Papers by Takashi Ninomiya
Transformer-based Approach for Predicting Chemical Compound Structures (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing methods to predict chemical compound structures from their names are limited and use handcrafted rules. |
| Approach: | They propose a Transformer-based model that predicts SMILES strings from chemical compound names instead of handcrafted rules. |
| Outcome: | The proposed model achieves higher F-measures than the existing model and the existing one. |
Synchronous Syntactic Attention for Transformer Neural Machine Translation (2021.acl-srw)
Copied to clipboard
| Challenge: | Existing syntaxbased NMT models use monolingual syntactic information on either side or both. |
| Approach: | They propose a mechanism that synchronizes source-side and target-side syntactic self-attentions by minimizing the difference between target- and target side self- attentions mapped by the encoder-decoder attention matrix. |
| Outcome: | The proposed method improves translation performance on WMT14 En-De, WMT16 En-Ro, and ASPEC Ja-En (up to +0.38 points in BLEU). |
A Benchmark Dataset for Multi-Level Complexity-Controllable Machine Translation (2022.lrec-1)
Copied to clipboard
Kazuki Tani, Ryoya Yuasa, Kazuki Takikawa, Akihiro Tamura, Tomoyuki Kajiwara, Takashi Ninomiya, Tsuneo Kato
| Challenge: | Existing test datasets for MLCC-MT have three problems: A source language sentence and its simplified target language sentence are not necessarily exactly parallel. |
| Approach: | They propose to use a test dataset to evaluate multi-level complexity-controllable machine translation (MLCC-MT) their results are compared to a standard test dataset constructed from the Newsela corpus . |
| Outcome: | The proposed test dataset is based on the Newsela corpus and is released . it includes automatic filtering, manual check for parallel target language sentences . |
Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for unsupervised quality estimation of machine translation are limited to several major language pairs. |
| Approach: | They propose a method to distill language-agnostic meaning embeddings from multilingual sentence encoders for unsupervised quality estimation of machine translation. |
| Outcome: | The proposed method achieves higher correlations with human evaluations on unsupervised translation quality estimation. |
Hie-BART: Document Summarization with Hierarchical BART (2021.naacl-srw)
Copied to clipboard
| Challenge: | Existing document summarization models do not capture hierarchical structures of documents . proposed model incorporates multi-granularity self-attention (MG-SA) |
| Approach: | They propose a new abstractive document summarization model, hierarchical BART . the proposed model captures hierarchically structured sentences in the BART model . |
| Outcome: | The proposed model outperforms baseline models and improves performance on CNN/Daily Mail dataset. |
Transfer Fine-tuning for Quality Estimation of Text Simplification (2024.lrec-main)
Copied to clipboard
| Challenge: | Experimental results show that quality estimation of text simplification models can be improved on a small labeled corpus. |
| Approach: | They propose a method to train quality estimation of text simplification on a small-scale labeled corpus prior to fine-tuning pre-trained language models. |
| Outcome: | The proposed method improves quality estimation of text simplification on a small-scale labeled corpus. |
Utilizing Longer Context than Speech Bubbles in Automated Manga Translation (2024.lrec-main)
Copied to clipboard
Hiroto Kaino, Soichiro Sugihara, Tomoyuki Kajiwara, Takashi Ninomiya, Joshua B. Tanner, Shonosuke Ishiwatari
| Challenge: | Existing methods to capture contextual information for manga machine translation are difficult to perform . unofficially translated pirated copies of manga are circulating overseas in large numbers . |
| Approach: | They propose two new ways to capture broader contextual information in manga machine translation . scene-based translation considers previous scene and broader context information . detailed analysis reveals the effect of zero-anaphora resolution in translation - highlighting the usefulness of longer contextual information if manga is translated in Japanese . |
| Outcome: | The proposed methods improve translation quality for manga (Japanese-style comics) the results show that the combined methods achieve the highest quality. |
Controllable Text Simplification with Deep Reinforcement Learning (2022.aacl-short)
Copied to clipboard
| Challenge: | Existing methods for controlling sentence difficulty have not taken into account sentence-level difficulties. |
| Approach: | They propose a method for controlling the difficulty of a sentence based on deep reinforcement learning. |
| Outcome: | The proposed method generates sentences of appropriate difficulty for the target audience through reinforcement learning. |
Neural Machine Translation Incorporating Named Entity (C18-1)
Copied to clipboard
| Challenge: | Conventional NMT models have difficulty translating words with multiple meanings because of the high ambiguity. |
| Approach: | They propose a neural machine translation model that incorporates named entity (NE) tags of source-language sentences to reduce the difficulty in translating multiple meanings. |
| Outcome: | The proposed model achieves 3.11 point improvement in bilingual evaluation understudy (BLEU) on English-to-Japanese translation task with the ASPEC, and English- to-Bulgarian and English to-Romanian translation tasks with the Europarl corpus. |
A Japanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain (2022.lrec-1)
Copied to clipboard
Haruya Suzuki, Yuto Miyauchi, Kazuki Akiyama, Tomoyuki Kajiwara, Takashi Ninomiya, Noriko Takemura, Yuta Nakashima, Hajime Nagahara
| Challenge: | Existing studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. |
| Approach: | They extend the WRIME dataset with basic emotion intensity from both the writer's subjective and reader's perspective to include the Japanese sentiment polarity. |
| Outcome: | The proposed dataset is the first large-scale corpus to annotate both basic emotions and sentiment polarity labels from both the writer’s and reader’s perspectives. |
Evaluation Dataset for Japanese Medical Text Simplification (2024.naacl-srw)
Copied to clipboard
| Challenge: | Existing studies on medical text simplification in English have not been well explored in Japanese because of the lack of a parallel corpus of this domain. |
| Approach: | They propose a lexically constrained reranking method that allows to avoid technical terms to be output. |
