Papers by Masashi Toyoda
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. |
Query-Focused Individual Simulation with Progressive Persona Completion (2026.findings-acl)
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| Challenge: | Existing approaches to simulating individual responses from persona information assume rich persona profiles, which are often unavailable in practice. |
| Approach: | They propose a query-focused individual simulation where relevant persona information is identified and requested on demand for each query. |
| Outcome: | Experiments on two dialogue datasets show that the proposed method achieves comparable performance to approaches that rely on rich persona information extracted from dialogue history. |
Fine-grained Typing of Emerging Entities in Microblogs (2021.findings-emnlp)
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| Challenge: | Graus et al., 2018) defined emerging entities as those that appear in contexts that emphasize their novelty, and attempted to discover emerging entities from microblogs. |
| Approach: | They propose a task that assigns a fine-grained type to each emerging entity when a burst of posts containing that entity is first observed in a microblog. |
| Outcome: | The proposed model can type 'homographic' emerging entities without relying on prior knowledge of the target entity. |
Is He Extroverted? Identifying Missing Relevant Personas for Faithful User Simulation (2026.eacl-srw)
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| Challenge: | Existing user simulation approaches focus on generating user-like responses in dialogue without verifying whether critical personas are supplied. |
| Approach: | They propose a task of identifying persona dimensions that are relevant but missing in simulating a user's reply for a given dialogue context. |
| Outcome: | The proposed model identifies persona dimensions that are relevant but missing in simulating a user’s response for a given dialogue context. |
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. |
Entity Embedding Completion for Wide-Coverage Entity Disambiguation (2022.findings-emnlp)
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| Challenge: | Existing state-of-the-art ED models do not address out-of vocabulary entities that are absent from training data. |
| Approach: | They propose to extend a state-of-the-art ED model by dynamically computing embeddings of out-ofvocabulary entities by using entity descriptions and mention contexts. |
| Outcome: | The proposed model performs comparable to existing models whose embeddings are trained for all candidate entities as well as embedd-free models. |
Early Discovery of Disappearing Entities in Microblogs (2023.acl-long)
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| Challenge: | a study on detecting disappearing entities from noisy microblogs has been published on the real world . a major challenge is detecting uncertain contexts of disappearing entity from noisy posts . |
| Approach: | They propose to use Twitter to detect disappearing entities from noisy microblogs . they build large-scale Twitter datasets of disappearing entity and refine word embeddings based on these data . |
| Outcome: | The proposed method outperforms baseline methods on noisy microblog streams and more than 70% of disappearing entities in Wikipedia are discovered earlier than the update on Wikipedia. |
uBLEU: Uncertainty-Aware Automatic Evaluation Method for Open-Domain Dialogue Systems (2020.acl-srw)
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| Challenge: | Existing evaluation metrics for text generation tasks do not consider uncertain responses without writing additional reference responses by hand. |
| Approach: | They propose a human-aided, uncertainty-aware evaluation method for open-domain dialogue systems, BLEU. |
| Outcome: | The proposed method is comparable to existing methods on Twitter and improves state-of-the-art evaluation method RUBER. |
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 . |