DOMAIN ADAPTATION

A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors (P18-1)

Challenge:	Retrieval Augment Generation (RAG) has been developed for use in open-domain question answering (ODQA) but it is not optimized for use with other specialized domains such as healthcare and news.
Approach:	They propose an extension to RAG that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training.
Outcome:	The proposed extension can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training.

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Mikhail Khodak, Nikunj Saunshi, Yingyu Liang, Tengyu Ma, Brandon Stewart, Sanjeev Arora

Evaluating Domain Adaptation for Machine Translation Across Scenarios (L18-1)

Challenge:	Existing word2vec-based methods for learning rare or unseen words have been criticized for degrading performance in small corpus settings.
Approach:	They propose a la carte embedding method that relies on a linear transformation that is efficiently learnable using pretrained word vectors and linear regression.
Outcome:	The proposed method is based on a new dataset showing that it can be used when a word is encountered even if only a single usage example is available.

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Thierry Etchegoyhen, Anna Fernández Torné, Andoni Azpeitia, Eva Martínez Garcia, Anna Matamala

Multi-Domain Multilingual Question Answering (2021.emnlp-tutorials)

Challenge:	Statistical machine translation (SMT) has been the dominant approach for the last 20 years, with neural machine translation becoming the new main paradigm in academic research and the industry.
Approach:	They propose to compare domain-adapted statistical and neural machine translation systems on three different domains and language pairs with varying degrees of domain specificity and available training data.
Outcome:	The proposed system is the best choice for translation, with marked impacts for domains with higher specificity.

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Sebastian Ruder, Avi Sil

Accelerating LLM Fine-Tuning via Embedding Knowledge Transfer (2026.findings-acl)

Challenge:	Question answering (QA) is one of the most challenging tasks in natural language processing.
Approach:	a tutorial examines the state-of-the-art approaches to multi-domain and multilingual QA . they introduce standard benchmarks and discuss out-of the-box training with open-domain QA systems .
Outcome:	This tutorial aims to bridge the gap between open-domain and multilingual QA.

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Meishu Peng, Ziyue Zhang, Yi Zhang, Pengyang Wang, Zixuan Yuan, Denghui Zhang

Domain Differential Adaptation for Neural Machine Translation (D19-56)

Challenge:	Existing studies on parameter-efficient fine-tuning (PEFT) have produced many state-of-the-art results by adapting LLMs to new tasks, but it requires substantial training data and time to enhance model performance.
Approach:	They propose a parameter-efficient fine-tuning framework which efficiently transfers knowledge from a small expert model to a target large model via embedding layers.
Outcome:	The proposed framework accelerates domain-specific fine-tuning, improves model performance and remains robust across diverse model families and PEFT methods.

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Zi-Yi Dou, Xinyi Wang, Junjie Hu, Graham Neubig

Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification (2022.acl-long)

Challenge:	Neural networks are data hungry and domain sensitive, so it is difficult to obtain labeled data for every domain.
Approach:	They propose a framework for domain adaptation where we model the difference between domains instead of smoothing over them.
Outcome:	The proposed framework improves on domain adaptation in multiple experimental settings.

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George-Eduard Zaharia, Răzvan-Alexandru Smădu, Dumitru Cercel, Mihai Dascalu

Domain Adaptation with BERT-based Domain Classification and Data Selection (D19-61)

Challenge:	Existing datasets for complex word identification (CWI) are limited and the difficulty of the task is augmented by the scarcity of input examples.
Approach:	They propose a novel training technique for the complex word identification task based on domain adaptation to improve character and context representations.
Outcome:	The proposed training technique improves the target character and context representations and also smooths differences between datasets.

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Xiaofei Ma, Peng Xu, Zhiguo Wang, Ramesh Nallapati, Bing Xiang

How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation? (2021.acl-short)

Challenge:	Modern deep neural models with millions of parameters can easily adapt to a new learning task and dataset when enough supervision is given.
Approach:	They propose a domain adaptation framework based on curriculum learning and domain-discriminative data selection.
Outcome:	The proposed framework outperforms discrepancy-based methods on transfer tasks while consuming only fraction of training budget.

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Sayan Ghosh, Zheng Qi, Snigdha Chaturvedi, Shashank Srivastava

Multi-layer Annotation of the Rigveda (L18-1)

Challenge:	Existing approaches to Table-to-Text generation suffer from issues such as missing information, repetition and repetition.
Approach:	They propose to use Inverse Reinforcement Learning (IRL) to solve the Table-to-Text task . they use multiple interpretable unsupervised reward components that are combined linearly to form a composite reward function.
Outcome:	The proposed task outperforms strong RL baselines marginally in the Table-to-Text task.

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Oliver Hellwig, Heinrich Hettrich, Ashutosh Modi, Manfred Pinkal

Challenge:	Using a multi-level annotation, we present a corpus of the R. GVEDA .
Approach:	They propose a multi-level annotation of the R . GVEDA, a Sanskrit text composed in the 2. millenium BCE, and a basic argument identification algorithm to supplement missing verb-argument links.
Outcome:	The proposed model replaces verb-argument links by LSTM based model . the proposed model is based on a LS-based model to supplement missing verb-al arguments.

Tulun: Transparent and Adaptable Low-resource Machine Translation (2025.acl-demo)

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Raphael Merx, Hanna Suominen, Lois Yinghui Hong, Nick Thieberger, Trevor Cohn, Ekaterina Vylomova

Transferable Dialogue Systems and User Simulators (2021.acl-long)

Challenge:	a low-resource language that is the lingua franca in Timor-Leste lacks available corpora in the health domain.
Approach:	They propose a solution that combines neural MT with large language model-based post-editing guided by existing glossaries and translation memories.
Outcome:	The proposed system outperforms both standalone MT and LLM approaches across six low-resource languages on the FLORES dataset.

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Bo-Hsiang Tseng, Yinpei Dai, Florian Kreyssig, Bill Byrne

A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy (P19-1)

Challenge:	a lack of training data is limiting the development of dialogue systems . we develop a framework for creating dialogue data through self-play between agents .
Approach:	They propose a framework that can incorporate new dialogue scenarios through self-play between two agents.
Outcome:	The proposed framework is highly effective in bootstrapping the performance of two agents in transfer learning.

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Genady Beryozkin, Yoel Drori, Oren Gilon, Tzvika Hartman, Idan Szpektor

Challenge:	Named-entity recognition (NER) has seen significant progress with the application of Neural Networks to the task.
Approach:	They propose to use a given tag hierarchy to jointly learn a neural network that shares its tagging layer among all tag-sets.
Outcome:	The proposed model outperforms models that combine independent models and multitasking approaches in a domain adaptation for named-entity recognition task.

Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness (D19-56)

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Alexandre Berard, Ioan Calapodescu, Marc Dymetman, Claude Roux, Jean-Luc Meunier, Vassilina Nikoulina

On Domain-Adaptive Post-Training for Multimodal Large Language Models (2025.findings-emnlp)

Challenge:	BLEU: MT is a very robust and efficient way to translate user-generated content.
Approach:	They propose a task to encourage research on MT robustness and domain adaptation . they ask professionals to translate 11.5k french 4SQ reviews to English .
Outcome:	The proposed task improves on the existing MT systems in a real-world scenario . the proposed methods improve translation accuracy and sentiment analysis .

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Daixuan Cheng, Shaohan Huang, Ziyu Zhu, Xintong Zhang, Xin Zhao, Zhongzhi Luan, Bo Dai, Zhenliang Zhang

Can Continual Pretraining Bridge the Performance Gap between General-purpose and Specialized Language Models in the Medical Domain? (2026.acl-long)

Challenge:	Adapting general multimodal large language models to specific domains is important for practical applications.
Approach:	They investigate domain adaptation of multimodal large language models via post-training . they develop a generate-then-filter pipeline that curates diverse visual instruction tasks .
Outcome:	The proposed model outperforms existing models in domain adaptation by combining data from open-source models with training pipelines.

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Niclas Doll, Jasper Schulze Buschhoff, Shalaka Satheesh, Hammam Abdelwahab, Héctor Allende-Cid, Katrin Klug

DILBERT: Customized Pre-Training for Domain Adaptation with Category Shift, with an Application to Aspect Extraction (2021.emnlp-main)

Challenge:	specialized models have a large potential for translation and translation, but they lack the integration of domainspecific knowledge and terminology into clinical workflows.
Approach:	They construct a German medical corpus to continuously pre-train and merge three well-known LLMs and use it to improve model performance.
Outcome:	The proposed model family significantly outperforms the mistral-Small-24B-Instruct model family on German medical benchmarks.

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Entony Lekhtman, Yftah Ziser, Roi Reichart

Sentiment Analysis

Challenge:	Existing methods for pre-training can be sub-optimal in some cases . for example, aspect extraction tasks require domain and category invariant representations .
Approach:	They propose a domain-invariant learning scheme for BERT to fine-tune pre-trained language models on a source domain and then apply it to a different target domain.
Outcome:	The proposed scheme improves performance over state-of-the-art models while using fraction of the unlabeled data.

OpusFilter: A Configurable Parallel Corpus Filtering Toolbox (2020.acl-demos)

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Mikko Aulamo, Sami Virpioja, Jörg Tiedemann

Non-Parametric Domain Adaptation for End-to-End Speech Translation (2022.emnlp-main)

Challenge:	OpusFilter is a toolbox for filtering parallel corpora using noisy training data.
Approach:	They propose a toolbox for filtering parallel corpora with heuristic filters, language identification libraries, character-based language models and word alignment tools.
Outcome:	The proposed tool outperforms a similar tool on a Finnish-English news translation task using noisy web crawls.

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Yichao Du, Weizhi Wang, Zhirui Zhang, Boxing Chen, Tong Xu, Jun Xie, Enhong Chen

NeuroX Library for Neuron Analysis of Deep NLP Models (2023.acl-demo)

Challenge:	End-to-end speech translation (E2E-ST) systems have received increasing attention due to its less error propagation, lower latency and fewer parameters.
Approach:	They propose a non-parametric method that leverages in-domain text translation corpus to achieve domain adaptation for E2E-ST systems.
Outcome:	The proposed method outperforms the existing in-domain fine-tuning strategies on the Europarl-ST benchmark.

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Fahim Dalvi, Hassan Sajjad, Nadir Durrani

Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis (2020.coling-main)

Challenge:	NeuroX is an open-source toolkit to conduct neuron analysis of natural language processing models.
Approach:	They propose a Python toolkit to conduct neuron analysis of natural language processing models.
Outcome:	a new open-source toolkit enables neuron analysis of natural language processing models . the framework provides a framework for data processing and evaluation, making it easier for researchers and practitioners to perform neuron analyses.

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Minlong Peng, Qi Zhang

Representation Learning

Challenge:	Cross-domain sentiment analysis is a hot topic in research and industry . domain-invariant representation learning (DIRL) is used to learn a feature representation across domains . but, when label distribution P(Y) shifts across domain, it degrades performance .
Approach:	They propose a domain-invariant representation learning framework to improve cross-domain sentiment analysis performance.
Outcome:	The proposed model is easy to transfer existing models to the proposed model.

No Label? No Problem: Unsupervised Continual Learning for Adaptive Medical ASR (2026.eacl-industry)

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Meizhu Liu, Tao Sheng

Multimodality

Challenge:	Medical audio often contains specialized terminology, such as medication names, which existing ASR systems struggle to transcribe accurately.
Approach:	They propose an unsupervised continual learning ASR framework that adapts to new data while preserving prior knowledge.
Outcome:	Experiments on real-world medical audio show that the proposed framework improves over state-of-the-art models.

A Simple Yet Effective Hybrid Pre-trained Language Model for Unsupervised Sentence Acceptability Prediction (2022.aacl-short)

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Yang Zhao, Issei Yoshida

T2NER: Transformers based Transfer Learning Framework for Named Entity Recognition (2021.eacl-demos)

Challenge:	Existing unsupervised prediction approaches rely on language models to estimate sentence acceptability . low-frequency words would have a significant negative impact on sentence likelihood .
Approach:	They propose a method that substitutes Part-of-Speech (POS) tags for low-frequency words in sentences . their method improves both a sentence acceptability benchmark and a cross-domain sentence evaluation corpus .
Outcome:	The proposed method improves on a sentence acceptability benchmark and a cross-domain sentence evaluation corpus.

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Saadullah Amin, Guenter Neumann

Adaptor: Objective-Centric Adaptation Framework for Language Models (2022.acl-demo)

Challenge:	Named entity recognition (NER) is an important task in information extraction due to large variations in entity names and flexibility in how entities are mentioned.
Approach:	They propose a Transformers based Transfer Learning framework for Named Entity Recognition (T2NER) that integrates transformer models with the state-of-the-art in NLP and provides a unified platform for transfer learning.
Outcome:	The proposed framework bridges the gap between the state-of-the-art in transformer models and the state of the art in NER with deep transformer models.

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Michal Štefánik, Vít Novotný, Nikola Groverová, Petr Sojka

Continually Improving Extractive QA via Human Feedback (2023.emnlp-main)

Challenge:	Adaptor library aims to simplify complex training processes requiring customizations.
Approach:	They introduce Adaptor library which transposes traditional model-centric approach to objective-centric training pipeline with Objective as central abstraction.
Outcome:	The proposed framework simplifies training processes and improves reproducibility.

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Ge Gao, Hung-Ting Chen, Yoav Artzi, Eunsol Choi

Bridge-Based Active Domain Adaptation for Aspect Term Extraction (2021.acl-long)

Challenge:	a study of extractive question answering systems using human feedback shows promising potential for continual learning.
Approach:	They study extractive question answering system by using user feedback to improve it . they design and deploy an iterative approach where users ask questions and provide feedback .
Outcome:	The proposed model improves over time across different data regimes and domains . human user feedback is more affordable and abundant than annotations provided by trained experts .

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Zhuang Chen, Tieyun Qian

Sentiment Analysis

Challenge:	Existing methods to transfer aspect terms are limited because they require labeled pivot words or expensive computing resources.
Approach:	They propose a method that actively supplements transferable knowledge by recognizing syntactic roles as pivots instead of links to pivots.
Outcome:	The proposed method significantly outperforms existing methods.

