Takumi Ito


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Challenges in Reproducing Human Evaluation Results for Role-Oriented Dialogue Summarization
Takumi Ito | Qixiang Fang | Pablo Mosteiro | Albert Gatt | Kees van Deemter
Proceedings of the 3rd Workshop on Human Evaluation of NLP Systems

There is a growing concern regarding the reproducibility of human evaluation studies in NLP. As part of the ReproHum campaign, we conducted a study to assess the reproducibility of a recent human evaluation study in NLP. Specifically, we attempted to reproduce a human evaluation of a novel approach to enhance Role-Oriented Dialogue Summarization by considering the influence of role interactions. Despite our best efforts to adhere to the reported setup, we were unable to reproduce the statistical results as presented in the original paper. While no contradictory evidence was found, our study raises questions about the validity of the reported statistical significance results, and/or the comprehensiveness with which the original study was reported. In this paper, we provide a comprehensive account of our reproduction study, detailing the methodologies employed, data collection, and analysis procedures. We discuss the implications of our findings for the broader issue of reproducibility in NLP research. Our findings serve as a cautionary reminder of the challenges in conducting reproducible human evaluations and prompt further discussions within the NLP community.

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Investigating the Effectiveness of Multiple Expert Models Collaboration
Ikumi Ito | Takumi Ito | Jun Suzuki | Kentaro Inui
Findings of the Association for Computational Linguistics: EMNLP 2023

This paper aims to investigate the effectiveness of several machine translation (MT) models and aggregation methods in a multi-domain setting under fair conditions and explore a direction for tackling multi-domain MT. We mainly compare the performance of the single model approach by jointly training all domains and the multi-expert models approach with a particular aggregation strategy. We conduct experiments on multiple domain datasets and demonstrate that a combination of smaller domain expert models can outperform a larger model trained for all domain data.

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SKIM at WMT 2023 General Translation Task
Keito Kudo | Takumi Ito | Makoto Morishita | Jun Suzuki
Proceedings of the Eighth Conference on Machine Translation

The SKIM team’s submission used a standard procedure to build ensemble Transformer models, including base-model training, back-translation of base models for data augmentation, and retraining of several final models using back-translated training data. Each final model had its own architecture and configuration, including up to 10.5B parameters, and substituted self- and cross-sublayers in the decoder with a cross+self-attention sub-layer. We selected the best candidate from a large candidate pool, namely 70 translations generated from 13 distinct models for each sentence, using an MBR reranking method using COMET and COMET-QE. We also applied data augmentation and selection techniques to the training data of the Transformer models.

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Missing Information, Unresponsive Authors, Experimental Flaws: The Impossibility of Assessing the Reproducibility of Previous Human Evaluations in NLP
Anya Belz | Craig Thomson | Ehud Reiter | Gavin Abercrombie | Jose M. Alonso-Moral | Mohammad Arvan | Anouck Braggaar | Mark Cieliebak | Elizabeth Clark | Kees van Deemter | Tanvi Dinkar | Ondřej Dušek | Steffen Eger | Qixiang Fang | Mingqi Gao | Albert Gatt | Dimitra Gkatzia | Javier González-Corbelle | Dirk Hovy | Manuela Hürlimann | Takumi Ito | John D. Kelleher | Filip Klubicka | Emiel Krahmer | Huiyuan Lai | Chris van der Lee | Yiru Li | Saad Mahamood | Margot Mieskes | Emiel van Miltenburg | Pablo Mosteiro | Malvina Nissim | Natalie Parde | Ondřej Plátek | Verena Rieser | Jie Ruan | Joel Tetreault | Antonio Toral | Xiaojun Wan | Leo Wanner | Lewis Watson | Diyi Yang
Proceedings of the Fourth Workshop on Insights from Negative Results in NLP

We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/less reproducible. We present our results and findings, which include that just 13% of papers had (i) sufficiently low barriers to reproduction, and (ii) enough obtainable information, to be considered for reproduction, and that all but one of the experiments we selected for reproduction was discovered to have flaws that made the meaningfulness of conducting a reproduction questionable. As a result, we had to change our coordinated study design from a reproduce approach to a standardise-then-reproduce-twice approach. Our overall (negative) finding that the great majority of human evaluations in NLP is not repeatable and/or not reproducible and/or too flawed to justify reproduction, paints a dire picture, but presents an opportunity for a rethink about how to design and report human evaluations in NLP.


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Lower Perplexity is Not Always Human-Like
Tatsuki Kuribayashi | Yohei Oseki | Takumi Ito | Ryo Yoshida | Masayuki Asahara | Kentaro Inui
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In computational psycholinguistics, various language models have been evaluated against human reading behavior (e.g., eye movement) to build human-like computational models. However, most previous efforts have focused almost exclusively on English, despite the recent trend towards linguistic universal within the general community. In order to fill the gap, this paper investigates whether the established results in computational psycholinguistics can be generalized across languages. Specifically, we re-examine an established generalization —the lower perplexity a language model has, the more human-like the language model is— in Japanese with typologically different structures from English. Our experiments demonstrate that this established generalization exhibits a surprising lack of universality; namely, lower perplexity is not always human-like. Moreover, this discrepancy between English and Japanese is further explored from the perspective of (non-)uniform information density. Overall, our results suggest that a cross-lingual evaluation will be necessary to construct human-like computational models.


