Papers by Satoshi Akasaki
Conversation Initiation by Diverse News Contents Introduction (N19-1)
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| Challenge: | Existing conversation systems assume that the user always initiates conversation and focus on how to respond to the given user’s utterance. |
| Approach: | They propose to generate initial utterance by summarizing and chatting about news articles to avoid boredom by relying on boilerplate utterrances like greetings. |
| Outcome: | The proposed model outperforms baseline models and based on information retrieval based and generation based models. |
Predicting Cross-lingual Trends in Microblogs (2025.emnlp-industry)
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| Challenge: | Existing trend prediction methods only make predictions within a language, but this is not enough to predict cross-lingual trends. |
| Approach: | They propose a method to predict which microblog trends will cross linguistic boundaries to become popular in other languages and when. |
| Outcome: | The proposed model outperforms existing trend prediction methods and LLM-based approaches by 4% in F1-score . |
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. |
Detecting Ambiguous Utterances in an Intelligent Assistant (2024.emnlp-industry)
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| Challenge: | ambiguous utterances can be interpreted as either chat or task intents in intelligent assistants . ambiguity of intent is particularly noticeable in intelligent devices where task-oriented and non-task-oriented utterrances are mixed and most utterations are short due to characteristics of devices. |
| Approach: | They propose to feed sentence embeddings developed from microblogs and search logs with a self-attention mechanism to detect ambiguous utterances robustly. |
| Outcome: | The proposed model outperforms baselines and a strong LLM-based model. |
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. |
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. |