AugCSE: Contrastive Sentence Embedding with Diverse Augmentations (2022.aacl-main)

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Zilu Tang, Muhammed Yusuf Kocyigit, Derry Tanti Wijaya

TADA: Efficient Task-Agnostic Domain Adaptation for Transformers (2023.findings-acl)

Challenge:	Similar work has shown that a single augmentation can be used to learn a robust generalpurpose representation with contrastive learning.
Approach:	They propose a unified framework to utilize diverse sets of data augmentations to achieve a better, general-purpose sentence embedding model.
Outcome:	The proposed framework achieves state-of-the-art results on downstream transfer tasks and performs competitively on semantic textual similarity tasks, using only unsupervised data.

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Chia-Chien Hung, Lukas Lange, Jannik Strötgen

PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models (2020.tacl-1)

Challenge:	Pre-trained transformer-based language models are limited in their expressiveness and domain knowledge.
Approach:	They propose a task-agnostic domain adaptation method which is modular, parameter-efficient, and data-efficient.
Outcome:	The proposed method is efficient and modular, parameter-efficient, and data-efficient.

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Eyal Ben-David, Carmel Rabinovitz, Roi Reichart

Unsupervised Domain Adaptation for Keyphrase Generation using Citation Contexts (2024.findings-emnlp)

Challenge:	PERL is a representation learning model that uses labeled data from the source domain and unlabeled data not necessarily drawn from the target domain.
Approach:	They propose a model that extends contextualized word embedding models with pivot-based fine-tuning to address this bottleneck.
Outcome:	The proposed model outperforms strong baselines across 22 sentiment classification domain adaptation setups and improves in-domain model performance.

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Florian Boudin, Akiko Aizawa

Practical Takes on Federated Learning with Pretrained Language Models (2023.findings-eacl)

Challenge:	Existing methods for keyphrase generation are limited to resource-rich languages.
Approach:	They propose to extract silver-standard keyphrases from citation contexts to create synthetic labeled data for domain adaptation.
Outcome:	The proposed method produces significant and consistent improvements over baselines across three domains.

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Ankur Agarwal, Mehdi Rezagholizadeh, Prasanna Parthasarathi

Contextual Parameter Generation for Universal Neural Machine Translation (D18-1)

Challenge:	federated learning with pretrained language models for language tasks entails data privacy constraints when learning from diverse data domains.
Approach:	They propose to use pretrained language models to learn from diverse data domains . they elaborate hypotheses over the components in federated NLP architectures based on three tasks .
Outcome:	The proposed model can generalize by adapting to the different domains.

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Emmanouil Antonios Platanios, Mrinmaya Sachan, Graham Neubig, Tom Mitchell

English-Indonesian Neural Machine Translation for Spoken Language Domains (P19-2)

Challenge:	Existing approaches to multilingual neural machine translation lack language-specific parameterization.
Approach:	They propose a modification to existing neural machine translation models that allows for language specific parameterization and domain adaptation.
Outcome:	The proposed model surpasses state-of-the-art for both the IWSLT-15 and IWSTL-17 datasets and can perform zero-shot translation.

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Meisyarah Dwiastuti

Source-Free Unsupervised Domain Adaptation for Question Answering via Prompt-Assisted Self-learning (2024.findings-naacl)

Challenge:	Neural machine translation (NMT) is a data-driven method that requires a large amount of data to build a robust model.
Approach:	They conduct a study on Neural Machine Translation (NMT) for English-Indonesian and Indonesian-English (ID-EN) they build NMT systems using the Transformer model for both translation directions and implement domain adaptation method to train pre-trained NMT on speech language data.
Outcome:	The proposed model can learn formal translation outputs for English-Indonesian and Indonesian-English (ID-EN) given a small dataset of speech-styled language and a larger dataset of less formal language, the proposed model will be useful for learning formality level.

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Maxwell Yin, Boyu Wang, Charles Ling

A Hybrid Approach for Automatic Extraction of Bilingual Multiword Expressions from Parallel Corpora (L18-1)

Challenge:	Existing SFDA methods focus on the adaptation phase, overlooking the impact of source domain training on model generalizability.
Approach:	They propose a source-free domain adaptation approach for Question Answering where a model trained on a domain is adapted to unlabeled target domains without additional source data.
Outcome:	The proposed model outperforms existing methods in managing domain gaps and demonstrating greater stability across target domains.

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Nasredine Semmar

AutoChecklist: Composable Pipelines for Checklist Generation and Scoring with LLM-as-a-Judge (2026.acl-demo)

Challenge:	Specific-domain bilingual lexicons are composed of MultiWord Expressions (MWEs) the manual construction of MWEs bilingual dictionaries is costly and time-consuming.
Approach:	They propose to use word alignment approaches to automatically construct bilingual lexicons of MWEs from parallel corpora by formalizing the alignment process as an integer linear programming problem.
Outcome:	The proposed approach extracts and aligns multiword expressions from parallel corpora and then filters them using linguistic patterns to build bilingual lexicons.

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Karen Zhou, Chenhao Tan

Challenge:	AutoChecklist is an open-source library that unifies checklist-based evaluation into composable pipelines.
Approach:	They propose an open-source library that unifies checklist-based evaluation into composable pipelines.
Outcome:	The open-source library unifies checklist-based evaluation into composable pipelines.

TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning (2021.findings-emnlp)

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Kexin Wang, Nils Reimers, Iryna Gurevych

A Hybrid Supervised-LLM Pipeline for Actionable Suggestion Mining in Unstructured Customer Reviews (2026.eacl-industry)

Challenge:	Existing methods to learn sentence embeddings require labeled data, but it is expensive.
Approach:	They propose an unsupervised method which learns sentence embeddings using unlabeled data . they propose a transformer-based sequence denoising auto-encoder which can be used for training .
Outcome:	The proposed method outperforms existing methods on four datasets from heterogeneous domains.

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Aakash Trivedi, Aniket Upadhyay, Pratik Narang, Dhruv Kumar, Praveen Kumar

M2D2: A Massively Multi-Domain Language Modeling Dataset (2022.emnlp-main)

Challenge:	Existing approaches to extract actionable suggestions from customer reviews are often mixed-intent, unstructured text.
Approach:	They propose a hybrid pipeline that uses a RoBERTa classifier and a precision–recall surrogate to extract actionable suggestions from customer reviews.
Outcome:	The proposed pipeline outperforms prompt-only, rule-based, and classifier-only baselines in extraction accuracy and cluster coherence.

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Machel Reid, Victor Zhong, Suchin Gururangan, Luke Zettlemoyer

Large-scale Machine Translation for Indian Languages in E-commerce under Low Resource Constraints (2022.emnlp-industry)

Challenge:	M2D2 consists of 8.5B tokens and spans 145 domains extracted from Wikipedia and Semantic Scholar.
Approach:	They propose to organize 145 domains into 22 groups and use ontologies from Wikipedia and ArXiv to study domain adaptation in language models.
Outcome:	The proposed model enables the study of domain adaptation in language models (LMs) it shows that small amounts of fine-grained data can lead to larger in-domain performance gains than weakly relevant data.

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Amey Patil, Nikesh Garera

Efficient Vocabulary Reduction for Small Language Models (2025.coling-industry)

Challenge:	We have deployed reliable and precise large-scale machine translation systems for several Indian regional languages.
Approach:	They develop a structured model development pipeline as a closed feedback loop with external manual feedback through an Active Learning component.
Outcome:	The proposed model improves over iterations for English to Hindi and for other languages.

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Yuta Nozaki, Dai Nakashima, Ryo Sato, Naoki Asaba

Relevance-assisted Generation for Robust Zero-shot Retrieval (2023.emnlp-industry)

Challenge:	Large language models (LLMs) have high computational costs and energy consumption, making their deployment in industrial settings difficult.
Approach:	They propose a small language model that compresses the embedding layer and reduces model size without significant loss of performance.
Outcome:	The proposed model reduces the embedding layer while maintaining performance while improving accuracy and performance.

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Jihyuk Kim, Minsoo Kim, Joonsuk Park, Seung-won Hwang

The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error (2022.findings-acl)

Challenge:	Despite strong in-domain performance, dense retrievers have shown poor generalization to out-of-domain zero-shot tasks where no training queries are available.
Approach:	They propose to generate domain-specific pseudo queries for fine-tuning with domain-relevant relevance between PQ and documents.
Outcome:	The proposed approach is more robust to domain shifts, validated on BEIR zero-shot tasks.

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Katherine Atwell, Anthony Sicilia, Seong Jae Hwang, Malihe Alikhani

Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains (C18-1)

Challenge:	Discourse analysis is very low on texts outside of the training distribution’s coverage, diminishing the practical utility of existing models.
Approach:	They propose to use a distribution shift statistic to estimate the error-gap of a discourse model and to use it to estimate it.
Outcome:	The proposed model can be estimated via distribution shift but does not correlate with change in the observed error of a classifier (i.e. error-gap).

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Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde

Exploring Zero and Few-shot Techniques for Intent Classification (2023.acl-industry)

Challenge:	Existing domain adaptation methods for sentiment analysis are sensitive to domain differences, resulting in classifiers that perform poorly on new domains.
Approach:	They propose a domain adaptation problem as an embedding projection task using two mono-domain embeddable spaces and a bi-domain space to project across domains and predict sentiment.
Outcome:	The proposed model performs better on domains similar to state-of-the-art methods while requiring longer training times.

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Soham Parikh, Mitul Tiwari, Prashil Tumbade, Quaizar Vohra

Building an Efficient Multilingual Non-Profit IR System for the Islamic Domain Leveraging Multiprocessing Design in Rust (2024.emnlp-industry)

Challenge:	Intent classification is the primary natural language understanding task for a virtual agent or a chatbot.
Approach:	They propose four different approaches to zero-shot intent classification with low-resource constraints . they use domain adaptation, data augmentation, and parametric fine-tuning to achieve this .
Outcome:	The proposed approaches perform well in low-resource settings for zero/few-shot intent classification . the proposed methods remove or substantially reduce the work to provide intent-utterances .

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Vera Pavlova, Mohammed Makhlouf

Information Retrieval

Challenge:	Existing models that are pre-trained on a general domain can deteriorate performance due to domain shift when applied to new domains.
Approach:	They propose to train a multilingual non-profit IR system for the Islamic domain using Rust Language capabilities.
Outcome:	The proposed model outperforms models pre-trained on general domains and on resource-constrained devices.

Modeling Temporality of Human Intentions by Domain Adaptation (D18-1)

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Xiaolei Huang, Lixing Liu, Kate Carey, Joshua Woolley, Stefan Scherer, Brian Borsari

Neuron-level Interpretation of Deep NLP Models: A Survey (2022.tacl-1)

Challenge:	Recent research shows that themes and words within a conversation change across time, whereas topics and the patient's attitude towards their willingness to change might shift.
Approach:	They propose a method that models the temporal factor by using domain adaptation on clinical dialogue corpora, Motivational Interviewing (MI).
Outcome:	The proposed method improves on a college alcoholism dataset using a bi-LSTM and topic model to learn language usage change across different time sessions.

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Hassan Sajjad, Nadir Durrani, Fahim Dalvi

Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable (P18-1)

Challenge:	Existing work on deep neural networks has focused on representation analysis, but recent work focused on analyzing neurons within these models.
Approach:	They propose to analyze neural networks to uncover linguistic concepts captured by the network . they propose to use a granular approach to analyze neurons within these models .
Outcome:	The proposed method combines methods to discover and understand neurons in a network with evaluation methods.

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Viktor Hangya, Fabienne Braune, Alexander Fraser, Hinrich Schütze

Iterative Dual Domain Adaptation for Neural Machine Translation (D19-1)

Challenge:	Previously, domain adaptation approaches to bilingual tasks were proposed . we show that simple adaptation process involving only unlabeled text is highly effective .
Approach:	They propose a method for domain adaptation of bilingual word embeddings using unlabeled data . they then tailor a semi-supervised classification method from computer vision to these tasks .
Outcome:	The proposed method improves on two bilingual tasks using unlabeled data.

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Jiali Zeng, Yang Liu, Jinsong Su, Yubing Ge, Yaojie Lu, Yongjing Yin, Jiebo Luo

Analysis on Unsupervised Acquisition Process of Bilingual Vocabulary through Iterative Back-Translation (2024.lrec-main)

Challenge:	Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our proposed framework.
Approach:	They propose an iterative dual domain adaptation framework for neural machine translation that uses multiple corpora to perform bidirectional translation knowledge transfer.
Outcome:	Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of the proposed framework.

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Takuma Tanigawa, Tomoyosi Akiba, Hajime Tsukada

Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models (N18-2)

Challenge:	Existing studies have demonstrated the effectiveness of iterative back-translation, but its reason has not been sufficiently elucidated.
Approach:	They propose a method for machine translation known as iterative back-translation . they use two monolingual data to create a pseudo-bilingual data and update translation models .
Outcome:	The proposed method improves translation quality and improves BLEU.

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David Vilar

Handling Cross- and Out-of-Domain Samples in Thai Word Segmentation (2021.findings-acl)

Challenge:	In this paper we explore the use of Learning Hidden Unit Contribution for neural machine translation.
Approach:	They propose to use Learning Hidden Unit Contribution for the task of neural machine translation.
Outcome:	The proposed method achieves improvements of up to 2.6 BLEU points over a general system . it also achieves up to 6 BLUE points if the initial system has been trained on out-of-domain data .

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Peerat Limkonchotiwat, Wannaphong Phatthiyaphaibun, Raheem Sarwar, Ekapol Chuangsuwanich, Sarana Nutanong

DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation (2023.acl-long)

Challenge:	Word segmentation is domain-dependent, which can be a challenge in low-resource languages like Thai and Urdu . a framework to handle out-of-domain inputs is proposed to improve word segmentation .
Approach:	They propose a domaingeneric domain adaptation framework and data augmentation technique to combat low-resource problems.
Outcome:	The proposed model outperforms the state-of-the-art Thai word segmentation method in out-of domain scenarios.

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Menglong Lu, Zhen Huang, Yunxiang Zhao, Zhiliang Tian, Yang Liu, Dongsheng Li

FAtNet: Cost-Effective Approach Towards Mitigating the Linguistic Bias in Speaker Verification Systems (2022.findings-naacl)

Challenge:	Existing approaches to domain adaptation only use reliable pseudo instances, i.e., pseudo instances with high prediction confidence, to retrain the model.
Approach:	They propose a domain adversarial learning enhanced self-training framework that uses meta-learning to estimate the importance of each pseudo instance and a meta constructor to construct the meta-validation set.
Outcome:	The proposed framework reduces label noise and preserves hard examples while maintaining accuracy.

