Hideaki Tamori


2024

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A Japanese News Simplification Corpus with Faithfulness
Toru Urakawa | Yuya Taguchi | Takuro Niitsuma | Hideaki Tamori
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Text Simplification enhances the readability of texts for specific audiences. However, automated models may introduce unwanted content or omit essential details, necessitating a focus on maintaining faithfulness to the original input. Furthermore, existing simplified corpora contain instances of low faithfulness. Motivated by this issue, we present a new Japanese simplification corpus designed to prioritize faithfulness. Our collection comprises 7,075 paired sentences simplified from newspaper articles. This process involved collaboration with language education experts who followed guidelines balancing readability and faithfulness. Through corpus analysis, we confirmed that our dataset preserves the content of the original text, including personal names, dates, and city names. Manual evaluation showed that our corpus robustly maintains faithfulness to the original text, surpassing other existing corpora. Furthermore, evaluation by non-native readers confirmed its readability to the target audience. Through the experiment of fine-tuning and in-context learning, we demonstrated that our corpus enhances faithful sentence simplification.

2021

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Transformer-based Lexically Constrained Headline Generation
Kosuke Yamada | Yuta Hitomi | Hideaki Tamori | Ryohei Sasano | Naoaki Okazaki | Kentaro Inui | Koichi Takeda
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

This paper explores a variant of automatic headline generation methods, where a generated headline is required to include a given phrase such as a company or a product name. Previous methods using Transformer-based models generate a headline including a given phrase by providing the encoder with additional information corresponding to the given phrase. However, these methods cannot always include the phrase in the generated headline. Inspired by previous RNN-based methods generating token sequences in backward and forward directions from the given phrase, we propose a simple Transformer-based method that guarantees to include the given phrase in the high-quality generated headline. We also consider a new headline generation strategy that takes advantage of the controllable generation order of Transformer. Our experiments with the Japanese News Corpus demonstrate that our methods, which are guaranteed to include the phrase in the generated headline, achieve ROUGE scores comparable to previous Transformer-based methods. We also show that our generation strategy performs better than previous strategies.

2019

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A Large-Scale Multi-Length Headline Corpus for Analyzing Length-Constrained Headline Generation Model Evaluation
Yuta Hitomi | Yuya Taguchi | Hideaki Tamori | Ko Kikuta | Jiro Nishitoba | Naoaki Okazaki | Kentaro Inui | Manabu Okumura
Proceedings of the 12th International Conference on Natural Language Generation

Browsing news articles on multiple devices is now possible. The lengths of news article headlines have precise upper bounds, dictated by the size of the display of the relevant device or interface. Therefore, controlling the length of headlines is essential when applying the task of headline generation to news production. However, because there is no corpus of headlines of multiple lengths for a given article, previous research on controlling output length in headline generation has not discussed whether the system outputs could be adequately evaluated without multiple references of different lengths. In this paper, we introduce two corpora, which are Japanese News Corpus (JNC) and JApanese MUlti-Length Headline Corpus (JAMUL), to confirm the validity of previous evaluation settings. The JNC provides common supervision data for headline generation. The JAMUL is a large-scale evaluation dataset for headlines of three different lengths composed by professional editors. We report new findings on these corpora; for example, although the longest length reference summary can appropriately evaluate the existing methods controlling output length, this evaluation setting has several problems.

2017

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Analyzing the Revision Logs of a Japanese Newspaper for Article Quality Assessment
Hideaki Tamori | Yuta Hitomi | Naoaki Okazaki | Kentaro Inui
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism

We address the issue of the quality of journalism and analyze daily article revision logs from a Japanese newspaper company. The revision logs contain data that can help reveal the requirements of quality journalism such as the types and number of edit operations and aspects commonly focused in revision. This study also discusses potential applications such as quality assessment and automatic article revision as our future research directions.

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Proofread Sentence Generation as Multi-Task Learning with Editing Operation Prediction
Yuta Hitomi | Hideaki Tamori | Naoaki Okazaki | Kentaro Inui
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

This paper explores the idea of robot editors, automated proofreaders that enable journalists to improve the quality of their articles. We propose a novel neural model of multi-task learning that both generates proofread sentences and predicts the editing operations required to rewrite the source sentences and create the proofread ones. The model is trained using logs of the revisions made professional editors revising draft newspaper articles written by journalists. Experiments demonstrate the effectiveness of our multi-task learning approach and the potential value of using revision logs for this task.