Papers by Sora Ohashi
Text Classification with Negative Supervision (2020.acl-main)
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| Challenge: | Existing models for text representations have shown state-of-the-art performance on text classification tasks, however, the discrepancy between semantic similarity of texts and labelling standards affects classifiers. |
| Approach: | They propose a simple multitask learning model that uses negative supervision to generate distinct representations for texts with different labels. |
| Outcome: | The proposed model outperforms state-of-the-art models on classification tasks in three different languages. |
Distinct Label Representations for Few-Shot Text Classification (2021.acl-short)
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| Challenge: | Existing methods for few-shot text classification ignore the semantic relevance of labels and are difficult to train because of the lack of training examples. |
| Approach: | They propose a method that generates distinct label representations that embed information specific to each label. |
| Outcome: | The proposed method significantly improves few-shot text classification across models and datasets. |
Tiny Word Embeddings Using Globally Informed Reconstruction (2020.coling-main)
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| Challenge: | Existing methods for word embedding reconstruction use only local information of subwords and pre-trained word embeds. |
| Approach: | They propose a global loss function that uses words other than the target word to improve word embedding reconstruction by a factor of 200. |
| Outcome: | The proposed method reduces the model size of pre-trained word embeddings by a factor of 200 while preserving its quality. |