Papers by Hiroki Iida
Unsupervised Domain Adaptation for Sparse Retrieval by Filling Vocabulary and Word Frequency Gaps (2022.aacl-main)
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| Challenge: | IR models with a pretrained language model outperform lexical approaches like BM25 for vocabulary mismatch. |
| Approach: | They propose an unsupervised domain adaptation method by filling vocabulary gaps by expanding queries and documents through an MLM. |
| Outcome: | The proposed method outperforms the current state-of-the-art domain adaptation method on datasets with a large vocabulary gap from a source domain. |