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Divya Sharma, Arun Balaji Buduru

Cross-domain NER with Generated Task-Oriented Knowledge: An Empirical Study from Information Density Perspective (2024.emnlp-main)

Challenge:	Linguistic bias in Deep Neural Network (DNN) based systems is a critical challenge that needs attention.
Approach:	They propose to integrate a lightweight embedding with existing NLP systems to mitigate linguistic bias without adaptation.
Outcome:	The proposed framework reduces linguistic bias and enhances usability of baselines for twelve languages.

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Zhihao Zhang, Sophia Lee, Junshuang Wu, Dong Zhang, Shoushan Li, Erik Cambria, Guodong Zhou

Efficient Hierarchical Domain Adaptation for Pretrained Language Models (2022.naacl-main)

Challenge:	Cross-domain Named Entity Recognition (CDNER) is crucial for Knowledge Graph (KG) construction and natural language processing (NLP)
Approach:	They propose to automatically generate task-oriented knowledge using large language models (LLMs) and then employ task-orientated pre-training (TOPT) to facilitate domain adaptation.
Outcome:	The proposed model can learn to distinguish between different entities and improve its domain adaptation.

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Alexandra Chronopoulou, Matthew Peters, Jesse Dodge

Few-shot Adaptation Works with UnpredicTable Data (2023.acl-long)

Challenge:	Existing methods to allow domain adaptation to diverse domains are expensive and require continuing training in-domain.
Approach:	They propose a method to permit domain adaptation to many diverse domains using a computationally efficient adapter approach.
Outcome:	The proposed method allows domain adaptation to many diverse domains while avoiding negative interference between unrelated domains.

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Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez

Low-Resource Compositional Semantic Parsing with Concept Pretraining (2023.eacl-main)

Challenge:	Prior work on language models (LMs) shows that training on a large number of diverse tasks improves few-shot learning (FSL) performance on new tasks.
Approach:	They finetuned 413,299 tasks from internet tables to find narrow subsets outperform more diverse datasets.
Outcome:	The proposed model outperforms training on 40 human-curated NLP datasets on 52 downstream tasks, but not proportionally to dataset scale.

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Subendhu Rongali, Mukund Sridhar, Haidar Khan, Konstantine Arkoudas, Wael Hamza, Andrew McCallum

Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systems (C18-1)

Challenge:	Semantic parsing is a key role in voice assistants by mapping natural language to structured meaning representations.
Approach:	They propose an architecture to perform domain adaptation automatically with only a small amount of metadata about the new domain and without any new training data.
Outcome:	The proposed architecture outperforms existing models in low-resource settings.

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Van-Khanh Tran, Le-Minh Nguyen

SoMeWeTa: A Part-of-Speech Tagger for German Social Media and Web Texts (L18-1)

Challenge:	Existing methods to adapt to domains have shown promising results in how to reuse data in a domain-scalable framework efficiently.
Approach:	They propose an adversarial training procedure to train a Variational encoder-decoder based language generator via multiple adaptation steps.
Outcome:	The proposed method can adapt to a related domain using only a small amount of in-domain data.

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Thomas Proisl

Tagging

Challenge:	Off-the-shelf part-of-speech taggers perform poorly on web and social media data . this is due to the many unconventional spelling variants that occur in web and twitter texts and that result in a high proportion of out-of vocabulary words.
Approach:	They propose to use TIGER corpus as a part-of-speech tagger to train a German part- of-speak tagger on the web and social media data of the EmpiriST 2015 shared task.
Outcome:	The proposed tagger significantly improves on the state-of-the-art for both the web and social media data.

Residual Learning of Neural Text Generation with n-gram Language Model (2022.findings-emnlp)

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Huayang Li, Deng Cai, Jin Xu, Taro Watanabe

Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples (P18-1)

Challenge:	Experimental results show that n-gram models can achieve satisfactory performance on a large proportion of testing cases.
Approach:	They propose to learn a neural LM that fits the residual between an n-gram LM and the real-data distribution.
Outcome:	The proposed model achieves additional performance gains over popular standalone models on three typical language tasks.

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Vidur Joshi, Matthew Peters, Mark Hopkins

Breaking Boundaries in Retrieval Systems: Unsupervised Domain Adaptation with Denoise-Finetuning (2023.findings-emnlp)

Challenge:	Statistical parsers are often criticized for their performance outside of the domain they were trained on . we show that word representations reduce the need for domain adaptation when the target domain is syntactically similar to the source domain.
Approach:	They propose a way to adapt a parser to a syntactically similar target domain using partial annotations.
Outcome:	The proposed model increases the accuracy of a parser on the Wall Street Journal by 1.7% over the previous state-of-the-art model.

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Che Chen, Ching Yang, Chun-Yi Lin, Hung-Yu Kao

Continual Learning of Neural Machine Translation within Low Forgetting Risk Regions (2022.emnlp-main)

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.

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Shuhao Gu, Bojie Hu, Yang Feng

Improving End-to-End Task-Oriented Dialog System with A Simple Auxiliary Task (2021.findings-emnlp)

Challenge:	Currently, continuous learning methods suffer from catastrophic forgetting problem, causing model to forget previous knowledge while learning new knowledge.
Approach:	They propose a two-stage continuous learning method based on local features of the real loss to avoid catastrophic forgetting problem.
Outcome:	The proposed method achieves significant improvements on domain adaptation and more challenging language adaptation tasks.

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Yohan Lee

Pivot Based Language Modeling for Improved Neural Domain Adaptation (N18-1)

Challenge:	Using large pre-trained language models for end-to-end TOD modeling has made significant progress on benchmarks . a paradigm of leveraging large pretrained models has shown promising results .
Approach:	They combine paradigm of leveraging large pre-trained language models with multi-task learning framework . their model achieves new state-of-the-art results with combined scores of 108.3 and 107.5 .
Outcome:	The proposed model achieves state-of-the-art results on multiWOZ 2.0 and MultiWOZ 2.1 . it also improves generalization capability through domain adaptation experiments in the few-shot setting.

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Yftah Ziser, Roi Reichart

Towards Example-Based NMT with Multi-Levenshtein Transformers (2023.emnlp-main)

Challenge:	Existing work on domain adaptation does not exploit the structure of the input text . PBLM can naturally feed structure aware text classifiers such as LSTM and CNN .
Approach:	They propose a model that integrates pivot-based and NN modeling in a structure aware manner.
Outcome:	The proposed model can naturally feed structure aware text classifiers such as LSTM and CNN.

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Maxime Bouthors, Josep Crego, François Yvon

Vittorio Castelli, Rishav Chakravarti, Saswati Dana, Anthony Ferritto, Radu Florian, Martin Franz, Dinesh Garg, Dinesh Khandelwal, Scott McCarley, Michael McCawley, Mohamed Nasr, Lin Pan, Cezar Pendus, John Pitrelli, Saurabh Pujar, Salim Roukos, Andrzej Sakrajda, Avi Sil, Rosario Uceda-Sosa, Todd Ward, Rong Zhang

Challenge:	Retrieval-augmented machine translation (RAMT) is attracting growing attention . it is assumed to implement some form of domain adaptation .
Approach:	They propose a retrieval-augmented version of the Levenshtein Transformer to make it more transparent . they propose to perform training and inference in this model, based on multi-way alignment algorithms and imitation learning.
Outcome:	The proposed architecture improves translation performance and improves consistency of translations compared to previous models.

The TechQA Dataset (2020.acl-main)

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FinHarmBench: Financial Jailbreak Benchmark and Unsupervised Safety Fine-Tuning via Refusal Steering Distillation (2026.acl-industry)

Challenge:	TECHQA is a domain-adaptation question answering dataset for the technical support domain.
Approach:	They propose a domain-adaptation question-answering dataset for the technical support domain that contains actual questions posed by users on a technical forum .
Outcome:	The TECHQA dataset highlights two real-world issues from the automated customer support domain.

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Yubin Choi, Yujin Yang, Subin Kim, Seokil Ham, Seungju Cho, Jungmin Son, Youngjun Kwak, Changick Kim

Adversarial Domain Adaptation for Duplicate Question Detection (D18-1)

Challenge:	Existing safety benchmarks focus on general harms and lack the granularity needed to capture domain-specific financial threats.
Approach:	They propose a benchmark to evaluate financially harmful and confusable benign prompts.
Outcome:	The proposed framework improves refusal behavior without annotating refusal responses.

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Darsh Shah, Tao Lei, Alessandro Moschitti, Salvatore Romeo, Preslav Nakov

DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations (2025.emnlp-industry)

Challenge:	Recent years have seen the rise of community question answering forums . duplicate questions easily become ubiquitous as users often ask the same question, possibly in a slightly different formulation, making it difficult to find the best (or one correct) answer.
Approach:	They propose to use domain adaptation to detect duplicate questions in forums . they find that domain adaptation improves performance over multiple pairs of domains .
Outcome:	The proposed approach improves 5.6% over the best baseline across multiple pairs of domains.

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Elena Khasanova, Harsh Saini, Md Tahmid Rahman Laskar, Xue-Yong Fu, Cheng Chen, Shashi Bhushan Tn

Reasoning

Challenge:	Large Language Models (LLMs) have been used in real-world industrial scenarios for various natural language processing tasks, but their high inference cost makes their deployment impractical, necessitating the use of smaller models.
Approach:	They propose a continual pre-training technique that generates diverse task instructions and responses via reading comprehension on conversation transcripts, enabling better instruction generalization.
Outcome:	The proposed technique improves small LLMs’ domain adaptability for business conversational tasks, compared with traditional methods that rely on next-token prediction.

Robust Retrieval Augmented Generation for Zero-shot Slot Filling (2021.emnlp-main)

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Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Alfio Gliozzo

Domain Adaptation for Question Answering via Question Classification (2022.coling-1)

Challenge:	Automating high quality knowledge graphs from a given collection of documents remains a challenging problem in AI.
Approach:	They propose a novel approach to slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models.
Outcome:	The proposed model improves on both T-REx and zsRE slot filling datasets and ranks at the top-1 position in the KILT leaderboard.

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Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang

Efficient Cluster-Based k-Nearest-Neighbor Machine Translation (2022.acl-long)

Challenge:	Question answering systems often experience performance deterioration upon user-generated questions.
Approach:	They propose a question classification framework to help QA domains adapt to different domains.
Outcome:	The proposed framework improves on state-of-the-art datasets against multiple datasets.

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Dexin Wang, Kai Fan, Boxing Chen, Deyi Xiong

We Need To Talk About Random Splits (2021.eacl-main)

Challenge:	k-Nearest-Neighbor Machine Translation (kNN-MT) is a non-parametric solution for domain adaptation . previous studies have shown that kNN retrieval is at the expense of high latency .
Approach:	They propose to use clustering to improve retrieval efficiency by combining a non-parametric MT with an in-domain feature-based retrieval module.
Outcome:	The proposed method reduces translation latency by 57% while maintaining the most useful information of the original datastore.

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Anders Søgaard, Sebastian Ebert, Jasmijn Bastings, Katja Filippova

CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding (2021.findings-acl)

Challenge:	argued that random splits, like standard splits lead to overly optimistic performance estimates.
Approach:	They argue that random splits, like standard splits lead to overly optimistic performance estimates.
Outcome:	The proposed method leads to more realistic performance estimates than standard splits.

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Dustin Wright, Isabelle Augenstein

Challenge:	Scientific document understanding is challenging due to the highly domain specific nature of scientific language.
Approach:	They propose a large, contextualized, rigorously cleaned labelled dataset for cite-worthiness detection built from extracted scientific documents.
Outcome:	The proposed model improves on a paragraphlevel contextualized sentence labelling model based on Longformer . the model shows a 5 F1 point improvement over SciBERT which considers only individual sentences .

Simple, Scalable Adaptation for Neural Machine Translation (D19-1)

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Ankur Bapna, Orhan Firat

ixi-GEN: Efficient Industrial sLLMs through Domain Adaptive Continual Pretraining (2025.emnlp-industry)

Challenge:	Recent advances in deep learning have led to significantly improved quality on Neural Machine Translation (NMT) however, performance on out-of-domain data or low resource languages remains poor.
Approach:	They propose a simple yet efficient approach for adapting pre-trained models to multiple tasks simultaneously.
Outcome:	The proposed approach is on par with full fine-tuning on domain adaptation and massively multilingual NMT on a massively multilingual dataset.

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Seonwu Kim, Yohan Na, Kihun Kim, Hanhee Cho, Geun Lim, Mintae Kim, Seongik Park, Ki Hyun Kim, Youngsub Han, Byoung-Ki Jeon

GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval (2022.naacl-main)

Challenge:	Domain Adaptive Continual Pretraining (DACP) is a method to mitigate performance degradation in small LLMs and enhance their effectiveness in target domains.
Approach:	They propose a continual pretraining methodology that optimizes sLLMs within service domains and enhances their effectiveness in target domains.
Outcome:	The proposed model achieves significant gains in target-domain performance while preserving general capabilities, offering a cost-efficient and scalable solution for enterprise-level deployment.

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Kexin Wang, Nandan Thakur, Nils Reimers, Iryna Gurevych

Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies (2021.eacl-main)

Challenge:	Dense retrieval approaches suffer from the lexical gap and require large amounts of training data.
Approach:	They propose an unsupervised method for domain adaptation that uses query generator and pseudo labeling from a cross-encoder to improve retrieval performance.
Outcome:	The proposed method outperforms state-of-the-art retrieval methods on domain-specialized datasets by 9.3 points nDCG@10 on six tasks.

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Gabriele Pergola, Elena Kochkina, Lin Gui, Maria Liakata, Yulan He

What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation (2023.findings-acl)

Challenge:	Biomedical question-answering (QA) provides users with high-quality information from a vast scientific literature.
Approach:	They propose to use a biomedical entity-aware masking strategy to fine-tune masked language models to their domains.
Outcome:	The proposed approach is an adaptation process for masked LMs, not memory or components.

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Wenhao Zhu, Shujian Huang, Yunzhe Lv, Xin Zheng, Jiajun Chen

Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks (2021.emnlp-main)

Challenge:	kNN-MT builds an external datastore, which saves all target language token occurrences in the parallel corpus.
Approach:	They propose a new paradigm for domain adaptation by building an external datastore which usually saves all target language token occurrences in the parallel corpus.
Outcome:	The proposed model can be easily pruned according to local correctness, and it is more explainable.

