Papers by Akihiro Tamura
Transformer-based Approach for Predicting Chemical Compound Structures (2020.aacl-main)
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| 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)
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| 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). |
Improving Neural Machine Translation with Neural Syntactic Distance (N19-1)
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| Challenge: | Neural syntactic distance (NSD) is used to represent constituent trees using a sequence whose length is identical to the number of words in the sentence. |
| Approach: | They propose five strategies to improve NMT with explicit use of syntactic information . et al., 2014) propose a set of five strategies that incorporate syntastic information into the encoder and/or decoder of the baseline model. |
| Outcome: | The proposed strategies improve translation performance of the baseline model (+2.1 (En–Ja), +1.3 (Ja–En), +1.2 (En-Ch), and +1.0 (Ch–En) BLEU. |
A Benchmark Dataset for Multi-Level Complexity-Controllable Machine Translation (2022.lrec-1)
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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 . |
Hie-BART: Document Summarization with Hierarchical BART (2021.naacl-srw)
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| 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. |
Neural Machine Translation Incorporating Named Entity (C18-1)
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| 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. |
Probabilistic Bilingual Subword Segmentation with Latent Subword Alignment (2026.eacl-srw)
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| 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)
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| 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)
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| 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)
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| 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)
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| 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. |
Forest-Based Neural Machine Translation (P18-1)
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| Challenge: | Compared with string-to-string systems, tree-based NMT methods use more syntactic information and can incorporate prior knowledge. |
| Approach: | They propose a tree-based neural machine translation method that translates a linearized packed forest under a simple sequence-to-sequence framework. |
| Outcome: | The proposed method outperforms tree-based approaches in the BLEU score of the proposed model. |
Supervised Visual Attention for Multimodal Neural Machine Translation (2020.coling-main)
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| 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. |