Papers by Katsuki Chousa
Incorporating Noisy Length Constraints into Transformer with Length-aware Positional Encodings (2020.coling-main)
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| Challenge: | Neural Machine Translation suffers from an under-translation problem due to limited modeling of output sequence lengths. |
| Approach: | They propose a method to train a Transformer model using length constraints based on positional encoding. |
| Outcome: | The proposed method outperforms a vanilla Transformer in an English-to-Japanese translation by 3.22 points . the noise injection improved robustness for length prediction errors, especially within the window size. |
WikiSplit++: Easy Data Refinement for Split and Rephrase (2024.lrec-main)
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| Challenge: | Existing text simplification methods rely on encoder-decoder models to achieve this task. |
| Approach: | They propose a text-to-text generation approach that applies encoder-decoder models to a large-scale dataset to improve Split and Rephrase. |
| Outcome: | The proposed approach improves Split and Rephrase readability and performance on large datasets, but still suffers from hallucinations and under-splitting. |
Automatic Evaluation of Language Generation Technology Based on Structure Alignment (2025.coling-main)
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| Challenge: | Existing methods for automatic evaluation ignore syntax of sentences despite its importance in determining meaning. |
| Approach: | They propose an automatic evaluation metric that considers both the words in sentences and their syntactic structures. |
| Outcome: | The proposed method is comparable to baselines from two NLP tasks. |
One Single Hub Text Breaks CLIP: Identifying Vulnerabilities in Cross-Modal Encoders via Hubness (2026.acl-long)
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| Challenge: | et al., 2010) show that hub embeddings are close to many unrelated examples in high-dimensional embeddable spaces . cross-modal encoders that project different modalities into a shared space are useful for cross-module applications . |
| Approach: | They propose a method for identifying the hub embedding and its corresponding hub text . they use images to evaluate cross-modal encoders that project different modalities into a shared space . |
| Outcome: | The proposed method can identify a single hub embedding and its corresponding hub text . it achieves comparable or higher similarity scores than human-written reference captions in many images . |
JaParaPat: A Large-Scale Japanese-English Parallel Patent Application Corpus (2024.lrec-main)
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| Challenge: | a recent study has demonstrated that patent translation accuracy improves as the amount of training data or the number of model parameters increases. |
| Approach: | They construct a bilingual corpus of Japanese-English patent application data from 2000 to 2021 . they extracted 1.4M Japanese- English document pairs and extracted 350M sentence pairs . |
| Outcome: | The proposed method improves translation accuracy by 20 bleu points . it is the first publicly available large-scale Japanese-English patent corpus . |
SpanAlign: Sentence Alignment Method based on Cross-Language Span Prediction and ILP (2020.coling-main)
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| Challenge: | Existing methods for automatic sentence alignment assume monotonic alignments, but they can handle non-monotonic alignments. |
| Approach: | They propose a method to automatically extract parallel sentences from noisy parallel documents by embeddings and encoding each source and target sentence. |
| Outcome: | The proposed method improves translation accuracy by 4.1 BLEU scores on English-Japanese . it can predict spans in target document from sentences in source document . |
JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus (2022.lrec-1)
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| Challenge: | Existing parallel corpora for English-Japanese are limited, limiting the accuracy of machine translation models. |
| Approach: | They propose a web-based English-Japanese parallel corpus with 21 million unique sentence pairs . this is more than twice as many as the previous corpus JParaCrawl v2.0 . |
| Outcome: | The proposed corpus boosts the accuracy of machine translation models on various domains. |
Hacking Neural Evaluation Metrics with Single Hub Text (2026.eacl-short)
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| Challenge: | Recent embedding-based neural text evaluation metrics are not reliable due to black-box nature of neural networks. |
| Approach: | They propose to find a single adversarial text in the discrete space that is consistently evaluated as high-quality regardless of the test cases. |
| Outcome: | The proposed method outperforms translations generated individually for each source sentence in English-to-Japanese and English- to-German translation tasks. |
A Supervised Word Alignment Method based on Cross-Language Span Prediction using Multilingual BERT (2020.emnlp-main)
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| Challenge: | supervised word alignment tools such as GIZA++, MGIZA (Gao and Vogel, 2008) and FastAlign remain stagnant in terms of word alignment accuracy. |
| Approach: | They propose a supervised word alignment method based on cross-language span prediction by formalizing a word alignment problem as a collection of independent predictions from a token in the source sentence to a span in the target sentence. |
| Outcome: | The proposed method significantly outperforms previous supervised and unsupervised word alignment methods without any bitexts for pretraining. |