Papers with transfer
Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+ (2026.eacl-srw)
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York Hay Ng, Aditya Khan, Xiang Lu, Matteo Salloum, Michael Zhou, Phuong Hanh Hoang, A. Seza Doğruöz, En-Shiun Annie Lee
| Challenge: | Existing linguistic knowledge bases such as URIEL+ lack a principled method for aggregating these signals into a single, comprehensive score. |
| Approach: | They propose a framework for type-matched language distances that unifies these signals into a robust, task-agnostic composite distance. |
| Outcome: | The proposed representations improve transfer performance when the distance type is relevant to the task, while yielding gains in most tasks. |
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition (2022.acl-long)
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| Challenge: | Existing models for named entity recognition only consider the potential transferability between two identical tasks across both domains. |
| Approach: | They propose to use a similarity metric model to improve cross-lingual named entity recognition task on target domain. |
| Outcome: | Empirical studies on 7 different languages confirm the effectiveness of the proposed model. |
Emoji-Based Transfer Learning for Sentiment Tasks (2021.eacl-srw)
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| Challenge: | Sentiment tasks such as hate speech detection and sentiment analysis are often low-resource . a transfer learning approach is used to transfer the emotional information encoded in emojis to a sentiment task . |
| Approach: | They exploit emotional information encoded in emojis to enhance performance on sentiment tasks . they use a transfer learning approach where parameters learned by an e-based source task are transferred to a sentiment target task . |
| Outcome: | The proposed method improves sentiment tasks on languages other than English with high emoji content and label distribution under three conditions. |
So Different Yet So Alike! Constrained Unsupervised Text Style Transfer (2022.acl-long)
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| Challenge: | Automated transfer of text between domains does not maintain other attributes between the source and translated text. |
| Approach: | They propose a method for automatic transfer of text between domains that preserves semantic content but changes other attributes. |
| Outcome: | The proposed method retains lexical, syntactic and domain-specific constraints between domains for multiple benchmark datasets, including ones where more than one attribute change. |
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models (2021.naacl-main)
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| Challenge: | Language models are a new standard to build state-of-the-art NLP systems. |
| Approach: | They compare multilingual and monolingual models on unseen languages . they show that some languages benefit from transfer learning whereas others don't . |
| Outcome: | The proposed model behaves in multiple ways on unseen languages, while others fail to transfer . the results provide a promising direction towards making multilingual models useful for a new set of unseense languages. |
Machine Translation for Livonian: Catering to 20 Speakers (2022.acl-short)
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| Challenge: | Livonian is one of the most endangered languages in Europe with just a tiny handful of speakers and virtually no publicly available corpora. |
| Approach: | They aim to develop machine translation between Livonian and English using a linguistic similarity test and a dataset of parallel and monolingual data. |
| Outcome: | The proposed systems and the collected data, including a manually translated and verified translation benchmark, are publicly released via OPUS and Huggingface repositories. |
Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation (2021.naacl-main)
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Alexander Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, Yashar Mehdad
| Challenge: | Recent advances in summarization are driven by the availability of large datasets such as the CNN-DailyMail corpus and the New York Times corpus. |
| Approach: | They propose a method for fine-tuning pretrained models for summarization in unsupervised manner . they use Wikipedia data to produce pseudo-summaries which contain characteristics of target dataset . |
| Outcome: | The proposed method achieves state-of-the-art, zero-shot abstractive summarization performance on CNN-DailyMail dataset and compares with other methods on other datasets. |
Cross-lingual Few-Shot Learning on Unseen Languages (2022.aacl-main)
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| Challenge: | Large pre-trained language models have demonstrated the ability to obtain good performance on downstream tasks with limited examples in resource-rich languages. |
| Approach: | They propose to use a downstream sentiment analysis task to analyze the effectiveness of several few-shot learning strategies across 12 languages, including 8 unseen languages, to compare results. |
| Outcome: | The proposed model, XLM-R, gives the best performance on a task with few examples in resource-rich languages. |
Learning Universal Authorship Representations (2021.emnlp-main)
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Rafael A. Rivera-Soto, Olivia Elizabeth Miano, Juanita Ordonez, Barry Y. Chen, Aleem Khan, Marcus Bishop, Nicholas Andrews
| Challenge: | authorship verification has traditionally relied on modeling stylometric linguistic properties . but neural methods introduce a tradeoff: they obviate the need for manual feature design . |
| Approach: | They propose to use domain-specific features to improve authorship representations . they propose to study Amazon reviews, fanfiction short stories, and Reddit comments . |
| Outcome: | The proposed methods outperform existing methods in large-scale authorship verification scenarios. |
Cross-lingual Alignment Methods for Multilingual BERT: A Comparative Study (2020.findings-emnlp)
