Papers with domain adaptation techniques
Biomedical Relation Classification by single and multiple source domain adaptation (D19-62)
Copied to clipboard
| Challenge: | Existing supervised systems are highly data-driven and require a lot of effort to label data for a new domain. |
| Approach: | They propose to transfer knowledge from one or more related source domains to another domain to improve relation classification. |
| Outcome: | The proposed model outperforms neural-network based models on biomedical datasets and with contextualized embeddings on 3 biomedically-relevant datasets. |
What’s in a Domain? Learning Domain-Robust Text Representations using Adversarial Training (N18-2)
Copied to clipboard
| Challenge: | a key roadblock is application to new domains, unseen in training. |
| Approach: | They propose a method to optimise in- and out-of-domain accuracy by combing domain-specific and domain-general components with adversarial training for domain. |
| Outcome: | The proposed method improves on domain adaptation and domain-adversarial training. |
A Survey of Domain Adaptation for Neural Machine Translation (C18-1)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) is a deep learning based approach for machine translation. |
| Approach: | They propose to use a deep learning approach to train machine translation in scenarios where large-scale parallel corpora are available. |
| Outcome: | The proposed approach yields the state-of-the-art translation performance in resource rich scenarios. |
Zero-Shot Cross-Domain Aspect-Based Sentiment Analysis via Domain-Contextualized Chain-of-Thought Reasoning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Cross-domain aspect-based sentiment analysis (ABSA) aims to learn specific knowledge from a source domain to perform various tasks on a target domain. |
| Approach: | a new framework is proposed to learn specific knowledge from a source domain . the framework uses domain adaptation techniques to transfer domain-agnostic features . |
| Outcome: | a new learning framework for cross-domain aspect-based sentiment analysis is proposed . it effectively eliminates dependency on target-domain annotations, authors say . |
Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP (2023.findings-emnlp)
Copied to clipboard
Hyuhng Kim, Hyunsoo Cho, Sang-Woo Lee, Junyeob Kim, Choonghyun Park, Sang-goo Lee, Kang Yoo, Taeuk Kim
| Challenge: | Despite advances in computer vision, its application on language input still needs to be explored despite its feasibility. |
| Approach: | They propose a universal domain adaptation (uniDA) benchmark for natural language that offers thorough viewpoints of the model’s generalizability and robustness. |
| Outcome: | The proposed model can handle spoken language in the real world while also detecting unprocessable inputs from the target domain. |
TOP-Training: Target-Oriented Pretraining for Medical Extractive Question Answering (2025.coling-main)
Copied to clipboard
| Challenge: | e-health records underscore the growing significance of information extraction (IE) from these datasets. |
| Approach: | They propose a target-oriented pre-training paradigm for extractive question-answering in the medical domain . TOP-Training moves one step further than popular domain-oriented fine-tuning . |
| Outcome: | The proposed method improves on the Medical-EQA benchmarks. |
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing (2020.emnlp-main)
Copied to clipboard
| Challenge: | Knowledge graphs (KGs) vary greatly from one domain to another, resulting in a lack of domain-specific parallel graph-text data. |
| Approach: | They propose an unsupervised approach to graph-to-text generation and text-to graph knowledge extraction using WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome. |
| Outcome: | The proposed approach outperforms baselines on WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome. |
Fact or Fiction: Verifying Scientific Claims (2020.emnlp-main)
Copied to clipboard
David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi
| Challenge: | SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts annotated with labels and rationales. |
| Approach: | They construct a dataset of 1.4K scientific claims paired with evidence-containing abstracts annotated with labels and rationales to test their system. |
| Outcome: | The proposed system can verify claims related to COVID-19 by identifying evidence from the CORD-19 corpus. |
Cross-Domain Label-Adaptive Stance Detection (2021.emnlp-main)
Copied to clipboard
| Challenge: | Stance detection is a task that focuses on the classification of a writer’s viewpoint towards a target. |
| Approach: | They propose an end-to-end unsupervised framework for out-of-domain prediction of unseen, user-defined labels. |
| Outcome: | The proposed framework shows that it can be used to predict unseen labels over strong baselines. |