Papers with domain adaptation methods
Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering (2024.eacl-srw)
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| Challenge: | Existing domain adaptation methods do not account for unseen natural distribution shifts. |
| Approach: | They perform experiments on 4 different datasets under varying amounts of distribution shift . they analyze how "in-the-wild" generation can help achieve domain generalization . |
| Outcome: | The proposed approach augments reading comprehension datasets with generated data to improve robustness towards natural distribution shifts. |
Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases (2023.findings-eacl)
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| Challenge: | Legal Judgment Prediction (LJP) is a classification task that uses textual descriptions of case facts as the input. |
| Approach: | They propose to use legal reasoning to map article text to specific case fact text to improve the model's generalization to zero-shot settings. |
| Outcome: | The proposed model outperforms straightforward fact classification and improves zero-shot transfer performance. |
Breaking Boundaries in Retrieval Systems: Unsupervised Domain Adaptation with Denoise-Finetuning (2023.findings-emnlp)
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| Challenge: | Existing domain adaptation methods for dense retrieval models use unadapted rerank models, leading to imprecise labels. |
| Approach: | They propose to adapt a rerank model to the target domain before using it for label generation. |
| Outcome: | The proposed model achieves better results across three retrieval datasets. |
Semi-supervised Stochastic Multi-Domain Learning using Variational Inference (P19-1)
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| Challenge: | Supervised NLP models rely on large collections of text which closely resemble intended testing setting. however, data is often messy, with domain labels not always available, or providing limited information about the style and genre of text. |
| Approach: | They propose a method to distill the important domain signal as part of a multi-domain learning system using a latent variable model. |
| Outcome: | The proposed model improves performance over benchmark domain adaptation methods . text corpora are often collated from several different sources, including news, literature, microblogs, and web crawls . |
Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word Embedding (2021.naacl-main)
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| 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. |
Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains (2020.findings-emnlp)
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| Challenge: | Understanding unexplored data is a slow process, and there is no labeled data at hand. |
| Approach: | They propose to use unsupervised methods to reveal rules which cluster unexplored corpus by its prominent categories to help domain experts understand their texts. |
| Outcome: | The proposed rules can be bootstrapped to identify target categories and deepen understanding of the data. |
Cross-Domain Evaluation of Edge Detection for Biomedical Event Extraction (2020.lrec-1)
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| Challenge: | Biomedical event extraction systems are evaluated in-domain and on complete event structures only. |
| Approach: | They present a cross-domain study of edge detection for biomedical event extraction . they analyze differences between five existing gold standard corpora and provide a strong baseline model . |
| Outcome: | The proposed model shows a drop in performance when the baseline is applied on out-of-domain data. |
Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios (2022.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) tasks require a large amount of training data and domains are often scarcely labeled. |
| Approach: | They propose a hardness-guided domain adaptation framework for bioNER tasks that leverages domain hardness information to improve the adaptability of the learnt model in low-resource scenarios. |
| Outcome: | The proposed model outperforms the state-of-the-art MetaNER model on biomedical datasets. |
Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging (D18-1)
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| Challenge: | Existing POS tagging methods for Twitter use labeled newswire text . however, Twitter users tend to mimic formal media expressions and develop linguistically informal styles. |
| Approach: | They propose to use newswire text to learn POS tagging for Twitter while twitter users are developing linguistically informal styles. |
| Outcome: | The proposed method achieves better performance than state-of-the-art methods on three different datasets. |
Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction (2022.naacl-main)
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| Challenge: | Existing approaches to perform aspect and opinion co-extraction are difficult due to the lack of fine-grained annotations. |
| Approach: | They propose a framework to transfer knowledge from a labeled source domain to an unlabeled target domain. |
| Outcome: | The proposed framework is more effective than previous domain adaptation methods on three datasets. |
Unsupervised Domain Adaptation for Event Detection using Domain-specific Adapters (2021.findings-acl)
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| Challenge: | Existing approaches to ED are limited due to complexity of textual data and domain shift problem. |
| Approach: | They propose to use domain adapter-based Adaptation framework to improve event detection across domains. |