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| Challenge: | Multilingual BERT (mBERT) has shown reasonable capability for zero-shot cross-lingual transfer when fine-tuned on downstream tasks. |
| Approach: | They propose to use parallel corpora and rotational alignment methods to improve transfer performance in a zero-shot setting. |
| Outcome: | The proposed method improves rotation-based alignment on Name Entity Recognition and Semantic Slot Filling tasks. |
ELLA: Efficient Lifelong Learning for Adapters in Large Language Models (2026.eacl-long)
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| Challenge: | Existing approaches to training Large Language Models (LLMs) suffer from catastrophic forgetting when adapted sequentially to new tasks in a continual learning (CL) setting. Existing methods are impractical and could potentially violate privacy. |
| Approach: | They propose a training framework built on the principle of selective subspace de-correlation that characterizes the structure of past updates and penalizes alignments along their high-energy, task-specific directions. |
| Outcome: | The proposed training framework achieves state-of-the-art CL performance on three popular benchmarks spanning both classification and generative tasks with relative accuracy gains of up to 9.6% and a 35 smaller memory footprint. |
Disentangling the Roles of Target-side Transfer and Regularization in Multilingual Machine Translation (2024.eacl-long)
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| Challenge: | Multilingual Machine Translation (MMT) benefits from knowledge transfer across different language pairs, but performance differences between one-to-many and many-to-1 translation are negligible. |
| Approach: | They conduct a large-scale study that varies the auxiliary target-side languages along two dimensions to show the dynamic impact of knowledge transfer on the main language pairs. |
| Outcome: | The proposed model can translate between multiple languages with minimal positive transfer ability. |
TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style Control (2024.emnlp-main)
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| Challenge: | Existing models fail to generate singing voices rich in stylistic nuances for unseen singers due to multifaceted nature of singing styles. |
| Approach: | They propose a zero-shot SVS model for style transfer across cross-lingual speech and singing styles and multi-level style control. |
| Outcome: | Experimental results show that TCSinger outperforms baseline models in synthesis quality, singer similarity, and style controllability. |
BAD-X: Bilingual Adapters Improve Zero-Shot Cross-Lingual Transfer (2022.naacl-main)
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| Challenge: | Massively multilingual Transformers (MMTs) have dominated research in multilingual NLP and cross-lingual transfer recently. |
| Approach: | They propose to learn bilingual language pair adapters (BAs) when the goal is to optimize performance for a particular source-target transfer direction. |
| Outcome: | The proposed framework improves performance in three standard downstream tasks and for the majority of low-resource languages. |
LEDGAR: A Large-Scale Multi-label Corpus for Text Classification of Legal Provisions in Contracts (2020.lrec-1)
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| Challenge: | Contractual provisions are a primary research target in law studies as they constitute the legal essence of a contract. |
| Approach: | They propose to use LEDGAR to construct a multilabel corpus of legal provisions in contracts that is crawled and scraped from the public domain. |
| Outcome: | The proposed corpus is the first freely available corpus of its kind. |
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning (2022.findings-naacl)
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| Challenge: | Recent work shows that Relation Extraction tasks can be recasted as Textual Entailment tasks using verbalizations. |
| Approach: | They propose to recasted RE tasks as Textual Entailment tasks using verbalizations . they show that entailment reduces the need for manual annotation to 50% and 20% . |
| Outcome: | The proposed method reduces the need for manual annotation to 50% and 20% in event argument extraction tasks while achieving the same performance as with full training. |
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding (2021.naacl-main)
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Rob van der Goot, Ibrahim Sharaf, Aizhan Imankulova, Ahmet Üstün, Marija Stepanović, Alan Ramponi, Siti Oryza Khairunnisa, Mamoru Komachi, Barbara Plank
| Challenge: | Lack of publicly available evaluation data for low-resource languages limits progress in SLU . despite advances in neural modeling for slot and intent detection, datasets for SLU remain limited. |
| Approach: | They propose a joint learning approach with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. |
| Outcome: | The proposed model can learn English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. |
Beyond Borders: Investigating Cross-Jurisdiction Transfer in Legal Case Summarization (2024.naacl-long)
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| Challenge: | a study explores the cross-jurisdictional generalizability of legal case summarization models . fine-tuning on non-target datasets outperforms unsupervised methods, but success depends on similarity between source and target jurisdictions. |
| Approach: | They explore how to effectively summarize legal cases of a target jurisdiction where reference summaries are not available. |
| Outcome: | The proposed model can be generalized across jurisdictions and improve transfer performance. |
Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents (2022.acl-long)
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| Challenge: | Document-level neural machine translation (DocNMT) is a powerful tool for integrating cross-sentence context into translations. |
| Approach: | They explore whether and how contextual modeling in DocNMT is transferable via multilingual modeling. |
