Papers by Shoetsu Sato
Speculative Sampling in Variational Autoencoders for Dialogue Response Generation (2021.findings-emnlp)
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| Challenge: | Existing studies have tried to improve variational models but they fail to learn proper mappings. |
| Approach: | They propose to use a variable-based sampling technique to find the most probable one from redundantly sampled latent variables to tie up the variable with a given response. |
| Outcome: | The proposed method is effective in response generation with massive dialogue data constructed from Twitter posts. |
Vocabulary Adaptation for Domain Adaptation in Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Neural network methods exhibit strong performance only in a few resource-rich domains. |
| Approach: | They propose a method that fine-tunes embedding layers of a pre-trained NMT model to the target domain. |
| Outcome: | The proposed method improves fine-tuning performance in En-Ja and De-En translation by 3.86 and 3.28 BLEU points. |
Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings (N19-1)
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| Challenge: | Existing studies have attempted to personalize models to improve performance on NLP tasks such as sentiment analysis but they did not estimate subjective input. |
| Approach: | They propose a method of modeling personal biases in word meanings with personalized word embeddings by solving a task on subjective text while regarding words used by different individuals as different words. |
| Outcome: | The proposed method improves sentiment analysis and target task with reviews retrieved from RateBeer. |
Learning to Describe Unknown Phrases with Local and Global Contexts (N19-1)
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Shonosuke Ishiwatari, Hiroaki Hayashi, Naoki Yoshinaga, Graham Neubig, Shoetsu Sato, Masashi Toyoda, Masaru Kitsuregawa
| Challenge: | Existing methods for contextual guessing and definition generation do not take clues from local contexts. |
| Approach: | They propose a neural description model that takes clues from local and global contexts . they assume that the target phrase is newly emerged and there is no global context . |
| Outcome: | The proposed model takes clues from local and global contexts over existing methods . it is more effective than existing methods for non-standard English explanation . |