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Yao Qiu, Jinchao Zhang, Jie Zhou

Analyzing Challenges in Neural Machine Translation for Software Localization (2023.eacl-main)

Challenge:	Pre-trained models can be fine-tuned on domain-specific unlabeled data . however, most further pre-training works just keep running the conventional pre- training task .
Approach:	They propose to add a further pre-training phase to the model to improve downstream tasks . they propose to use a domain-adaptive pre-tuning phase to fine-tune the models on unlabeled data .
Outcome:	The proposed method improves multiple task-oriented dialogue downstream tasks.

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Sai Koneru, Matthias Huck, Miriam Exel, Jan Niehues

Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation (2022.findings-naacl)

Challenge:	Neural machine translation (NMT) is a new form of machine translation that reduces the post-editing time of human annotators.
Approach:	They propose to use a novel multilingual UI corpus collection to test NMT for user interfaces.
Outcome:	The proposed test set evaluates state-of-the-art methods on a UI translation task from English to German and identifies its limitations.

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Zhijing Wu, Hua Xu, Jingliang Fang, Kai Gao

Unsupervised Domain Adaptation Method with Semantic-Structural Alignment for Dependency Parsing (2021.findings-emnlp)

Challenge:	Existing stationary-trained MRC systems are usually trained with in-domain data but are applied to new domain data.
Approach:	They propose a continual machine reading comprehension model with uncertainty-aware fixed memory and adversarial domain adaptation that keeps a stable understanding by learning both memory and new domain data.
Outcome:	The proposed model is superior to strong baselines and has a substantial incremental learning ability without catastrophically forgetting under two different continual MRC settings.

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Boda Lin, Mingzheng Li, Si Li, Yong Luo

CTTA-T: Continual Test-Time Adaptation for Text Understanding via Teacher-Student with a Domain-aware and Generalized Teacher (2026.acl-long)

Challenge:	Existing methods for dependency parsing are often of the pseudo-annotation type, but they fail to consider the change of model structure for domain adaptation.
Approach:	They propose a method that accomplishes unsupervised cross-domain dependency parsing without using labeled data.
Outcome:	The proposed method achieves consistent performance improvement on CODT1 and CTB9 domains.

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Tianlun Liu, Zhiliang Tian, Zhen Huang, Xingzhi Zhou, Wanlong Yu, Tianle Liu, Feng Liu, Dongsheng Li

Reinforced Training Data Selection for Domain Adaptation (P19-1)

Challenge:	Existing models for text understanding fail to adapt to domain shifts in real-world applications . current models do not improve themselves as they are applied to new domains .
Approach:	They propose a continual test-time adaptation framework that adapts to evolving domains . they propose accumulating domains and a refine-then-filter framework to calibrate teacher predictions .
Outcome:	The proposed model excels in a teacher-student framework adaptable to evolving domains.

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Miaofeng Liu, Yan Song, Hongbin Zou, Tong Zhang

Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER (2022.acl-long)

Challenge:	Existing approaches to learn domains with massive data are not easy to implement and require a predefined threshold.
Approach:	They propose a framework that searches for training instances relevant to the target domain and learns better representations for them.
Outcome:	The proposed framework is effective in data selection and representation, but generalized to accommodate different NLP tasks.

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Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren

The challenges of temporal alignment on Twitter during crises (2022.findings-emnlp)

Challenge:	Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates.
Approach:	They propose a demonstration-based learning method which lets the input be prefaced by task demonstrations for in-context learning.
Outcome:	The proposed method improves on in-domain learning and domain adaptation in low-resource settings.

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Aniket Pramanick, Tilman Beck, Kevin Stowe, Iryna Gurevych

Challenge:	Existing models consider data spanning years to decades, but shorter time spans are critical for crisis data.
Approach:	They propose to use domain adaptation techniques to cope with performance degradation by leveraging domain adaptation.
Outcome:	The proposed models outperform baseline models under conditions of natural and human-induced disasters while highlighting the limitations of current models.

To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation (2025.coling-main)

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Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza

Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media (2021.findings-emnlp)

Challenge:	Active learning (AL) techniques reduce labeling costs for training neural machine translation models by selecting smaller representative subsets from unlabeled data for annotation.
Approach:	They propose an AL strategy that combines uncertainty and diversity for sentence selection.
Outcome:	The proposed method prioritizes diverse instances having high model uncertainty for annotation in early iterations.

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Paul Röttger, Janet Pierrehumbert

Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation (N19-1)

Challenge:	Language use differs between domains and even within a domain, language use changes over time.
Approach:	They propose to use social media comments to study temporal adaptations in pre-trained language models.
Outcome:	The proposed model performs better on past than on future test sets, whereas adapting to domain does not improve performance on the downstream task.

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Brian Thompson, Jeremy Gwinnup, Huda Khayrallah, Kevin Duh, Philipp Koehn

Nearest Neighbor Zero-Shot Inference (2022.emnlp-main)

Challenge:	Neural Machine Translation (NMT) performs poorly without large training corpora.
Approach:	They propose a machine learning method that retains the majority of general-domain performance lost in continued training without degrading in-domain.
Outcome:	The proposed method retains the majority of general-domain performance lost in continued training without degrading in-domain performances.

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Weijia Shi, Julian Michael, Suchin Gururangan, Luke Zettlemoyer

Neural Adaptation Layers for Cross-domain Named Entity Recognition (D18-1)

Challenge:	Using non-parametric memory for retrieval-augmented language models yields significant performance boosts over strong zeroshot baselines.
Approach:	They propose a retrieval-augmented language model with fuzzy verbalizers that expands the verbalizes that define different end-task class labels.
Outcome:	The proposed model outperforms non-retrieval-augmented language models on perplexity-based evaluations but gains transfer marginally . the main challenge is to achieve coverage of the verbalizer tokens that define the different end-task class labels.

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Bill Yuchen Lin, Wei Lu

Semi-supervised Domain Adaptation for Dependency Parsing (P19-1)

Challenge:	Named entity recognition is a type of information extraction task whereby features can be designed based on domain-specific knowledge.
Approach:	They propose to use existing neural architectures to adapt to new domains without retraining . they propose to add adaptation layers to existing neural models to minimize re-training based on source data.
Outcome:	The proposed approach significantly outperforms state-of-the-art methods on social media domains.

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Zhenghua Li, Xue Peng, Min Zhang, Rui Wang, Luo Si

Masked Language Model Scoring (2020.acl-main)

Challenge:	Currently, most studies on cross-domain parsing focus on unsupervised domain adaptation . however, unsupervised approaches make limited progress due to the intrinsic difficulty of both domain adaptation and parse.
Approach:	They propose a semi-supervised domain adaptation problem for Chinese dependency parsing by using newly-annotated large-scale domain-aware datasets.
Outcome:	The proposed method is more effective than direct corpus concatenation and multi-task learning.

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Julian Salazar, Davis Liang, Toan Q. Nguyen, Katrin Kirchhoff

You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions (2021.naacl-main)

Challenge:	Pretrained masked language models require finetuning for most tasks.
Approach:	They evaluate pretrained masked language models out of the box via their pseudo-log-likelihood scores (PLLs) they attribute this success to PLL’s unsupervised expression of linguistic acceptability without a left-to-right bias, greatly improving on scores from GPT-2 .
Outcome:	The proposed model outperforms autoregressive language models in a variety of tasks.

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Sergey Volokhin, Joyce Ho, Oleg Rokhlenko, Eugene Agichtein

Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection (2023.emnlp-main)

Challenge:	Existing methods for conversational recommendation include collaborative filtering, content-based filtering and user reviews.
Approach:	They propose to map a conversational user to most similar external reviewers, whose preferences are known, and adapt collaborative filtering techniques to estimate the current user’s preferences for new movies.
Outcome:	The proposed method can improve the accuracy of predicting user ratings for new movies by exploiting conversation content and external data.

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Gretel De la Peña Sarracén, Paolo Rosso, Robert Litschko, Goran Glavaš, Simone Ponzetto

Spectral Insights into Data-Oblivious Critical Layers in Large Language Models (2025.findings-acl)

Challenge:	Existing methods for few-shot cross-lingual transfer learning are limited in target languages due to the scarcity of resources.
Approach:	They propose a method which interpolates pairs of instances based on the angle of their representations and propose augmentation methods to enhance few-shot cross-lingual abusive language detection.
Outcome:	The proposed method improves few-shot cross-lingual abusive language detection in seven languages typologically distinct from English and three different domains.

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Xuyuan Liu, Lei Hsiung, Yaoqing Yang, Yujun Yan

PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack (2022.coling-1)

Challenge:	Recent studies have identified critical layers linked to specific functions or behaviors, limiting their use to post-hoc settings.
Approach:	They propose a data-oblivious approach to identify intrinsic critical layers in pre-fine-tuned LLMs by analyzing representation dynamics via Centered Kernel Alignment.
Outcome:	The proposed approach identifies critical layers in pre-fine-tuned models . layers with significant shifts in representation space are also those most affected during fine-tuning .

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Rui Zheng, Rong Bao, Qin Liu, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu

Robustness in NLP

Challenge:	Recent studies have shown that adversarial examples can be easily fooled by adversarially perturbed examples.
Approach:	They propose a pluggable defense module PlugAT to provide robust predictions by adding a few trainable parameters to the model inputs while keeping the original model frozen.
Outcome:	The proposed model improves robustness over several strong baselines whilst training only 9.1% parameters.

Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation (2022.findings-emnlp)

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Xu Guo, Boyang Li, Han Yu

A Two Stage Adaptation Framework for Frame Detection via Prompt Learning (2022.coling-1)

Challenge:	Prompt tuning is a technique for adapting large-scale pretrained language models for downstream tasks.
Approach:	They propose to condition a frozen pretrained language model with soft prompts from data . they propose to use a domain adaptation technique to regularize the decision boundary .
Outcome:	The proposed method outperforms full-model tuning in data-scarce settings by a large margin.

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Xinyi Mou, Zhongyu Wei, Changjian Jiang, Jiajie Peng

Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models’ Memories (2023.acl-long)

Challenge:	Existing frameworks focus on a single scenario or issue, ignoring the special characteristics of frame detection that new events emerge continuously and policy agenda changes dynamically.
Approach:	They propose a framework to adapt to different contexts and frame typologies . they propose coding tasks that learn transferable encoders and verbalizers based on pivots and prompts - and generalization tasks that apply them to new issues and label sets.
Outcome:	The proposed framework shows superiority in both full-resource and low-resourced conditions.

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Shizhe Diao, Tianyang Xu, Ruijia Xu, Jiawei Wang, Tong Zhang

Chunk-based Nearest Neighbor Machine Translation (2022.emnlp-main)

Challenge:	Pre-trained language models demonstrate excellent abilities to understand texts in the generic domain while struggling in a specific domain.
Approach:	They propose to decouple the feed-forward networks of the Transformer architecture into two parts to maintain old-domain knowledge and a mixture-of-adapters gate to inject domain-specific knowledge in parallel.
Outcome:	The proposed method achieves superior performance on in-domain, out-of-domain and knowledge-intensive tasks.

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Pedro Henrique Martins, Zita Marinho, André F. T. Martins

Knowledge Inheritance for Pre-trained Language Models (2022.naacl-main)

Challenge:	Semi-parametric models augment generation with retrieval, but require expensive retrieval operation for every generated token.
Approach:	They propose a semi-parametric model which augments generation with retrieval by retrieving tokens from a datastore.
Outcome:	The proposed model can retrieve chunks of tokens from the datastore, instead of a single token, with a low decoding speed.

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Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Modular Domain Adaptation (2022.findings-acl)

Challenge:	Existing large-scale pre-trained language models are mainly trained from scratch individually, ignoring that many well-taught PLMs are available.
Approach:	They propose a pre-training framework called knowledge inheritance and propose auxiliary supervision to efficiently learn larger PLMs.
Outcome:	The proposed framework can be used to train large-scale language models with huge parameters and a large dataset can be adapted to domain adaptation and knowledge transfer.

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Junshen Chen, Dallas Card, Dan Jurafsky

Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling (2025.acl-long)

Challenge:	Existing models for sentiment analysis and hate speech detection are difficult to account for domain shift without access to source data.
Approach:	They propose to treat domain adaptation as a modular process that involves separate model producers and model consumers . they demonstrate that they can independently cooperate to facilitate more accurate measurements of text .
Outcome:	The proposed methods improve out-of-domain accuracy on four multi-domain text classification datasets.

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Pritom Saha Akash, Kevin Chen-Chuan Chang

Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation (2021.naacl-main)

Challenge:	Existing topic modeling models struggle in low-resource settings where data is limited . et al., 2003: domain adaptation for low-source topic modeling is challenging in low resources .
Approach:	They propose a domain adaptation framework that disentangles domaininvariant and domain-specific components to improve topic adaptation.
Outcome:	The proposed model outperforms state-of-the-art methods on low-resource datasets on diverse datasets.

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Shuhao Gu, Yang Feng, Wanying Xie

Improving Domain Adaptation Translation with Domain Invariant and Specific Information (N19-1)

Challenge:	Existing methods for domain adaptation suffer from catastrophic forgetting, large domain divergence, and model explosion.
Approach:	They propose a method which prunes the model and keeps the important neurons or parameters responsible for both general-domain and in-domain translation.
Outcome:	The proposed method improves on different language pairs and domains compared with strong baselines.

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Shuhao Gu, Yang Feng, Qun Liu

Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding (2024.lrec-main)

Challenge:	Neural machine translation models are based on the encoder-decoder architecture, which makes them overfitting to frequent observations.
Approach:	They propose a method to explicitly model out-of-domain information in an encoder-decoder framework . they propose combining out- of-domain training data with out-out-of domain data .
Outcome:	The proposed method outperforms baselines on multiple data sets.