| Outcome: | The proposed model can be used to transfer from teacher languages to student languages with no documents but sentence level data. |
To Translate or Not to Translate: A Systematic Investigation of Translation-Based Cross-Lingual Transfer to Low-Resource Languages (2024.naacl-long)
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| Challenge: | XLT with multilingual language models is superfluous, says a new study . mBERT, XLM-R and mT5 are effective for cross-lingual transfer, authors say . |
| Approach: | They propose to use multilingual language models to improve cross-lingual transfer (XLT) they propose to add reliable translations to training data for XLT even for non-MT languages . |
| Outcome: | The proposed approaches outperform zero-shot XLT with mLMs, the authors show . the authors believe their findings warrant a broader inclusion of more robust translation-based baselines in XL research. |
Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections (P19-1)
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| Challenge: | Current systems for syntactic analysis tasks rely heavily on large scale annotated data. |
| Approach: | They propose to learn a generative model with a structured prior that uses labeled source and unlabeled target data jointly. |
| Outcome: | The proposed model improves on part-of-speech tagging and dependency parsing tasks on English as the only source corpus and on a wide range of target languages. |
Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning (2023.findings-emnlp)
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| Challenge: | Large-scale pre-trained language models have been fine-tuned for various NLP tasks . prompt tuning is a method that optimizes the output of the model to adapt to downstream tasks based on the posterior distribution of the source task. |
| Approach: | They propose a Bayesian approach to prompt tuning that optimizes for adapting pre-trained language models to downstream tasks rather than fine-tuning full model parameters. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on benchmark NLP tasks. |
Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent Variables (2021.findings-emnlp)
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| Challenge: | Existing methods to achieve zero-shot translation suffer from spurious correlations between output language and language invariant semantics. |
| Approach: | They propose a method that denoizes the autoencoder objective based on pivot language into traditional training objective to improve translation accuracy on zero-shot directions. |
| Outcome: | The proposed method eliminates spurious correlations and outperforms state-of-the-art methods on two benchmark machine translation datasets. |
Towards Zero-Shot Multilingual Transfer for Code-Switched Responses (2023.acl-long)
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| Challenge: | Recent task-oriented dialog systems have had great success building English-based personal assistants, but extending these systems to a global audience may take tremendous efforts. |
| Approach: | They propose a framework that allows for efficient transfer by learning task-specific representations and encapsulating source and target language representations. |
| Outcome: | The proposed framework is able to successfully transfer language knowledge even when the target language corpus is limited. |
Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter (2025.findings-acl)
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| Challenge: | Prior work on cross-lingual transfer often focuses on a small set of languages from a few language families and/or a single task. |
| Approach: | They analyze cross-lingual transfer for 263 languages from a wide variety of language families . they include three popular NLP tasks: POS tagging, dependency parsing, topic classification . |
| Outcome: | The proposed approach is based on linguistic similarity measures for 263 languages . the results show that the effect of linguistic similarities on transfer performance depends on a range of factors . |
Visually Grounded Continual Language Learning with Selective Specialization (2023.findings-emnlp)
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| Challenge: | Existing studies on the role of model components in learning specialized or generalizable representations are lacking. |
| Approach: | They propose to analyze selection strategies for visually grounded continual language learning using two diagnostic datasets. |
| Outcome: | The proposed models outperform existing models and provide enough control and flexibility for a thorough model analysis. |
How Multilingual is Multilingual BERT? (P19-1)
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| Challenge: | Existing studies have shown that deep, contextualized language models can encode syntactic and named entity information, but they have focused on what models trained on English capture about English. |
| Approach: | They propose a multilingual model pre-trained from monolingual Wikipedia corpora . they show that multilingual BERT is surprisingly good at zero-shot cross-lingual model transfer . |
| Outcome: | The proposed model can find translation pairs, but it exhibits systematic deficiencies affecting certain language pairs. |
Emerging Cross-lingual Structure in Pretrained Language Models (2020.acl-main)
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| Challenge: | Recent work has shown that multilingual pretraining works, but is unable to measure these effects. |
| Approach: | They propose to use multilingual masked language modeling to train a model on concatenated text from multiple languages to find universal latent symmetries in embedding spaces. |
| Outcome: | The proposed models can be trained on concatenated text from multiple languages without shared vocabulary or domain similarity. |
Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers (D19-1)
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| Challenge: | linguistic typology has shown great promise in pre-neural parsing, but results for neural architectures have been mixed. |
