Papers by Ryo Takahashi
Modeling Event Salience in Narratives via Barthes’ Cardinal Functions (2020.coling-main)
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| Challenge: | Existing methods for estimating event salience without annotations are prohibitively costly because they require annotators to understand the concept of event salientity. |
| Approach: | They propose to use Barthes’ definition of event salience to compute event salientity without annotations by using a pre-trained language model. |
| Outcome: | The proposed methods outperform baseline methods on folktales with event salience annotation and fine-tuned language model is key factor in improving the methods. |
Are Prompt-based Models Clueless? (2022.acl-long)
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| Challenge: | Prompting has reduced the data requirement by reusing the language model head and formatting the task input to match the pre-training objective. |
| Approach: | They propose to examine whether few-shot prompt-based models exploit superficial cues by reusing the model head and formatting the input to match the pre-training objective. |
| Outcome: | The proposed models perform well on instances with superficial cues, but often outperform random accuracy on instances without superficial cuing. |
Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder (P18-1)
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| Challenge: | Embedding models for entities and relations are useful for recovering missing facts in knowledge bases. |
| Approach: | They propose a dimension reduction technique by training relations jointly with an autoencoder to capture compositional constraints. |
| Outcome: | The proposed model improves on Knowledge Base Completion tasks with a significantly higher mean rank and better compositional training. |
Two Training Strategies for Improving Relation Extraction over Universal Graph (2021.eacl-main)
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| Challenge: | Existing methods for Distantly Supervised Relation Extraction (DS-RE) with a UG may lead to degradation in performance. |
| Approach: | They propose to use a Universal Graph (UG) to train a distantly supervised relation extraction model. |
| Outcome: | The proposed training strategies on biomedical and NYT10 datasets prove the robustness of the proposed methods and achieve state-of-the-art results. |
Word Rotator’s Distance (2020.emnlp-main)
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| Challenge: | Existing approaches to measure textual similarity are inconsistent with the word alignment and are empirically inferior to the simple cosine similarity between general-purpose sentence vectors. |
| Approach: | They propose to decouple word vectors into their norm and direction and then grow the norm and directions of word vector. |
| Outcome: | The proposed methods outperform alignment-based approaches on several benchmarks and strong baselines on the semantic textual similarity task. |
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution (2020.coling-main)
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| Challenge: | Existing methods to augment labeled data are limited by the scarcity of labeles . a method called contextual data augmentation (CDA) can be used to augment labels . |
| Approach: | They propose a data augmentation method that generates labeled training instances using a pretrained language model. |
| Outcome: | The proposed method can improve the quality of augmented training data compared to the conventional method. |