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Ahmad Idrissi-Yaghir, Amin Dada, Henning Schäfer, Kamyar Arzideh, Giulia Baldini, Jan Trienes, Max Hasin, Jeanette Bewersdorff, Cynthia S. Schmidt, Marie Bauer, Kaleb E. Smith, Jiang Bian, Yonghui Wu, Jörg Schlötterer, Torsten Zesch, Peter A. Horn, Christin Seifert, Felix Nensa, Jens Kleesiek, Christoph M. Friedrich

Domain Adaptive Text Style Transfer (D19-1)

Challenge:	Pre-trained language models can struggle in specialized domains such as medicine . existing generalpurpose pre-tried models can be used and refined through further pre-training on domainspecific unlabeled data.
Approach:	They pre-trained German medical language models on 2.4B tokens from translated public data and 3B token of German clinical data.
Outcome:	The proposed models outperform clinical models on various downstream tasks in germany . the authors show that continuous pre-training can match or exceed clinical models trained from scratch .

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Dianqi Li, Yizhe Zhang, Zhe Gan, Yu Cheng, Chris Brockett, Bill Dolan, Ming-Ting Sun

Semi-Supervised Domain Adaptation for Emotion-Related Tasks (2023.findings-acl)

Challenge:	Text style transfer without parallel data is a promising method for learning, but in the scenario where less data is available, it may yield poor performance.
Approach:	They propose to leverage available data to learn domain-adaptive text style transfer models . they evaluate two style transfer tasks where only limited non-parallel data is available .
Outcome:	The proposed models learn from the source domain to: (i) distinguish stylized information and generic content information; (ii) maximally preserve content information and (iv) adaptively transfer the styles in a domain-aware manner.

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Mahshid Hosseini, Cornelia Caragea

An Analysis of Dialogue Act Sequence Similarity Across Multiple Domains (2022.lrec-1)

Challenge:	Semi-supervised domain adaptation (SSDA) is a model trained from a label-rich source domain to a new but related domain with a few labels of target data.
Approach:	They propose to decompose the semi-supervised domain adaptation framework into two subcomponents of unsupervised domain adaption (UDA) from the source to the target domain and semi-supervised learning (SSL) in the target.
Outcome:	The proposed method is based on the co-learning of multiple classifiers for computer vision tasks and is published in the journal Nature.

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Ayesha Enayet, Gita Sukthankar

A Multilingual BPE Embedding Space for Universal Sentiment Lexicon Induction (P19-1)

Challenge:	a recent study shows that many machine learning models perform poorly when exposed to domain shifts due to contextual differences.
Approach:	They analyze dialogue act sequences from related domains to predict performance degradation . they find that when dialogue acts sequences are dissimilar they lie further away in embedding space .
Outcome:	The proposed model can be trained even when the datasets are corrupted with noise.

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Mengjie Zhao, Hinrich Schütze

Domain adaptation for part-of-speech tagging of noisy user-generated text (N19-1)

Challenge:	Existing methods for sentiment lexicon induction are limited to low-resource languages.
Approach:	They propose a method for sentiment lexicon induction that is applicable to the entire range of typological diversity of the world's languages.
Outcome:	The proposed method is applicable to the entire range of typological diversity of the world's languages.

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Luisa März, Dietrich Trautmann, Benjamin Roth

Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization (2022.naacl-main)

Challenge:	Existing POS taggers for canonical German text achieve good results around 97% accuracy, but when applying these trained models to out-of-domain data the performance decreases drastically.
Approach:	They propose a neural network that trains an out-of-domain model on a large newswire corpus and transfers those weights by using them as a prior for a model trained on the target domain.
Outcome:	The proposed model achieves a tagging accuracy of slightly over 90%, improving on the previous state of the art for this task.

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Lulu Zhao, Fujia Zheng, Weihao Zeng, Keqing He, Weiran Xu, Huixing Jiang, Wei Wu, Yanan Wu

Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation (2021.findings-emnlp)

Challenge:	Existing methods for domain adaptation of abstractive dialogue summarization lack generalization ability on new domains.
Approach:	They propose a domain-oriented prefix-tuning model that uses a prefix module to alleviate domain entanglement and discrete prompts to guide the model to focus on key contents of dialogues.
Outcome:	The proposed model can be generalized to two multi-domain dialogue summarization datasets.

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Xin Zheng, Zhirui Zhang, Shujian Huang, Boxing Chen, Jun Xie, Weihua Luo, Jiajun Chen

Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling (N19-1)

Challenge:	kNN-MT is a non-parametric method that uses nearest neighbor retrieval to translate out-of-domain sentences, rare words, etc.
Approach:	They propose a framework that directly uses in-domain monolingual sentences to build an effective datastore for k-nearest-neighbor retrieval.
Outcome:	The proposed framework improves translation accuracy with target-side monolingual data while achieving comparable performance with back-translation.

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Kathleen C. Fraser, Nicklas Linz, Bai Li, Kristina Lundholm Fors, Frank Rudzicz, Alexandra König, Jan Alexandersson, Philippe Robert, Dimitrios Kokkinakis

DAdEE: Unsupervised Domain Adaptation in Early Exit PLMs (2024.findings-emnlp)

Challenge:	Existing work on speech and language models has been limited by the size of available datasets.
Approach:	They propose to augment a small French dataset with a much larger English dataset to augment the language model to model the order in which information units are produced by dementia patients and controls.
Outcome:	The proposed model improves classification performance in English and French separately.

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Divya Jyoti Bajpai, Manjesh Hanawal

Data Augmentation with Atomic Templates for Spoken Language Understanding (D19-1)

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.

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Zijian Zhao, Su Zhu, Kai Yu

Improving Referring Ability for Biomedical Language Models (2024.findings-emnlp)

Challenge:	Existing methods to enlarge SLU data require large amounts of labelled data.
Approach:	They propose a data augmentation method with atomic templates for Spoken Language Understanding which generates atomic exemplars from atomic template.
Outcome:	The proposed method improves on a DSTC 2&3 dataset which is a domain adaptation setting of SLU.

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Junfeng Jiang, Fei Cheng, Akiko Aizawa

Vocabulary Adaptation for Domain Adaptation in Neural Machine Translation (2020.findings-emnlp)

Challenge:	Existing auto-regressive large language models (LLMs) are primarily trained using documents from general domains.
Approach:	They propose to use citation network to improve the pre-training of auto-regressive large language models (LLMs) in the biomedical domain.
Outcome:	Empirical studies show that the proposed method improves both the intra-sample and inter-sammple referring abilities of auto-regressive large language models in the biomedical domain.

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Shoetsu Sato, Jin Sakuma, Naoki Yoshinaga, Masashi Toyoda, Masaru Kitsuregawa

Investigating Catastrophic Forgetting During Continual Training for Neural Machine Translation (2020.coling-main)

Challenge:	Neural network methods exhibit strong performance only in a few resource-rich domains.
Approach:	They propose a method that fine-tunes embedding layers of a pre-trained NMT model to the target domain.
Outcome:	The proposed method improves fine-tuning performance in En-Ja and De-En translation by 3.86 and 3.28 BLEU points.

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Shuhao Gu, Yang Feng

Konooz: Multi-domain Multi-dialect Corpus for Named Entity Recognition (2025.findings-acl)

Challenge:	Neural machine translation models suffer from catastrophic forgetting during continual training . models tend to overfit to frequent observations in the in-domain data but forget previously learned knowledge.
Approach:	They investigated the causes of catastrophic forgetting in NMT models by examining their parameters and modules.
Outcome:	The proposed model forgets previously learned knowledge and swings to fit new data . the results show that some parameters are important for both the general-domain and in-domain translation and the great change of them during continual training brings about the performance decline in general- domain.

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Nagham Hamad, Mohammed Khalilia, Mustafa Jarrar

Filtering Back-Translated Data in Unsupervised Neural Machine Translation (2020.coling-main)

Challenge:	Using the Wojood framework, we compare existing Arabic Named Entity Recognition models with domain and dialect divergence and resource scarcity.
Approach:	They propose a multi-dimensional Arabic named entity corpus covering 16 dialects across 10 domains and an annotation scheme using the Wojood guidelines.
Outcome:	The proposed model performs better on 16 dialects across 10 domains and 16 domains, while other models struggle with different dialects and domains.

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Jyotsana Khatri, Pushpak Bhattacharyya

Intermediate Self-supervised Learning for Machine Translation Quality Estimation (2020.coling-main)

Challenge:	Current state of the art approaches for unsupervised neural machine translation (NMT) use only monolingual data for training.
Approach:	They propose an approach to filter back-translated data as part of the training process of unsupervised neural machine translation (NMT) they propose a weight component based on the quality of pseudo parallel sentence pairs generated in back-translation phase.
Outcome:	The proposed approach improves the training performance of unsupervised neural machine translation systems by giving weight to good pseudo parallel sentence pairs in the back-translation phase.

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Raphael Rubino, Eiichiro Sumita

PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks (2025.emnlp-main)

Challenge:	Existing methods for machine translation quality estimation (QE) rely on annotated data.
Approach:	They propose a self-supervised learning task for machine translation (MT) that orients a pre-trained model towards the target task.
Outcome:	The proposed method outperforms existing methods on English-to-German and English- to-Russian translation directions and is comparable to existing models.

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Yunuo Liu, Dawei Zhu, Zena Al-Khalili, Dai Cheng, Yanjun Chen, Dietrich Klakow, Wei Zhang, Xiaoyu Shen

Reasoning

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, such as code generation, mathematical problem-solving, and general-purpose human instruction following.
Approach:	They propose to use large language models to process questions expressed in natural language to automate tourism-booking prices when multiple, overlapping farerules apply.
Outcome:	The proposed model can automate tourism-booking prices when multiple, overlapping farerules apply.

IYKYK: Using language models to decode extremist cryptolects (2026.eacl-long)

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Christine de Kock, Arij Riabi, Zeerak Talat, Michael Sejr Schlichtkrull, Pranava Madhyastha, Eduard Hovy

Maximum Bayes Smatch Ensemble Distillation for AMR Parsing (2022.naacl-main)

Challenge:	Extremist groups develop complex in-group language to exclude or mislead outsiders . general purpose LLMs cannot consistently detect or decode extremist language .
Approach:	They evaluate the ability of current language technologies to detect and interpret the cryptolects of two online extremist platforms.
Outcome:	The proposed models can detect and interpret extremist language better than current models.

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Young-Suk Lee, Ramón Astudillo, Hoang Thanh Lam, Tahira Naseem, Radu Florian, Salim Roukos

Local Byte Fusion for Neural Machine Translation (2023.acl-long)

Challenge:	AMR parsing has experienced an unprecendented increase in performance in the last three years due to a mixture of effects including architecture improvements and transfer learning.
Approach:	They propose to combine Smatch-based ensembling techniques with ensemble distillation to overcome this diminishing returns of silver data.
Outcome:	The proposed technique can produce gains rivaling those of human annotated data for QALD-9 and achieve a new state-of-the-art for BioAMR.

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Makesh Narsimhan Sreedhar, Xiangpeng Wan, Yu Cheng, Junjie Hu

Task-adaptive Pre-training of Language Models with Word Embedding Regularization (2021.findings-acl)

Challenge:	Existing NLP models rely on a pre-built subword tokenizer to tokenize a sentence . this can be rigid and subwords from low-resource languages are under-represented .
Approach:	They propose a method for byte-based machine translation that aggregates local semantic information.
Outcome:	The proposed method improves on multilingual translation and cross-lingual transfer . it is parameter-efficient and performs competitively to subword models, it is shown .

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Kosuke Nishida, Kyosuke Nishida, Sen Yoshida

Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models (P19-1)

Challenge:	Pre-trained language models acquire domain-independent knowledge through pre-training with massive textual resources.
Approach:	They propose a task-adaptive pre-training process that makes static embeddings close to the word embedds obtained in the target domain.
Outcome:	The proposed process improves on BioASQ and SQuAD when the pre-training corpora were not dominated by indomain data.

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Xiaolei Huang, Michael J. Paul

Balancing Knowledge Breadth and Task Depth for Effective Domain Adaptation Fine-Tuning (2026.findings-acl)

Challenge:	Recent studies show that document classifiers can become more stable over time when trained in ways that account for temporal variations.
Approach:	They propose a method for embedding diachronic word embedds into document classification models . they propose 'time-driven neural classification model' that accounts for temporal variations .
Outcome:	The proposed model can be trained on six corpora and make it more robust over time.

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Mu Zhang, Yuxiang Chu, Guangya Yu, Yongqi Fan, Weiyan Zhang, Hang Hu, Tong Ruan, Jingping Liu

Pre-train or Annotate? Domain Adaptation with a Constrained Budget (2021.emnlp-main)

Challenge:	a lack of knowledge breadth and task depth can hinder curriculum learning in domains such as medicine and finance.
Approach:	They propose a two-dimensional curriculum learning framework that coordinates model training along two orthogonal axes: the knowledge dimension and the task dimension.
Outcome:	The proposed framework improves accuracy on medical evaluations by 2.49% and on financial evaluations 1.2% compared with the second-best method.

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Fan Bai, Alan Ritter, Wei Xu

Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging (2020.emnlp-main)

Challenge:	Recent work shows that pre-training in-domain language models can boost performance when adapting to a new domain.
Approach:	They propose to combine annotation and pre-training to maximize performance under budget constraints.
Outcome:	The proposed approach is based on the annotation cost of three procedural text datasets and pre-training cost of 3 in-domain language models.

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Semih Yavuz, Kazuma Hashimoto, Wenhao Liu, Nitish Shirish Keskar, Richard Socher, Caiming Xiong

DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation (2024.naacl-long)

Challenge:	Existing studies on DA tagging focus on human-human social conversations, which is less applicable for task-oriented setting.
Approach:	They propose a controllable mechanism that augments text input by leveraging the pre-trained Mask token from BERT model.
Outcome:	The proposed mechanism augments text input by leveraging the pre-trained Mask token from BERT model.

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Ramraj Chandradevan, Kaustubh Dhole, Eugene Agichtein

Evaluation of Transfer Learning and Domain Adaptation for Analyzing German-Speaking Job Advertisements (2022.lrec-1)

Challenge:	State-of-the-art rankers pre-trained on large task-specific training data such as MS-MARCO exhibit strong performance on various ranking tasks without domain adaptation, also called zero-shot.
Approach:	They propose a method to generate unsupervised domain adaptation for ranking using large-scale task-specific training data such as MS-MARCO and Wikipedia retrieval.
Outcome:	The proposed method outperforms all zero-shot baselines and significantly outperfies the SOTA baselines on 16 out of 18 datasets, for an average of 4% relative improvement across all datasets.

