Papers with Unsupervised Domain Adaptation
UDALM: Unsupervised Domain Adaptation through Language Modeling (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing techniques for unsupervised domain adaptation (UDA) are limited by domain shift, which leads to performance degradation. |
| Approach: | They propose a fine-tuning procedure that uses a mixed classification and Masked Language Model loss to adapt to the target domain distribution in a robust and sample efficient manner. |
| Outcome: | The proposed procedure can adapt to the target domain distribution in a robust and sample efficient manner. |
Domain Confused Contrastive Learning for Unsupervised Domain Adaptation (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing studies on domain-shifting adaptations have focused on domain . |
| Approach: | They propose a self-supervised approach to unsupervised domain adduction using domain puzzles to bridge the source and target domains and retain discriminative representations after adaptation. |
| Outcome: | The proposed approach outperforms baselines and further ablation studies show that it is more stable and effective when performing other data augmentations. |
Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word Embedding (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent studies have classified dialectal Arabic into more fine-grained levels, including countries and cities. |
| Approach: | They propose to use Arabic domains to transfer knowledge from labeled source domains into unlabeled target domains by transferring the learned knowledge from a labele . |
| Outcome: | The proposed method outperforms other domain adaptation methods and improves performance by 20.8% over the zero-shot transfer learning from BERT. |
DAdEE: Unsupervised Domain Adaptation in Early Exit PLMs (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained Language Models (PLMs) exhibit good accuracy and generalization ability but their large size results in high inference latency. |
| Approach: | They propose an unsupervised domain adaptation framework that employs knowledge distillation to achieve domain-invariant representations at each layer. |
| Outcome: | The proposed framework outperforms early exit methods and domain adaptation methods under domain shift scenarios. |
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models have demonstrated their capability with few-shot inference . however, in-domain demonstrations are not always available in real scenarios . |
| Approach: | They propose unsupervised domain adaptation problem to adapt language models from source domain to target domain without any target labels. |
| Outcome: | The proposed model performs better than baseline models on Sentiment Analysis and Named Entity Recognition tasks. |
Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains. |
| Approach: | They propose a framework that allows to transfer the knowledge of source domain to the unlabeled target domain without using source data. |
| Outcome: | The proposed framework matches distributions between a trained source model and a set of target data and achieves superior performance on cross-domain text classification. |
Source-free Domain Adaptation for Aspect-based Sentiment Analysis (2024.lrec-main)
Copied to clipboard
| Challenge: | Unsupervised Domain Adaptation (UDA) of the Aspect-based Sentiment Analysis task is a data mining technique that involves aspect extraction and aspect sentiment classification subtasks. |
| Approach: | They propose a framework that allows model parameter transfer, not data transfer, between different domains. |
| Outcome: | The proposed framework performs competitively with traditional unsupervised domain adaptation methods under privacy conditions. |