Papers with Unsupervised domain adaptation
Simplified Neural Unsupervised Domain Adaptation (N19-1)
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| Challenge: | Existing unsupervised domain adaptation methods use neural networks to learn representations that are trained to predict the values of subset of important features called “pivot features.” |
| Approach: | They propose to combine the representation learner and task learner to improve on existing neural domain adaptation algorithms by removing heuristically-selected "pivot features" they show competitive performance with a simpler model. |
| Outcome: | The proposed model outperforms existing models by removing heuristically-selected pivot features. |
Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling (2020.findings-emnlp)
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Dejiao Zhang, Ramesh Nallapati, Henghui Zhu, Feng Nan, Cicero Nogueira dos Santos, Kathleen McKeown, Bing Xiang
| Challenge: | Existing approaches to learn a model from labeled data are expensive or prohibitive. |
| Approach: | They propose an unsupervised domain adaptation algorithm that leverages labeled data in a source domain to learn a well-performing model in . they use the Margin Disparity Discrepancy algorithm to optimize the margin loss on the source domain. |
| Outcome: | The proposed approach improves on a recent theoretical work on cross-lingual document classification and NER by a large margin. |
UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval (2026.findings-acl)
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| Challenge: | Existing methods focus on diversity but fail to capture model uncertainty. |
| Approach: | They propose a method to generalize neural retrievers to an unseen domain by generating pseudo queries on target domain documents. |
| Outcome: | The proposed method improves performance on large datasets with small and large models while limiting the learning utility of the current model. |