Papers with domain adaptation method
Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation (P19-1)
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| Challenge: | Existing methods for Combinatory Categorial Grammar (CCG) parsing are limited to a specific parser architecture, making it non-trivial to apply to current parsers. |
| Approach: | They propose a domain adaptation method for Combinatory Categorial Grammar (CCG) they propose to generate CCG corpora using cheaper dependency trees. |
| Outcome: | The proposed method improves on speech conversation and math problems. |
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. |
Stanceosaurus: Classifying Stance Towards Multicultural Misinformation (2022.emnlp-main)
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| Challenge: | Existing corpora focus on misinformation spreading within western countries. |
| Approach: | They present a new corpus of tweets annotated with stance towards 250 misinformation claims. |
| Outcome: | The proposed method achieves 53.1 F1 on Hindi and 50.4 F1 in Arabic without any target-language fine-tuning. |
Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble (2020.emnlp-main)
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Peerat Limkonchotiwat, Wannaphong Phatthiyaphaibun, Raheem Sarwar, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Thai word segmentation is domain-dependent, and researchers have been relying on transfer learning to adapt existing models to new domains. |
| Approach: | They propose a filter-and-refine solution to address Thai word segmentation as a domain-dependent problem. |
| Outcome: | The proposed method is an effective domain adaptation method and has similar performance as the transfer learning method. |
Domain Adaptation for Sentiment Analysis Using Robust Internal Representations (2023.findings-emnlp)
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| Challenge: | Cross-domain sentiment analysis methods reduce the domain gap by training generalizable classifiers for each domain . large interclass margins in source domain help to reduce the effect of "domain shift" in the target domain. |
| Approach: | They propose a domain adaptation method which induces large margins between data representations that belong to different classes in an embedding space. |
| Outcome: | The proposed method reduces the domain gap by training cross-domain generalizable classifiers . large interclass margins in the source domain help reduce the effect of "domain shift" the proposed method is available in the u.s. |