| Outcome: | The proposed framework significantly boosts the performance on target domains. |
DAdEE: Unsupervised Domain Adaptation in Early Exit PLMs (2024.findings-emnlp)
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| 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. |
Cross-Domain Review Generation for Aspect-Based Sentiment Analysis (2021.findings-acl)
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| Challenge: | Existing domain adaptation methods for Aspect-Based Sentiment Analysis lack finegrained labeled data. |
| Approach: | They propose a new domain adaptation paradigm called cross-domain review generation which aims to generate target-domain reviews with fine-grained annotation based on the labeled source domain. |
| Outcome: | The proposed approach is superior to state-of-the-art domain adaptation methods. |
Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation (D19-1)
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| Challenge: | Existing domain adaptation methods focus on the adaptation from the source domain to the entire target domain without considering the diversity of individual sample samples. |
| Approach: | They propose a fine-grained knowledge fusion model with the domain relevance modeling scheme to control the balance between learning from the target domain data and learning from a source domain model. |
| Outcome: | The proposed model outperforms baselines and state-of-the-art models on three sequence labeling tasks. |
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition (2021.acl-long)
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| Challenge: | Experimental results show that crowdsourced annotations are highly effective under supervised conditions. |
| Approach: | They propose an annotator-aware representation learning model that is inspired by domain adaptation methods which attempt to capture effective domain-alike features. |
| Outcome: | The proposed model is highly effective on a benchmark dataset and achieves state-of-the-art performance with only a very small scale of expert annotations. |
AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization (2021.naacl-main)
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| Challenge: | State-of-the-art abstractive summarization models rely on extensive labeled data, which lowers their generalization ability on domains where such data are not available. |
| Approach: | They propose to use domain adaptation methods to simulate the low-resource domain adaptation setting for abstractive summarization systems with existing datasets across six diverse target domains. |
| Outcome: | The proposed model can be used to adapt to a low-resource domain adaptation setting. |
Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning (2022.coling-1)
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| Challenge: | Existing approaches to cross-domain text classification focus on one-to-one domain adaptation. |
| Approach: | They propose a framework for domain generalization that uses contrastive learning with a memory-saving queue. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on Amazon review sentiment datasets and rumour detection datasets. |
SplitThenMerge: Token-Level Skill-Compositional Sparse Mixture-of-Experts for Complex Domain-Specific Tasks (2026.findings-acl)
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| Challenge: | Existing domain adaptation methods train heterogeneous skills together, making it difficult to reliably coordinate multiple skills when solving complex tasks. |
| Approach: | They propose a framework that decomposes domain competence into atomic skills and composes them dynamically during generation. |
| Outcome: | The proposed framework decomposes domain competence into atomic skills, trains them independently, and composes them dynamically during generation. |
Semantic-conditioned Dual Adaptation for Cross-domain Query-based Visual Segmentation (2023.findings-acl)
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| Challenge: | Existing approaches to visual segmentation from language queries require expensive labeling and degradation when deployed to an unseen domain. |
| Approach: | They propose a task to adapt a visual segmentation model from a labeled domain to an unseen domain. |
| Outcome: | The proposed framework achieves precise feature- and relation-invariant across domains via universal semantic structure. |
Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis (2023.acl-long)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a task of analyzing people's sentiments at the aspect level. |
| Approach: | They propose a unified bidirectional generative framework to tackle cross-domain ABSA tasks . the framework trains a model in both text-to-label and label-totext directions . |
| Outcome: | The proposed framework trains a model in both label-to-label and label- to-text directions to learn domain-agnostic features. |
Reinforced IR: A Self-Boosting Framework For Domain-Adapted Information Retrieval (2025.acl-long)
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| Challenge: | Existing retrieval methods struggle with highly specialized situations that require extensive domain expertise. |
| Approach: | They propose a method that integrates additional information from an LLM-based generator to enhance query performance and train the retriever to better discriminate the relevant documents identified by the generator. |
| Outcome: | The proposed method outperforms existing domain adaptation methods by a large margin and leads to substantial improvements in retrieval quality across a wide range of application scenarios. |