Papers by Kosuke Yamada
FrameEOL: Semantic Frame Induction using Causal Language Models (2025.findings-emnlp)
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| Challenge: | Semantic frame induction is the task of clustering frame-evoking words according to the semantic frames they evoke. |
| Approach: | They propose a prompt-based method for obtaining Frame Embeddings that outputs One frame-name as a Label . |
| Outcome: | The proposed method outperforms existing methods on English and Japanese datasets. |
Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction (2023.findings-acl)
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| Challenge: | Existing methods for semantic frame induction are labor intensive . a method that uses contextualized embeddings can be used to acquire frame element knowledge. |
| Approach: | They propose a method that applies deep metric learning to semantic frame induction tasks . they use a pre-trained language model to fine-tune frame-annotated models to perform argument clustering . |
| Outcome: | The proposed method achieves substantially better performance than existing methods on FrameNet. |
Transformer-based Live Update Generation for Soccer Matches from Microblog Posts (2023.emnlp-main)
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| Challenge: | Existing systems to generate sports updates from tweets are not able to handle vast amounts of diverse tweets, and this remains a challenge for future studies. |
| Approach: | They propose to generate live updates for soccer matches from tweets using a large pre-trained language model and incorporate a classifier to control the number of updates and a mechanism to reduce redundancy of duplicate and similar updates. |
| Outcome: | The proposed system can generate live updates for soccer matches from tweets and achieve high performance by considering preceding updates. |
Building a Buzzer-quiz Answering System (2023.acl-srw)
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| Challenge: | A buzzer quiz is a genre of quiz in which multiple players simultaneously listen to a quiz being read aloud and respond it by buzzing in as soon as they can predict the answer. |
| Approach: | They propose two types of buzzer-quiz answering systems: a system that directly generates an answer from part of a question by using an autoregressive language model and a second system that reconstructs the entire question by applying an autoreregressively language model. |
| Outcome: | The proposed system estimates the accuracy of the answers by using the internal scores of each model. |
Semantic Frame Induction from a Real-World Corpus (2025.acl-srw)
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| Challenge: | Existing studies on semantic frame induction have demonstrated that pre-trained language models (PLMs) have led to more accurate results. |
| Approach: | They conduct semantic frame induction using the Colossal Clean Crawled Corpus and assess the applicability of existing frame inducing methods to real-world data. |
| Outcome: | The proposed methods outperform existing methods on real-world data and can induce frames corresponding to novel concepts. |
Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts (2020.findings-emnlp)
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| Challenge: | Existing methods for dividing biomedical abstracts into rhetorical segments assign a rhetorical label to each sentence while considering context in the abstract. |
| Approach: | They propose to use Neural Semi-Markov Conditional Random Fields to assign a rhetorical label to a span that consists of continuous sentences. |
| Outcome: | The proposed method achieved the best micro sentence-F1 score and the best macro span-F1. |
Verb Sense Clustering using Contextualized Word Representations for Semantic Frame Induction (2021.findings-acl)
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| Challenge: | Contextualized word representations are effective in many natural language processing tasks, but it remains unclear to what extent they can cover hand-coded semantic information such as semantic frames. |
| Approach: | They compare contextualized word representations with two English frame-semantic resources . they find that several contextualized representations are informative for semantic frame induction . |
| Outcome: | The proposed representations are useful in natural language processing tasks, but are not fully understood by the literature. |
Semantic Frame Induction with Deep Metric Learning (2023.eacl-main)
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| Challenge: | Recent studies have shown the usefulness of contextualized word embeddings in semantic frame induction, but they are not always consistent with human intuitions about semantic frames. |
| Approach: | They propose a model that fine-tunes contextualized embeddings to perform semantic frame induction. |
| Outcome: | The proposed model improves clustering evaluation scores on FrameNet by 8 points or more. |
Transformer-based Lexically Constrained Headline Generation (2021.emnlp-main)
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Kosuke Yamada, Yuta Hitomi, Hideaki Tamori, Ryohei Sasano, Naoaki Okazaki, Kentaro Inui, Koichi Takeda
| Challenge: | Existing automatic headline generation methods cannot include a given phrase in the generated headline. |
| Approach: | They propose a Transformer-based method that guarantees to include a given phrase in a generated headline. |
| Outcome: | The proposed method achieves ROUGE scores comparable to previous methods with Japanese news corpus. |
Incorporating Textual Information on User Behavior for Personality Prediction (P19-2)
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| Challenge: | Recent studies have shown that textual information of user posts and user behaviors are useful for predicting the personality of social media users. |
| Approach: | They propose to use textual information of user behaviors to predict personality of Twitter users by taking user behaviors into account. |
| Outcome: | The proposed models can predict personality of users who do not post frequently, while taking user behaviors into account. |
Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering (2021.acl-short)
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| Challenge: | Recent studies show that clustering-based methods focus too much on the surface information of frame-evoking verbs and divide instances of the same verb into too many different frame clusters. |
| Approach: | They propose a semantic frame induction method using masked word embeddings and two-step clustering to overcome these drawbacks. |
| Outcome: | The proposed method reduces the number of instances of the same verb into too many clusters . it uses masked word embeddings and two-step clustering to avoid drawbacks compared with other methods . |