Papers by Hiyori Yoshikawa
Selective-LAMA: Selective Prediction for Confidence-Aware Evaluation of Language Models (2023.findings-eacl)
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| Challenge: | Recent studies suggest that neural language models learn and store a large amount of facts and commonsense knowledge from training data. |
| Approach: | They propose a benchmark task that evaluates the amount of relational knowledge stored in pre-trained language models. |
| Outcome: | The proposed evaluations show that the selection of confidence functions is more robust to simple guesses than the accuracy-based evaluation. |
On the (In)Effectiveness of Images for Text Classification (2021.eacl-main)
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| Challenge: | Existing studies have focused on text classification, but have shown that images do not improve NLP tasks. |
| Approach: | They focus on text classification, where images complement the text and the Wikipedia page can be in one of a number of different languages. |
| Outcome: | The proposed model trains without external pre-training, but when combined with BERT models pre-trained on large-scale external data, images contribute nothing. |