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Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese
Tatsuki Kuribayashi | Takumi Ito | Jun Suzuki | Kentaro Inui
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We examine a methodology using neural language models (LMs) for analyzing the word order of language. This LM-based method has the potential to overcome the difficulties existing methods face, such as the propagation of preprocessor errors in count-based methods. In this study, we explore whether the LM-based method is valid for analyzing the word order. As a case study, this study focuses on Japanese due to its complex and flexible word order. To validate the LM-based method, we test (i) parallels between LMs and human word order preference, and (ii) consistency of the results obtained using the LM-based method with previous linguistic studies. Through our experiments, we tentatively conclude that LMs display sufficient word order knowledge for usage as an analysis tool. Finally, using the LM-based method, we demonstrate the relationship between the canonical word order and topicalization, which had yet to be analyzed by large-scale experiments.

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Tohoku-AIP-NTT at WMT 2020 News Translation Task
Shun Kiyono | Takumi Ito | Ryuto Konno | Makoto Morishita | Jun Suzuki
Proceedings of the Fifth Conference on Machine Translation

In this paper, we describe the submission of Tohoku-AIP-NTT to the WMT’20 news translation task. We participated in this task in two language pairs and four language directions: English <–> German and English <–> Japanese. Our system consists of techniques such as back-translation and fine-tuning, which are already widely adopted in translation tasks. We attempted to develop new methods for both synthetic data filtering and reranking. However, the methods turned out to be ineffective, and they provided us with no significant improvement over the baseline. We analyze these negative results to provide insights for future studies.

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Langsmith: An Interactive Academic Text Revision System
Takumi Ito | Tatsuki Kuribayashi | Masatoshi Hidaka | Jun Suzuki | Kentaro Inui
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Despite the current diversity and inclusion initiatives in the academic community, researchers with a non-native command of English still face significant obstacles when writing papers in English. This paper presents the Langsmith editor, which assists inexperienced, non-native researchers to write English papers, especially in the natural language processing (NLP) field. Our system can suggest fluent, academic-style sentences to writers based on their rough, incomplete phrases or sentences. The system also encourages interaction between human writers and the computerized revision system. The experimental results demonstrated that Langsmith helps non-native English-speaker students write papers in English. The system is available at https://emnlp-demo.editor.langsmith.co.jp/.


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Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance
Takumi Ito | Tatsuki Kuribayashi | Hayato Kobayashi | Ana Brassard | Masato Hagiwara | Jun Suzuki | Kentaro Inui
Proceedings of the 12th International Conference on Natural Language Generation

The writing process consists of several stages such as drafting, revising, editing, and proofreading. Studies on writing assistance, such as grammatical error correction (GEC), have mainly focused on sentence editing and proofreading, where surface-level issues such as typographical errors, spelling errors, or grammatical errors should be corrected. We broaden this focus to include the earlier revising stage, where sentences require adjustment to the information included or major rewriting and propose Sentence-level Revision (SentRev) as a new writing assistance task. Well-performing systems in this task can help inexperienced authors by producing fluent, complete sentences given their rough, incomplete drafts. We build a new freely available crowdsourced evaluation dataset consisting of incomplete sentences authored by non-native writers paired with their final versions extracted from published academic papers for developing and evaluating SentRev models. We also establish baseline performance on SentRev using our newly built evaluation dataset.

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TEASPN: Framework and Protocol for Integrated Writing Assistance Environments
Masato Hagiwara | Takumi Ito | Tatsuki Kuribayashi | Jun Suzuki | Kentaro Inui
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

Language technologies play a key role in assisting people with their writing. Although there has been steady progress in e.g., grammatical error correction (GEC), human writers are yet to benefit from this progress due to the high development cost of integrating with writing software. We propose TEASPN, a protocol and an open-source framework for achieving integrated writing assistance environments. The protocol standardizes the way writing software communicates with servers that implement such technologies, allowing developers and researchers to integrate the latest developments in natural language processing (NLP) with low cost. As a result, users can enjoy the integrated experience in their favorite writing software. The results from experiments with human participants show that users use a wide range of technologies and rate their writing experience favorably, allowing them to write more fluent text.


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Coreference Resolution on Math Problem Text in Japanese
Takumi Ito | Takuya Matsuzaki | Satoshi Sato
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

This paper describes a coreference resolution system for math problem text. Case frame dictionaries and a math taxonomy are utilized for supplying domain knowledge. The system deals with various anaphoric phenomena beyond well-studied entity coreferences.

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Semantic Parsing of Pre-university Math Problems
Takuya Matsuzaki | Takumi Ito | Hidenao Iwane | Hirokazu Anai | Noriko H. Arai
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We have been developing an end-to-end math problem solving system that accepts natural language input. The current paper focuses on how we analyze the problem sentences to produce logical forms. We chose a hybrid approach combining a shallow syntactic analyzer and a manually-developed lexicalized grammar. A feature of the grammar is that it is extensively typed on the basis of a formal ontology for pre-university math. These types are helpful in semantic disambiguation inside and across sentences. Experimental results show that the hybrid system produces a well-formed logical form with 88% precision and 56% recall.