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Ann-Sophie Gnehm, Eva Bühlmann, Simon Clematide

Few-Shot Document-Level Relation Extraction (2022.naacl-main)

Challenge:	a paper presents text mining approaches on German-speaking job advertisements . transfer learning and domain adaptation are used to build text mining applications .
Approach:	They propose text mining approaches on German-speaking job advertisements . they use transfer learning and domain adaptation to build language models adapted to job ads .
Outcome:	The proposed approaches outperform general-domain language models pre-trained on ten times more data.

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Nicholas Popovic, Michael Färber

Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents (2022.emnlp-main)

Challenge:	Existing benchmarks for relation extraction are built on sentence-level corpora, but document-level ones provide more realism.
Approach:	They propose a few-shot document-level relation extraction benchmark based on document-based corpora.
Outcome:	The proposed benchmark is based on two existing supervised learning data sets, DocRED and sciERC.

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Marcio Fonseca, Yftah Ziser, Shay B. Cohen

Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition (2021.acl-long)

Challenge:	Using a factorization approach, summarization decisions are conflated into a single feedforward step without taking into account contextual factors.
Approach:	They propose to factorize summarization into two steps following a budget and content guidance.
Outcome:	The proposed method outperforms PEGASUS in domain adaptation and generates significantly higher ROUGE scores on multiple benchmarks for long document summarization.

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Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, Pengjun Xie

Candidate-Aware Retrieval and Reranking for Multiple-Choice Question Answering: Arabic as a Case Study (2026.findings-acl)

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.

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Yassine Bouziane, Youness Moukafih, Mounir Ghogho

Can Data Diversity Enhance Learning Generalization? (2022.coling-1)

Challenge:	Large language models (LLMs) have recently achieved impressive results on multiple-choice question answering (MCQA) despite advances in English, LLMs continue to underperform in Arabic due to gaps in data coverage, linguistic transfer, and evaluation design.
Approach:	They propose a method that jointly models the relevance of both the question and its candidate answers when selecting contextual passages.
Outcome:	The proposed approach outperforms standard RAG baselines and reranker baselines while remaining competitive with considerably larger models.

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Yu Yu, Shahram Khadivi, Jia Xu

Challenge:	a diversity advanced actor-critical reinforcement learning framework is used to improve NLP generalization and accuracy.
Approach:	They introduce Diversity Advanced Actor-Critic reinforcement learning framework to improve NLP generalization and accuracy.
Outcome:	The proposed framework outperforms domain adaptation and generalization baselines without using any target domain knowledge.

Text Style Transfer Back-Translation (2023.acl-long)

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Daimeng Wei, Zhanglin Wu, Hengchao Shang, Zongyao Li, Minghan Wang, Jiaxin Guo, Xiaoyu Chen, Zhengzhe Yu, Hao Yang

Multi-Task Deep Neural Networks for Natural Language Understanding (P19-1)

Challenge:	Current methods require large amount of bilingual training data, which is challenging and sometimes impossible task.
Approach:	They propose a method to modify the style of inputs by modifying the source side of BT data.
Outcome:	The proposed method significantly improves translation quality against popular BT benchmarks on high-resource and low-resourced language pairs.

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Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao

SEDAR: a Large Scale French-English Financial Domain Parallel Corpus (2020.lrec-1)

Challenge:	Existing approaches to learning vector-space representations of text are multitask learning and language model pre-training.
Approach:	They propose a multi-task deep neural network (MT-DNN) that leverages cross-task data and incorporates a pre-trained bidirectional transformer language model.
Outcome:	The proposed model achieves state-of-the-art on ten NLU tasks and pushes the GLUE benchmark to 82.7% (2.2% absolute improvement)

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Abbas Ghaddar, Phillippe Langlais

PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition (2021.emnlp-main)

Challenge:	Existing approaches for neural machine translation use small amount of data or monolingual data.
Approach:	They describe acquisition, preprocessing and characteristics of a large English-French parallel corpus for the financial domain.
Outcome:	The proposed corpus contains 8.6 million high quality sentence pairs . the first release of the corpus is available on github.

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Tao Zhang, Congying Xia, Philip S. Yu, Zhiwei Liu, Shu Zhao

Evaluating Tokenizers Impact on OOVs Representation with Transformers Models (2022.lrec-1)

Challenge:	Existing approaches to Named Entity Recognition (NER) are limited in labeled resources and domain shift.
Approach:	They propose a progressive domain adaptation knowledge distillation approach to adapt high-resource domains to low-resourced target domains by employing three components to achieve superior domain adaptability.
Outcome:	The proposed approach can adapt high-resource domains to low-resourced target domains even if they are diverse in terms and writing styles.

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Alexandra Benamar, Cyril Grouin, Meryl Bothua, Anne Vilnat

Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data (2022.lrec-1)

Challenge:	Pre-trained Transformer models have proven their effectiveness in adapting to multiple NLP tasks and domains.
Approach:	They evaluated three categories of out-of-vocabulary words using three French domain-specific datasets on the legal, medical, and energetical domains to robustly analyze these categories.
Outcome:	The proposed models can create new representations for out-of-vocabulary words by adding external morpho-syntactic context rather than improving the semantic understanding of the words directly.

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Thomas Vakili, Anastasios Lamproudis, Aron Henriksson, Hercules Dalianis

Semi-supervised New Slot Discovery with Incremental Clustering (2022.findings-emnlp)

Challenge:	Automatic de-identification systems introduce errors due to their imperfect precision and may negatively impact the utility of the de-identified dataset.
Approach:	They propose to de-identifie a large clinical corpus in Swedish by removing entire sentences containing sensitive data or by replacing sensitive words with realistic surrogates.
Outcome:	The proposed models are safe to distribute to other academic researchers and reduce privacy risks.

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Yuxia Wu, Lizi Liao, Xueming Qian, Tat-Seng Chua

Domain-Agnostic Adapter Architecture for Deception Detection: Extensive Evaluations with the DIFrauD Benchmark (2024.lrec-main)

Challenge:	Existing methods to discover new slots rely on unsupervised slot induction or domain adaptation, and are limited in transferring prior knowledge to new slots.
Approach:	They propose a Semi-supervised Incremental Clustering method to discover new slots with existing linguistic annotation models and limited known slot data.
Outcome:	The proposed method significantly outperforms state-of-the-art models on five public datasets.

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Dainis A. Boumber, Fatima Zahra Qachfar, Rakesh Verma

Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets (2023.findings-acl)

Challenge:	Existing research focuses predominantly on specific fields, which results in the need for clarity on linguistic markers associated with deception.
Approach:	They propose a domain-independent fraud detection benchmark with 100,000 honest and misleading statements in seven domains and a parameter-efficient finetuning adapter to improve tuning methods.
Outcome:	The proposed adapter outperforms all competition on the DIFrauD benchmark and is able to predict the performance of the proposed model.

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Rui Wang, Tong Yu, Junda Wu, Handong Zhao, Sungchul Kim, Ruiyi Zhang, Subrata Mitra, Ricardo Henao

AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization (2021.naacl-main)

Challenge:	a federated domain adaptation approach is used to learn with NER datasets from multiple platforms while not violating data privacy.
Approach:	They propose to use a distillation approach to facilitate knowledge transfer across platforms.
Outcome:	The proposed model performs better in the clinic domain.

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Tiezheng Yu, Zihan Liu, Pascale Fung

From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation (2025.coling-main)

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.

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Ali Marashian, Enora Rice, Luke Gessler, Alexis Palmer, Katharina von der Wense

Iterative Domain-Repaired Back-Translation (2020.emnlp-main)

Challenge:	Existing data for low-resource languages are limited; the languages that could most benefit from domain adaptation (DA) are the ones left behind.
Approach:	They propose a realistic setting in which they aim to translate between a high-resource and a low-resourced language with limited parallel data, a bilingual dictionary, and c) a monolingual target-domain corpus in the high-rsource language.
Outcome:	The proposed methods are compared with a human evaluation of DALI and show that the most effective is the simplest.

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Hao-Ran Wei, Zhirui Zhang, Boxing Chen, Weihua Luo

Dynamic Data Selection and Weighting for Iterative Back-Translation (2020.emnlp-main)

Challenge:	Existing studies show that NMT models perform poorly in specific domains when in-domain parallel corpora are scarce or nonexistent.
Approach:	They propose an iterative domain-repaired back-translation framework to refine translations in bilingual data by round-trip translating monolingual sentences.
Outcome:	The proposed framework achieves 15.79 and 4.47 BLEU improvements over unadapted models and back-translation in domain-specific translations.

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Zi-Yi Dou, Antonios Anastasopoulos, Graham Neubig

Rule-based Morphological Inflection Improves Neural Terminology Translation (2021.emnlp-main)

Challenge:	Experimental results demonstrate that our methods achieve improvements of up to 1.8 BLEU points over competitive baselines.
Approach:	They propose a data selection and weighting strategy to iterate back-translation models and apply it to it . they use a target language to back-transcribe monolingual data, which is of high quality and reflect the target domain.
Outcome:	The proposed approach achieves 1.8 BLEU points over baselines on domain adaptation, low-resource, and high-resourced MT settings and on two language pairs.

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Weijia Xu, Marine Carpuat

Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network (D19-1)

Challenge:	Current approaches to incorporating terminology constraints in machine translation (MT) typically assume that the constraint terms are provided in their correct morphological forms.
Approach:	They propose a framework for incorporating lemma constraints in machine translation . they use a cross-lingual inflection module that inflects the target lemmo constraints based on the source context.
Outcome:	The proposed framework outperforms existing methods with lower training costs and linguistic knowledge in domain adaptation and low-resource MT settings.

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Shuqing Bian, Wayne Xin Zhao, Yang Song, Tao Zhang, Ji-Rong Wen

Improving Few-Shot Domain Transfer for Named Entity Disambiguation with Pattern Exploitation (2022.findings-emnlp)

Challenge:	Existing methods to solve person-job fit in single-domain setting are limited by labeled data.
Approach:	They propose a deep global match network for capturing the global semantic interactions between two sentences from a job posting and a candidate resume respectively.
Outcome:	The proposed model is effective when there is not enough labeled data.

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Philip Blair, Kfir Bar

Detecting Proxy Gaming in RL and LLM Alignment via Evaluator Stress Tests (2026.findings-acl)

Challenge:	Named entity disambiguation is a critical subtask of entity linking . a model can be trained on a domain, but it needs to be adapted to the domain .
Approach:	They propose to reformulate named entity disambiguation as a masked language modeling problem.
Outcome:	The proposed model improves on a mental health news dataset without sacrifices in accuracy.

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Sanjeda Akter, Ibne Farabi Shihab, Anuj Sharma

Challenge:	Proxy optimization is a challenge spanning reinforcement learning and LLM alignment.
Approach:	They propose an invariance-based framework that detects proxy gaming by separating exploitable sensitivity from content-driven improvements using semantic validity audits.
Outcome:	The proposed framework achieves 78.4% precision and 81.7% recall across 15 environments and 5 algorithms.

DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation (2022.acl-long)

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Nitay Calderon, Eyal Ben-David, Amir Feder, Roi Reichart

Multi-Stage Pre-training for Automated Chinese Essay Scoring (2020.emnlp-main)

Challenge:	Existing domain adaptation (DA) algorithms are not able to handle out-of-distribution examples due to the costly and labor-intensive data labeling process.
Approach:	They propose a controllable generation approach to deal with domain adaptation challenge by generating a domain-counterfactual textual example from an input text.
Outcome:	The proposed approach outperforms baselines and improves accuracy of state-of-the-art unsupervised DA algorithm.

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Wei Song, Kai Zhang, Ruiji Fu, Lizhen Liu, Ting Liu, Miaomiao Cheng

SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains (2025.naacl-long)

Challenge:	Existing methods for automatic essay scoring are based on hand-crafted surface-level features, but recent advances in representation learning have improved performance.
Approach:	They propose a pre-training based automated Chinese essay scoring method with weakly supervised pre- training, supervised cross- prompt fine-tuning and supervised target- prompt refine-tuneing.
Outcome:	The proposed method improves a state-of-the-art neural essay scorer in terms of effectiveness and domain adaptation ability, while in-depth analysis also reveals its limitations.

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Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai, Yaochen Xie, Xianfeng Tang, Chen Luo, Yang Li, Joyce C. Ho, Carl Yang, Qi He

Learning Kernel-Smoothed Machine Translation with Retrieved Examples (2021.emnlp-main)

Challenge:	Retrieval-augmented generation (RAG) enhances the question answering abilities of large language models (LLMs) however, adapting general-purpose RAG systems to specialized fields poses unique challenges due to distribution shifts and limited access to domain-specific data.
Approach:	They propose a method that equips large language models with joint capabilities of question answering and question generation for domain adaptation.
Outcome:	Experiments on 11 datasets across three different domains verify the efficacy of SimRAG over baselines by 1.2%–8.6%.

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Qingnan Jiang, Mingxuan Wang, Jun Cao, Shanbo Cheng, Shujian Huang, Lei Li

Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation (P19-1)

Challenge:	Existing methods to update deployed models are prone to overfit . however, non-parametric methods are liable to over-fit the retrieved examples .
Approach:	They propose to learn Kernel-Smoothed Translation with Example Retrieval (KSTER) this approach allows users to adapt models to emerging cases without retraining .
Outcome:	The proposed approach achieves 1.1 to 1.5 BLEU scores over existing methods without retraining . the proposed model is released on https://github.com/jiangqn/KSTER.

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Yftah Ziser, Roi Reichart

Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift (2020.lrec-1)

Challenge:	Existing approaches to domain adaptation (DA) require labeled data that can be found in only a handful of domains.
Approach:	They propose a task-refinement learning approach to solve pivot detection problems . they propose to train PBLM models with gradually increasing information exposed about each pivot .
Outcome:	The proposed approach achieves state-of-the-art accuracy in six domain adaptation setups for sentiment classification.

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Matej Martinc, Petra Kralj Novak, Senja Pollak

Representation Learning

Challenge:	Existing methods for word embeddings have been used to model semantic relations with word embeds.
Approach:	They propose a method that leverages contextual embeddings for diachronic semantic shift detection by generating time specific word representations from BERT embedds.
Outcome:	The proposed method performs comparable to the current state-of-the-art without time consuming domain adaptation on large corpora.

CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection (2022.coling-1)

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Jens Lemmens, Jens Van Nooten, Tim Kreutz, Walter Daelemans

Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning (2022.coling-1)

Challenge:	CoNTACT is a Dutch language model adapted to the domain of COVID-19 tweets . a turbulent vaccine debate has emerged between advocates and opponents of vaccines - a polarization that will continue to influence future views on vaccines.
Approach:	They propose a Dutch language model adapted to the domain of COVID-19 tweets . they use 2.8M Dutch COVId-19 related tweets posted in 2021 to test the model .
Outcome:	The proposed model shows statistically significant gains over RobBERT on two tasks.

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Qingyu Tan, Ruidan He, Lidong Bing, Hwee Tou Ng

Asymmetric Mutual Learning for Multi-source Unsupervised Sentiment Adaptation with Dynamic Feature Network (2022.coling-1)

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.

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Rui Li, Cheng Liu, Dazhi Jiang

Unmasking the Imposters: How Censorship and Domain Adaptation Affect the Detection of Machine-Generated Tweets (2025.coling-main)

Challenge:	Recent work on pre-trained language models (PrLMs) on labeled sentiment datasets has shown significant improvements on widerange of NLP tasks, including sentiment classification.
Approach:	They propose a multi-source unsupervised sentiment adaptation problem with pre-trained features to exploit the extracted pre-train features for efficient domain adaptation.
Outcome:	The proposed model outperforms the state-of-the-art methods on multiple sentiment benchmarks and extensive ablation studies to verify the effectiveness of each module.

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Bryan E. Tuck, Rakesh Verma

Cross-Domain Fake News Detection based on Dual-Granularity Adversarial Training (2025.coling-main)

Challenge:	generative AI has been used to generate fluent and convincing text on social media platforms . a new study examines the generative capabilities of four popular large language models .
Approach:	They propose a methodology to examine the generative capabilities of four prominent LLMs on Twitter using a dataset from Llama 3, Mistral, Qwen2 and GPT4o.
Outcome:	The proposed method examines the generative capabilities of four prominent LLMs on Twitter.

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Wenjie Wei, Yanyue Zhang, Jinyan Li, Panfei Liu, Deyu Zhou

Together We Can: Multilingual Automatic Post-Editing for Low-Resource Languages (2024.findings-emnlp)

Challenge:	Existing approaches to detect fake news in unseen domains are limited by domain-specific training.
Approach:	They propose a cross-domain fake news detection method based on adversarial training . they use a document-level and entity-level model to generate domain-independent representations .
Outcome:	The proposed method can detect fake news in unseen domains with the help of pre-trained language models.

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Sourabh Deoghare, Diptesh Kanojia, Pushpak Bhattacharyya

KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation (2020.acl-main)

Challenge:	Existing studies on multilingual automatic post-editing systems for low-resource Indo-Aryan languages have focused on different models for different language pairs.
Approach:	They propose to use a multilingual automatic post-editing system to improve machine translations for low-resource Indo-Aryan languages.
Outcome:	The proposed model outperforms English-Hindi and English-Marathi models by 2.5 and 2.39 TER points.

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Hao Zhou, Chujie Zheng, Kaili Huang, Minlie Huang, Xiaoyan Zhu

Meta-Reinforced Multi-Domain State Generator for Dialogue Systems (2020.acl-main)

Challenge:	Existing knowledge-driven dialog data is limited due to the lack of dialog data which consists of multi-turn conversations on multiple topics and with knowledge annotations.
Approach:	They propose a Chinese multi-domain knowledge-driven conversation dataset which grounds the topics in multi-turn conversations to knowledge graphs.
Outcome:	The proposed dataset can be enhanced by introducing background knowledge, but there is still a large space for leveraging knowledge to model multi-turn conversations for further research.

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Yi Huang, Junlan Feng, Min Hu, Xiaoting Wu, Xiaoyu Du, Shuo Ma

Hands-off Image Editing: Language-guided Editing without any Task-specific Labeling, Masking or even Training (2025.coling-main)

Challenge:	Existing methods to train a multi-domain dialogue state tracker are lacking in accuracy.
Approach:	They propose a Meta-Reinforced Multi-Domain State Generator to train a DST meta-learning model with a few domains as source domains and a new domain as target domain.
Outcome:	The proposed system outperforms the traditional training approach with extremely little training data in target domain.

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Rodrigo Santos, António Branco, João Ricardo Silva, Joao Rodrigues

Multimodality

Challenge:	State-of-the-art approaches to this task resort to supervised training and labelling, masking or training.
Approach:	They propose an approach that does without any task-specific supervision and offers thus a better potential for improvement.
Outcome:	The proposed approach achieves very competitive performance and scales up in a way that requires no task-specific supervision.

FewRel 2.0: Towards More Challenging Few-Shot Relation Classification (D19-1)

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Tianyu Gao, Xu Han, Hao Zhu, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Unsupervised Domain Adaptation of Language Models for Reading Comprehension (2020.lrec-1)

Challenge:	Few-shot domain adaptation and NOTA detection are two real-world challenges for few-shot relation classification models.
Approach:	They propose a task to investigate two aspects of few-shot relation classification models . they build upon the FewRel dataset by adding a new test set in a different domain .
Outcome:	The proposed task can evaluate few-shot domain adaptation and few- shot none-of-the-above detection on a new domain and NOTA relation choice.

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Kosuke Nishida, Kyosuke Nishida, Itsumi Saito, Hisako Asano, Junji Tomita

Towards Domain-Agnostic and Domain-Adaptive Dementia Detection from Spoken Language (2023.acl-long)

Challenge:	State-of-the-art reading comprehension models do not have general linguistic intelligence . accuracy of out-domain datasets is affected by the distribution of data .
Approach:	They propose to use supervised RC training data in the source domain and unlabeled passages in the target domain to adapt models.
Outcome:	The proposed model outperforms the model without domain adaptation with five datasets in different domains.

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Shahla Farzana, Natalie Parde

Unsupervised Multi-View Post-OCR Error Correction With Language Models (2021.emnlp-main)

Challenge:	Domain adaptation (DA) techniques have been used to improve performance of NLP systems for healthcare tasks due to numerous complexities of data.
Approach:	They propose to use domain adaptation techniques to improve generalizability across diverse datasets for dementia detection.
Outcome:	The proposed model achieves a 22% increase in accuracy adapting from a conversational to task-oriented dataset compared to a jointly trained baseline.

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Harsh Gupta, Luciano Del Corro, Samuel Broscheit, Johannes Hoffart, Eliot Brenner

MedScore: Generalizable Factuality Evaluation of Open-ended Long-form Medical Answers by Domain-adapted Claim Decomposition and Verification (2026.findings-acl)

Challenge:	Prior work used text generation techniques or redundancy in similar passages for OCR error correction, which is not appropriate in cases of low corpus redundancies or weak document contextual information.
Approach:	They propose to use a pretrained language model to reconcile different OCR views in unsupervised way so that their combination contains fewer errors than each individual view.
Outcome:	The proposed model can reconcile multiple OCR views so that their combined version contains fewer errors than the best OCR view.

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Heyuan Huang, Alexandra DeLucia, Vijay Murari Tiyyala, Mark Dredze

Challenge:	Existing factuality evaluation pipelines are poor matches for medical domains . existing methods are limited to objective, entity-centric, formulaic texts .
Approach:	They propose a pipeline to decompose medical answers into condition-aware valid facts . they use a decomposition-then-verify approach to evaluate generated text .
Outcome:	The proposed method extracts up to three times as many valid facts as existing methods . the resulting factuality score substantially varies by decomposition method, corpus, and used backbone LLM .

Domain adapted machine translation: What does catastrophic forgetting forget and why? (2024.emnlp-main)

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Danielle Saunders, Steve DeNeefe

Improving the Detection of Multilingual Online Attacks with Rich Social Media Data from Singapore (2023.acl-long)

Challenge:	Neural Machine Translation (NMT) models can be specialized by domain adaptation, often fine-tuning on a dataset of interest.
Approach:	They propose a novel approach to understanding catastrophic forgetting during NMT adaptation by investigating the relationship between the data and the in-domain vocabulary coverage.
Outcome:	The proposed model can be specialized by fine-tuning on a domain of interest, but can fail to achieve the predicted quality of the target domain.

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Janosch Haber, Bertie Vidgen, Matthew Chapman, Vibhor Agarwal, Roy Ka-Wei Lee, Yong Keong Yap, Paul Röttger

Word Matters: What Influences Domain Adaptation in Summarization? (2024.acl-long)

Challenge:	Toxic content is a global problem, but most resources for detecting toxic content are in English . new datasets and models for non-English languages focus exclusively on one language or dialect .
Approach:	They propose to use a multilingual dataset of online attacks to identify code-mixed toxic content in Singapore . they collect reddit comments in Indonesian, Malay, Singlish, and other languages and provide fine-grained hierarchical labels for attacks .
Outcome:	The proposed dataset provides fine-grained hierarchical labels for online attacks in Singapore . it shows that the metadata can be used for granular error analysis .

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Yinghao Li, Siyu Miao, Heyan Huang, Yang Gao

How Far Is Too Far? Studying the Effects of Domain Discrepancy on Masked Language Models (2024.lrec-main)

Challenge:	Large Language Models (LLMs) can generalize domain datasets unseen during training but are not able to predict domain adaptation performance.
Approach:	They propose to quantify dataset learning difficulty as the learning difficulty of generative summarization, which is determined by word-based compression rate and abstraction level.
Outcome:	The proposed model can predict performance on unknown domain datasets without training, and it is based on the findings.

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Subhradeep Kayal, Alexander Rakhlin, Ali Dashti, Serguei Stepaniants

FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation (2022.lrec-1)

Challenge:	Pre-trained masked language models perform strongly on a wide variety of NLP tasks.
Approach:	They propose a mechanism to quantify the difference in domains between the pre-trained model and the task and partition it using a cloze task.
Outcome:	The proposed model performs better on openly available e-commerce datasets than the original model on scientific and biomedical datasets.

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Wenhao Zhu, Shujian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen

LoRA-PAR: A Flexible Dual-System LoRA Partitioning Approach to Efficient LLM Fine-Tuning (2025.findings-emnlp)

Challenge:	Recent research on domain adaptation neglects diversity in translation within a domain . current research on NMT models considers very broad target domains .
Approach:	They propose a fine-grained domain adaptation task for autonomous vehicles, AI education, real-time networks, and smart phone.
Outcome:	The proposed task is compared with a dataset of Chinese-English translation tasks for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone.

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Yining Huang, Bin Li, Keke Tang, Meilian Chen

HYPERTTS: Parameter Efficient Adaptation in Text to Speech Using Hypernetworks (2024.lrec-main)

Challenge:	Large-scale generative models like DeepSeek-R1 and OpenAI-O1 benefit substantially from chain-of-thought reasoning, yet pushing their performance typically requires vast data, large model sizes, and full-parameter fine-tuning.
Approach:	They propose a dual-system LoRA framework that partitions data and parameters by System 1 or System 2 demands and adopts a two-stage fine-tuning strategy to enhance knowledge and intuition.
Outcome:	The proposed framework partitions data and parameters by System 1 or System 2 demands, using fewer yet more focused parameters for each task.

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Yingting Li, Rishabh Bhardwaj, Ambuj Mehrish, Bo Cheng, Soujanya Poria

Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP (2021.emnlp-main)

Challenge:	Neural text-to-speech (TTS) systems limited to predefined speaker styles or specific sets of speaker IDs.
Approach:	They propose a network that can adapt adapter parameters to new speakers . they compare two domain adaptation settings and find it to be very efficient .
Outcome:	The proposed Adapters improve speech synthesis performance on two domains and compare them with baselines.

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Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schoelkopf

Challenge:	a meta-analysis of published studies shows that the causal direction of data collection can explain some trends in NLP . semi-supervised learning and domain adaptation performance differ on a number of tasks .
Approach:	They argue that the causal direction of the data collection process has nontrivial implications . authors categorize common NLP tasks according to their causal direction . they also empirically assay the validity of the ICM principle for text data .
Outcome:	The proposed model can explain differences in semi-supervised learning and domain adaptation performance across settings.

FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue (2022.emnlp-main)

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Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang

Contrastive Domain Adaptation for Question Answering using Limited Text Corpora (2021.emnlp-main)

Challenge:	Prior studies of task transfer in dialogue consider only 2-4 tasks, focus on multitasks.
Approach:	They propose a benchmark for FEw-sample TAsk transfer in open-domain dialogue.
Outcome:	The proposed benchmark analyzes the transferability between 132 source-target task pairs and provides a baseline for future work.

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Zhenrui Yue, Bernhard Kratzwald, Stefan Feuerriegel

Exploring LLM Annotation for Adaptation of Clinical Information Extraction Models under Data-sharing Restrictions (2025.findings-acl)

Challenge:	Existing question generation methods rely on large amounts of synthetically generated datasets and costly computational resources.
Approach:	They propose a framework for domain adaptation that combines question generation and domain-invariant learning to answer out-of-domain questions in settings with limited text corpora.
Outcome:	The proposed framework improves on state-of-the-art questions in a domain with limited text corpora.

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Seiji Shimizu, Hisada Shohei, Yutaka Uno, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki

Integrating Pre-Trained Speech and Language Models for End-to-End Speech Recognition (2024.findings-acl)

Challenge:	In-hospital text data often contains valuable clinical information, yet fine-tuned small language models (SLMs) for information extraction remain challenging due to differences in formatting and vocabulary across institutions.
Approach:	They leverage large language models to annotate the target domain data for adaptation . they use in-hospital text data to extract clinical information .
Outcome:	The proposed model outperforms manual annotation on four clinical information extraction tasks with a larger number of annotated data.

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Yukiya Hono, Koh Mitsuda, Tianyu Zhao, Kentaro Mitsui, Toshiaki Wakatsuki, Kei Sawada

Joint Geometrical and Statistical Domain Adaptation for Cross-domain Code Vulnerability Detection (2023.emnlp-main)

Challenge:	Mainstream of automatic speech recognition (ASR) has shifted from pipeline methods to end-to-end (E2E) methods.
Approach:	They propose to integrate a pre-trained speech representation model and a large language model (LLM) for automatic speech recognition in an end-to-end manner.
Outcome:	The proposed model achieves comparable performance to modern E2E ASR models by utilizing powerful pre-training models with the proposed integrated approach.