| Outcome: | The proposed method improves on the weblogs of Japanese patients and reduces the need for a training corpus. |
Domain Adaptation of Image Encoder for Multimodal Manga Translation (2026.eacl-srw)
Copied to clipboard
| Challenge: | Existing machine translation systems lack sufficient manga comprehension capabilities when utilizing image information. |
| Approach: | They propose a domain-adapted image encoder training method for manga . the method trains encoders to acquire visual features that consider the structural and sequential characteristics of the manga based on a Japanese-English translation task. |
| Outcome: | The proposed method improves translation evaluation metrics in Japanese-English translation task compared to the conventional method . |
Probabilistic Bilingual Subword Segmentation with Latent Subword Alignment (2026.eacl-srw)
Copied to clipboard
| Challenge: | Existing methods do not consider parallel relationships, preventing translation model training. |
| Approach: | They propose a method for learning subword correspondences in parallel sentence pairs using the EM algorithm. |
| Outcome: | The proposed method improves translation accuracy for many tasks. |
Multimodal Neural Machine Translation Using Synthetic Images Transformed by Latent Diffusion Model (2023.acl-srw)
Copied to clipboard
| Challenge: | Existing methods to translate source language sentences using images are not optimal for machine translation. |
| Approach: | They propose a new multimodal neural machine translation model using synthetic images transformed by a latent diffusion model. |
| Outcome: | The proposed model improves translation performance on English-German translation tasks using the Multi30k dataset. |
Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing (D19-1)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is one of the important basic technologies for Natural Language Processing (NLP) . |
| Approach: | They propose to use long short-term memory (LSTM) of NER model to capture chemical com- pound paraphrases by sharing parameters of LSTM and character embeddings be- tween the two models. |
| Outcome: | The proposed method improves chemi- cal NER and achieves state-of-the-art performance on the BioCreative IV’s CHEMDNER task. |
A Visually-Grounded Parallel Corpus with Phrase-to-Region Linking (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing multimodal corpora lack the ability to be used in multilingual or non-English scenarios. |
| Approach: | They extend a Flickr30k Entities image-caption dataset with Japanese translations to provide a multilingual corpus. |
| Outcome: | The proposed dataset is the first multilingual image-caption dataset with Japanese translations. |
Bilingual Subword Segmentation for Neural Machine Translation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing subword segmentation methods tokenize sentences without considering translation . proposed method could be more favorable to machine translation if it uses bilingual sentences . |
| Approach: | They propose a subword segmentation method that tokenizes sentences by using subword units induced from bilingual sentences. |
| Outcome: | The proposed method improves translation performance on translation tasks up to +0.81 BLEU. |
Distractor Generation for Fill-in-the-Blank Exercises by Question Type (2023.acl-srw)
Copied to clipboard
| Challenge: | Existing studies have generated words that are semantically similar to the correct words as distractors for fill-in-the-blank questions. |
| Approach: | They propose a method to automatically generate distractors for fill-in-the-blank questions in entrance examinations for Japanese universities. |
| Outcome: | The proposed method is effective on 500 actual questions on English fill-in-the-blank questions in Japanese universities. |
Emotional Intensity Estimation based on Writer’s Personality (2022.aacl-srw)
Copied to clipboard
| Challenge: | Existing emotion analysis models are difficult to accurately estimate the writer’s subjective emotions behind the text. |
| Approach: | They propose a method for personalized emotional intensity estimation based on a writer's personality test for Japanese SNS posts. |
| Outcome: | The proposed method improves on the existing method and the proposed hybrid model achieved state-of-the-art performance. |
Multi-Source Text Classification for Multilingual Sentence Encoder with Machine Translation (2024.naacl-srw)
Copied to clipboard
| Challenge: | Pre-trained multilingual sentence encoders suffer from performance degradation for non-English languages. |
| Approach: | They propose a method of machine translating a source sentence into English and then inputting it together with the source sentence in a multi-source manner. |
| Outcome: | The proposed method improves the performance of pre-trained multilingual sentence encoders in Japanese on sentiment analysis and topic classification tasks. |
MultiMSD: A Corpus for Multilingual Medical Text Simplification from Online Medical References (2025.findings-acl)
Copied to clipboard
| Challenge: | Medical texts contain technical terms, and non-experts often cannot use information effectively. |
| Approach: | They propose a method for training medical text simplification models to actively paraphrase medical terms. |
| Outcome: | The proposed method improves the performance of medical text simplification in nine languages. |
Paraphrase-based Contrastive Learning for Sentence Pair Modeling (2025.naacl-srw)
Copied to clipboard
| Challenge: | Existing methods to improve performance of sentence pair modeling are not available on a large-scale for non-English languages. |
| Approach: | They propose a method to apply contrastive learning to pre-trained masked language models . they use sentence embeddings of paraphrase pairs to make similar sentences . |
| Outcome: | The proposed method can be used on four sentence pair modeling tasks in English and Japanese. |
Supervised Visual Attention for Multimodal Neural Machine Translation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing studies show that a conventional visual attention mechanism trained in an unsupervised manner is not effective for multimodal neural machine translation. |
| Approach: | They propose a supervised visual attention mechanism for multimodal neural machine translation that captures the relationship between a word and an image region more precisely than a conventional visual attention system. |
| Outcome: | The proposed model improves on English-German and German-English translation tasks and English-Japanese and Japanese-English tasks using the Flickr30k Entities JP dataset. |