021. In this task, the systems are required to output translated sentences that cont…
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents
Ryo Fujii, Masato Mita, Kaori Abe +4
Neural Machine Translation (NMT) has shown drastic improvement in its quality when translating clean input, such as text from the news domain. However, existing studies suggest tha…
Recovery command generation towards automatic recovery in ICT systems by Seq2Seq learning
Hiroki Ikeuchi, Akio Watanabe, Tsutomu Hirao +4
With the increase in scale and complexity of ICT systems, their operation increasingly requires automatic recovery from failures. Although it has become possible to automatically d…
JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpus
Makoto Morishita, Jun Suzuki, Masaaki Nagata
Recent machine translation algorithms mainly rely on parallel corpora. However, since the availability of parallel corpora remains limited, only some resource-rich language pairs c…
NTT's Machine Translation Systems for WMT19 Robustness Task
Soichiro Murakami, Makoto Morishita, Tsutomu Hirao +1
This paper describes NTT's submission to the WMT19 robustness task. This task mainly focuses on translating noisy text (e.g., posts on Twitter), which presents different difficulti…
An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation
Makoto Morishita, Yusuke Oda, Graham Neubig +3
Training of neural machine translation (NMT) models usually uses mini-batches for efficiency purposes. During the mini-batched training process, it is necessary to pad shorter sent…
Neural Reranking Improves Subjective Quality of Machine Translation: NAIST at WAT2015
Graham Neubig, Makoto Morishita, Satoshi Nakamura
This year, the Nara Institute of Science and Technology (NAIST)'s submission to the 2015 Workshop on Asian Translation was based on syntax-based statistical machine translation, wi…