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Qianjin Du, Shiji Zhou, Xiaohui Kuang, Gang Zhao, Jidong Zhai

InfoEnh: Towards Multimodal Sentiment Analysis via Information Bottleneck Filter and Optimal Transport Alignment (2024.lrec-main)

Challenge:	Existing approaches to detect code vulnerability are limited by labeled training data on target domains.
Approach:	They propose a cross-domain code vulnerability detection framework called MNCRI . they propose mutual nearest neighbor contrastive learning to align the source and target domains .
Outcome:	The proposed framework outperforms state-of-the-art methods in cross-domain code vulnerability detection tasks.

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Yifeng Xie, Zhihong Zhu, Xuan Lu, Zhiqi Huang, Haoran Xiong

Multimodality

Challenge:	Existing methods for multi-modal sentiment analysis have been developed to overcome these challenges.
Approach:	They propose a method that utilizes a masking technique as the bottleneck for information filtering and integrates all modalities into a common feature space via domain adaptation.
Outcome:	Extensive experiments on two benchmark MSA datasets show the proposed method performs better than baselines.

Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation (2024.lrec-main)

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Mateusz Klimaszewski, Piotr Andruszkiewicz, Alexandra Birch

Generate, Discriminate, Evolve: Enhancing Context Faithfulness via Fine-Grained Sentence-Level Self-Evolution (2025.findings-acl)

Challenge:	Existing approaches to modularity are limited to the case of pre-trained modules in a pre-training language model.
Approach:	They propose a method that allows the transfer of pre-trained PEFT modules between incompatible PLMs without any change in the inference complexity.
Outcome:	The proposed method allows the transfer of modules between incompatible PLMs without any change in the inference complexity.

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Kun Li, Tianhua Zhang, Yunxiang Li, Hongyin Luo, Abdalla Mohamed Salama Sayed Moustafa, Xixin Wu, James R. Glass, Helen M. Meng

Challenge:	Existing methods to improve context faithfulness in large language models are either inadequate or overlook the potential for self-improvement.
Approach:	They propose a framework that enhances context faithfulness through fine-grained sentence-level optimization.
Outcome:	Experiments on ASQA and ConFiQA datasets show that GenDiE surpasses baselines in faithfulness and correctness and exhibits robust performance for domain adaptation.

VE-KD: Vocabulary-Expansion Knowledge-Distillation for Training Smaller Domain-Specific Language Models (2024.findings-emnlp)

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Pengju Gao, Tomohiro Yamasaki, Kazunori Imoto

MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation (2025.findings-acl)

Challenge:	VE-KD is a method that balances knowledge distillation and vocabulary expansion with the aim of training efficient domain-specific language models.
Approach:	They propose a method that balances knowledge distillation and vocabulary expansion with the aim of training efficient domain-specific language models.
Outcome:	VE-KD outperforms DistilBERT and Adapt-and-Distill in biomedical domain tasks . compared with other methods, it outperformed Distilbert and adapted-and distill .

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Haris Riaz, Sourav Sanjukta Bhabesh, Vinayak Arannil, Miguel Ballesteros, Graham Horwood

Exploring Linguistic Properties of Monolingual BERTs with Typological Classification among Languages (2023.findings-emnlp)

Challenge:	Recent smaller language models rely on synthetic data generated using larger Language models.
Approach:	They propose a method for generating synthetic data that enhances diversity through meta-prompting . they use 25 million tokens of synthetic data generated by a language model orchestrated by multiple “expert” LLM agents to collaboratively generate data.
Outcome:	The proposed method outperforms the base LLM in Finance and Biomedicine with 25 million tokens of synthetic data.

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Elena Ruzzetti, Federico Ranaldi, Felicia Logozzo, Michele Mastromattei, Leonardo Ranaldi, Fabio Zanzotto

Information Retrieval

Challenge:	a recent study examined how models for typologically similar languages encode structural information.
Approach:	They propose to layer-wise compare transformers for typologically similar languages to observe similarities . they use a domain adaptation on semantically equivalent texts to measure similarity .
Outcome:	The proposed model outperforms all other models on unseen sentences . the proposed model is based on a typologically similar language .

Adversarial Alignment with Anchor Dragging Drift (A3D2): Multimodal Domain Adaptation with Partially Shifted Modalities (2025.acl-long)

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Jun Sun, Xinxin Zhang, Simin Hong, Jian Zhu, Lingfang Zeng

GCML: Gradient Coherence Guided Meta-Learning for Cross-Domain Emerging Topic Rumor Detection (2025.emnlp-main)

Challenge:	Domain adaptation is underexplored in multimodal learning environments due to expensive data collection and annotation.
Approach:	They propose a bi-alignment scheme to perform drift-drift and anchor-driving matching with partially shifting anchors.
Outcome:	The proposed approach achieves superior performance compared with state-of-the-art approaches.

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Zejiang He, Jingyuan Huang, Menglong Lu, Zhen Huang, Shanshan Liu, Zhiliang Tian, Dongsheng Li

3DS: Medical Domain Adaptation of LLMs via Decomposed Difficulty-based Data Selection (2025.emnlp-main)

Challenge:	Existing domain adaptation rumor detection methods ignore the data generalization differences and rely on a large amount of unlabeled target domain samples to achieve domain adaptation.
Approach:	They propose a Gradient Coherence guided Meta-Learning approach for emerging topics rumor detection that selectively learns more "generalizable" tasks that are more beneficial in adapting to the target domain.
Outcome:	The proposed method outperforms baselines on real-world datasets and significantly outperformed traditional methods on the in-domain condition.

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Hongxin Ding, Yue Fang, Runchuan Zhu, Xinke Jiang, Jinyang Zhang, Yongxin Xu, Weibin Liao, Xu Chu, Junfeng Zhao, Yasha Wang

LegoSLM: Connecting LLM with Speech Encoder using CTC Posteriors (2025.findings-emnlp)

Challenge:	Effective domain adaptation typically involves supervised fine-tuning on carefully selected instruction-tuned data.
Approach:	They propose a model-centric data selection framework that aligns data selection with the model’s knowledge distribution to improve model performance.
Outcome:	The proposed framework outperforms existing methods by up to 2.97% accuracy in the healthcare domain.

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Rao Ma, Tongzhou Chen, Kartik Audhkhasi, Bhuvana Ramabhadran

Adaptive Platt Scaling with Causal Interpretations for Self-Reflective Language Model Uncertainty Estimates (2025.findings-emnlp)

Challenge:	Recent studies show that pre-trained speech encoders and large language models can perform suboptimal performance on a range of spoken language processing tasks.
Approach:	They propose to combine large-scale pre-trained speech encoders and large-language models for better performance on automatic speech recognition tasks.
Outcome:	The proposed model can get an average of 49% WER reduction over the baseline model on 8 MLS testsets.

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Anthony Sicilia, Malihe Alikhani

Unlocking LLMs’ Self-Improvement Capacity with Autonomous Learning for Domain Adaptation (2025.findings-acl)

Challenge:	a new study examines the ability of large language models to self-monitor and ask for human intervention.
Approach:	They propose a formal analysis of LLM self-reflection for uncertainty estimation using domain adaptation theory.
Outcome:	The proposed method improves accuracy and human interpretation on reasoning tasks.

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Ke Ji, Junying Chen, Anningzhe Gao, Wenya Xie, Xiang Wan, Benyou Wang

Yang Zhao, Li Du, Xiao Ding, Yangou Ouyang, Hepeng Wang, Kai Xiong, Jinglong Gao, Zhouhao Sun, Dongliang Xu, Qing Yang, Dongchen Li, Bing Qin, Ting Liu

Challenge:	Existing models that use self-supervised and instruction fine-tuning can be trained using unlabeled corpora.
Approach:	They propose to use unlabeled target corpora to adapt large language models to new domains . they propose to employ self-supervised pre-training and instruction fine-tuning methods .
Outcome:	The proposed model can adapt to new domains using only a large amount of unlabeled target corpora.

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SParK-Eval: Evaluating Structure-Aware Knowledge Acquisition in LLMs for Domain Adaptation to Industrial Records (2026.findings-acl)

Challenge:	Existing methods for selecting training data from general datasets fail to account for the joint distribution of instructions, resulting in inefficient learning and suboptimal knowledge transfer.
Approach:	They propose a method that constructs a mixed gradient-based instruction graph to capture the joint distribution and interdependencies among instructions.
Outcome:	The proposed method outperforms existing methods on domain adaptation tasks and in complex, data-scarce scenarios.

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Ekant Muljibhai Amin, Yuta Koreeda, Yasuhiro Sogawa

Select High-quality Synthetic QA Pairs to Augment Training Data in MRC under the Reward Guidance of Generative Language Models (2024.lrec-main)

Challenge:	Large Language Models (LLMs) often struggle in domain adaptation for industrial settings where available corpora are limited and structurally diverse.
Approach:	They propose a framework that constructs question–answer pairs from pretraining data and annotates each with its input structure.
Outcome:	The proposed framework can be used to analyze how input structure affects parametric knowledge acquisition during domain-adaptive pretraining.

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Jing Jin, Houfeng Wang

Mixture of Small and Large Models for Chinese Spelling Check (2025.acl-long)

Challenge:	Existing approaches focus on downstream metrics to select QA pairs, which lack generalization across different datasets.
Approach:	They propose a general selection method that uses a large pre-trained language model as a reward model in a Reinforcement Learning framework for the training of the selection agent.
Outcome:	The proposed method improves performance on generative and extractive datasets.

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Ziheng Qiao, Houquan Zhou, Zhenghua Li

Challenge:	Chinese Spelling Check (CSC) tasks have been developed to correct spelling errors in given sentences . fine-tuned BERT-based models show excellent performance but suffer from edit pattern overfitting . a novel mixture approach that effectively combines small models and LLMs during beam search decoding phase improves accuracy and fluency of LLM.
Approach:	They propose a dynamic mixture approach that effectively combines small models and LLMs during beam search decoding phase.
Outcome:	The proposed method significantly boosts error correction capabilities, achieving state-of-the-art results across multiple datasets.

Beyond Online Sampling: Bridging Offline-to-Online Alignment via Dynamic Data Transformation for LLMs (2025.emnlp-main)

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Zhang Zhang, Guhao Feng, Jian Guan, Di He, Wei Wu

Embracing Anisotropy: Turning Massive Activations into Interpretable Control Knobs for Large Language Models (2026.acl-long)

Challenge:	Direct Preference Optimization (DPO) eliminates complex reward modeling in aligning large language models with human preferences, but its online variant faces significant efficiency bottlenecks due to costly real-time preference sampling and the reward model annotation.
Approach:	They propose a framework that transforms static datasets into dynamically adaptive equivalents without the need for an explicit reward model.
Outcome:	The proposed approach matches or exceeds the performance of a fully online DPO.

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Youngji Roh, Hyunjin Cho, Jaehyung Kim

Explainability & Interpretability in NLP

Challenge:	Prior work shows that Large Language Models exhibit highly anisotropic internal representations . prior work shows specialized dimensions capture domain-specific features .
Approach:	They propose a simple magnitude-based criterion to identify Domain-Critical Dimensions in a training-free manner.
Outcome:	The proposed method outperforms whole-dimension steering in domain adaptation and jailbreaking scenarios.

Towards Robust In-Context Learning for Machine Translation with Large Language Models (2024.lrec-main)

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Shaolin Zhu, Menglong Cui, Deyi Xiong

Training BERT Models to Carry over a Coding System Developed on One Corpus to Another (2024.lrec-main)

Challenge:	Experimental results demonstrate the effectiveness of our method, particularly in domain adaptation.
Approach:	They propose a method to retrieve translation pairs as demonstrations from an additional datastore to guide translation without updating the LLMs.
Outcome:	The proposed method reduces noise and improves translation performance in domain adaptation.

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Dalma Galambos, Pal Zsamboki

MedCoach: Enhancing Medical Reasoning in LLMs via Knowledge Graph-Augmented Chain-of-Thought Distillation (2026.findings-acl)

Challenge:	a pilot project aims to track trends in the perception of literary translation around the 1989 political transformation in Hungary.
Approach:	They train BERT models to carry over a coding system developed on a journal to another . aim is to track trends in perception of literary translation around 1989 political transformation .
Outcome:	The proposed system can carry over from one coding system to another, the authors show . the system can improve performance and provide better predictions from an ensemble .

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Chuan Li, Ye Lyu, Chengyu Wang, Mingyuan Fan, Cen Chen

Lost in Translation: Do LVLM Judges Generalize Across Languages? (2026.findings-acl)

Challenge:	Existing methods for training specialized reasoning models for the medical domain are limited due to the scarcity of high-quality, large-scale Chain-of-Thought (CoT) data.
Approach:	They propose a framework that introduces a dedicated coach role to guide the student model through question decomposition.
Outcome:	The proposed framework smooths the learning curve in medical reasoning by facilitating domain adaptation before advancing to complex long-chain reasoning.

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Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Mir Tafseer Nayeem, Amran Bhuiyan, Mizanur Rahman, Shafiq Joty, Enamul Hoque, Jimmy Huang

CAMEC: Complexity-Aware Multi-Expert Collaboration for Reliable Chinese Medical Question Answering (2026.acl-long)

Challenge:	MM-JudgeBench is the first large-scale benchmark for multilingual and multimodal judge model evaluation.
Approach:	They propose a multilingual benchmark for multilingual and multimodal judge model evaluation that includes over 60K pairwise preference instances spanning 25 typologically diverse languages.
Outcome:	The proposed benchmark includes over 60K pairwise preference instances spanning 25 languages.

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Yukang Wu, Xiyuan Jia, Jiayi Wu, HongChen Yu, Yuhan Qiu, Wu Guohua

Question Answering

Challenge:	Large language models are promising for medical question answering in china, but remain unreliable due to hallucinations, weak factual grounding and difficulty handling clinically complex cases.
Approach:	They propose a framework that combines hierarchical medical adaptation with complexity-aware expert routing for reliable Chinese medical QA.
Outcome:	The proposed framework outperforms strong general and medical LLM baselines on four Chinese medical benchmarks.

Learning Faster with Better Tokens: Parameter-Efficient Vocabulary Adaptation for Specialized Text Summarization (2026.acl-long)

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Gunjan Balde, Soumyadeep Roy, Mainack Mondal, Niloy Ganguly