Papers by Koichi Takeda
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. |
Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals (2022.starsem-1)
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| Challenge: | Existing methods to derive sentence embeddings have not been well understood what properties are captured in the resulting sentences depending on the supervision signals. |
| Approach: | They propose to combine two types of sentence embedding methods with similar architectures and tasks to investigate their properties. |
| Outcome: | The proposed methods perform better on unsupervised and downstream tasks than the proposed methods on untrained STS tasks and probing tasks. |
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. |
Verifying Claims About Metaphors with Large-Scale Automatic Metaphor Identification (2024.naacl-short)
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| Challenge: | Existing studies on metaphors have focused on a small number of examples, whereas few studies verify claims with large corpus. |
| Approach: | They propose to use a large corpus to verify existing claims about verb metaphors . they apply metaphor detection to sentences extracted from Common Crawl . |
| Outcome: | The proposed method identifies verb metaphors with lower concreteness, imageability, familiarity and more emotional and subjective sentences. |
Cross-Modal Similarity-Based Curriculum Learning for Image Captioning (2022.emnlp-main)
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| Challenge: | Existing image captioning approaches treat image-caption pairs indistinctly without considering the differences in their learning difficulties. |
| Approach: | They propose a pretrained vision–language model that measures cross-modal similarity and a model that uses cross-module similarity to measure the difficulty of captioning. |
| Outcome: | The proposed model achieves superior performance and competitive convergence speed to baselines without incurring additional training costs. |
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. |
Automating Interlingual Homograph Recognition with Parallel Sentences (2022.findings-aacl)
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| Challenge: | Existing methods for interlingual homograph recognition require linguistic knowledge and massive annotation work. |
| Approach: | They propose an automatic interlingual homograph recognition method based on cross-lingual word embedding similarity and co-occurrence of form-identical words in parallel sentences. |
| Outcome: | The proposed method can make accurate predictions across languages. |
Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot Examples (2024.acl-srw)
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| Challenge: | Decoder-based large language models (LLMs) have shown high performance on many tasks in natural language processing. |
| Approach: | They propose to automatically generate an NLI dataset with an LLM and use it for fine-tuning of PromptEOL. |
| Outcome: | The proposed model outperforms existing models on STS tasks without large manually annotated datasets. |
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. |
Leveraging Three Types of Embeddings from Masked Language Models in Idiom Token Classification (2022.starsem-1)
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| Challenge: | Recent research shows that contextualized word embeddings can give promising results for idiom token classification. |
| Approach: | They propose to leverage contextualized word embeddings from masked language models to improve idiom token classification. |
| Outcome: | The proposed method improves idiom token classification for English and Japanese datasets. |
DefSent: Sentence Embeddings using Definition Sentences (2021.acl-short)
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| Challenge: | Sentence embedding methods using natural language inference datasets are limited for limited languages due to large datasets. |
| Approach: | They propose a sentence embedding method that uses definition sentences from a word dictionary. |
| Outcome: | The proposed method performs comparably on unsupervised semantics textual similarity tasks and slightly better on SentEval tasks than methods using large NLI datasets. |
Sentence Representations via Gaussian Embedding (2024.eacl-short)
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| Challenge: | Sentence embeddings represent a sentence's meaning as a point in a vector space and primarily use symmetric measures such as the cosine similarity to measure the similarity between sentences, they cannot capture asymmetric relationships between two sentences, such as entailment and hierarchical relations. |
| Approach: | They propose a Gaussian-distribution-based contrastive learning framework for sentence embedding that can handle asymmetric inter-sentential relations and a similarity measure for identifying entailment relations. |
| Outcome: | The proposed framework performs comparable to that of previous methods on natural language inference tasks and estimates direction of entailment relations, which is difficult with point representations. |
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. |
Definition Generation for Automatically Induced Semantic Frame (2024.findings-acl)
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| Challenge: | Semantic frames are conceptual structures that describe specific types of situations or events. |
| Approach: | They propose to generate frame definitions from a set of frame-evoking words using a large language model. |
| Outcome: | The proposed task incorporates frame element reasoning as chain-of-thought to enhance the inclusion of correct frame elements in the generated definitions. |
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. |
Development of a Medical Incident Report Corpus with Intention and Factuality Annotation (2020.lrec-1)
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| Challenge: | Medical incident reports are documents that record what happened in a medical incident. |
| Approach: | They propose to annotate medical incident reports with annotations of intention and factuality and medication entities and their relations. |
| Outcome: | The proposed method combines the definition of medication entities and the method to annotate the relations between entities and extracts important information from the unstructured part. |
Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMs (2024.findings-acl)
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| Challenge: | Neural machine translation (NMT) systems do not take into account the complexity of the words used to compose the translations. |
| Approach: | They propose a method that replaces high Age of Acquisitions words in translations with simpler words to match the user’s level. |
| Outcome: | The proposed method replaces high-AoA words with lower-Aa words while maintaining high BLEU and COMET scores. |
Realistic Citation Count Prediction Task for Newly Published Papers (2023.findings-eacl)
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| Challenge: | Existing studies on citation count prediction assume that future citation counts of academic papers have not had enough time pass since publication. |
| Approach: | They propose to use citation counts of newly published papers as a realistic citation count prediction task and to use them to leverage the citations of papers shortly after publication. |
| Outcome: | The proposed methods significantly improve the performance of citation count prediction for newly published papers in a realistic setting. |
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 . |