| Approach: | They explore the task of leveraging typology in the context of cross-lingual dependency parsing. |
| Outcome: | The proposed approach improves performance in the context of cross-lingual dependency parsing. |
Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation (2023.emnlp-main)
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| Challenge: | Using a shared vocabulary is common practice in multilingual machine translation . however, when words overlap is small, e.g., using different writing systems, knowledge transfer is inhibited . |
| Approach: | They propose a re-parameterized method for building word embeddings using word equivalence classes and graph networks to fuse word embeds across languages. |
| Outcome: | The proposed method achieves evident BLEU improvements on high- and low-resource MNMT scenarios. |
Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs (2026.findings-acl)
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| Challenge: | Existing methods for large language models use parameter-efficient techniques such as Low-Rank Adaptation (LoRA) prior studies suggest that the inner A matrices are highly similar during training and therefore suitable for sharing. |
| Approach: | They propose an asymmetric multi-LoRA design with multiple A matrices and a single shared B in multi-task fine-tuning. |
| Outcome: | The proposed methods achieve more balanced performance across tasks with comparable or superior average accuracy relative to existing methods. |
New Protocols and Negative Results for Textual Entailment Data Collection (2020.emnlp-main)
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| Challenge: | Natural language inference data has proven useful in benchmarking and as pretraining data for tasks requiring language understanding. |
| Approach: | They propose four alternative protocols to improve annotation quality and diversity . they use 8.5k-example training sets to compare different protocols . |
| Outcome: | The proposed protocols improve the ease of training and quality of the examples. |
LiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs (2026.acl-long)
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Pei-Fu Guo, Yun-Da Tsai, Chun-Chia Hsu, Kai-Xin Chen, Ya An Tsai, Kai-Wei Chang, Nanyun Peng, Mi-Yen Yeh, Shou-De Lin
| Challenge: | Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either from genuine transfer or from prior exposure during pre-training. |
| Approach: | They propose a pipeline to isolate and measure cross-lingual knowledge transfer by identifying self-contained, time-sensitive knowledge entities from real-world domains and generating factual questions. |
| Outcome: | The proposed pipeline analyzes multiple LLMs across five languages and shows that cross-lingual transfer is strongly influenced by linguistic distance and often asymmetric across language directions. |
LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning (2025.findings-emnlp)
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| Challenge: | Existing LMM-based embedding models exhibit a high degree of overlap in similarity distribution between positive and negative pairs, making it challenging to distinguish hard negative pairs effectively. |
| Approach: | They propose a framework that improves the embedding model's representation learning for negative pairs based on their discriminative difficulty. |
| Outcome: | The proposed framework improves the embedding model's representation learning for negative pairs based on their discriminative difficulty. |
Improving Zero-shot Multilingual Neural Machine Translation by Leveraging Cross-lingual Consistency Regularization (2023.findings-acl)
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| Challenge: | Existing methods to improve zero-shot translation performance by learning language-agnostic representations and maximizing cross-lingual transfer have been proposed. |
| Approach: | They propose a cross-lingual consistency regularization to bridge the representation gap between different languages and boost zero-shot translation performance. |
| Outcome: | The proposed model improves translation performance on low-resource and high-res benchmarks and closes the sentence representation gap and aligns the representation space. |
Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual Generalization (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have implicitly transfer knowledge across languages, but not all languages have such generalization capabilities. |
| Approach: | They propose a meta-learning-based method to learn to align conceptual spaces of different languages to enhance cross-lingual generalization. |
| Outcome: | The proposed method achieves competitive results with state-of-the-art methods and narrows the performance gap between languages. |
Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens (2023.findings-emnlp)
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| Challenge: | Existing studies show that translation quality alone is not sufficient for measuring knowledge transfer in multilingual neural machine translation. |
| Approach: | They propose a method that measures representational similarities between languages to measure knowledge transfer. |
| Outcome: | The proposed method improves translation quality for low- and mid-resource languages across multiple datasets and models. |
Relation between Cross-Genre and Cross-Topic Transfer in Dependency Parsing (2024.lrec-main)
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| Challenge: | LAS scores in cross-genre transfer within and across treebanks align with topic distances. |
| Approach: | They propose to use topic modelling to assess whether genre is stable in dependency parsing with respect to topic distribution. |
| Outcome: | The proposed method is highly likely to capture topic similarity, but it is not clear whether it captures only genre features. |
Multilinguality Does not Make Sense: Investigating Factors Behind Zero-Shot Cross-Lingual Transfer in Sense-Aware Tasks (2025.emnlp-main)
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| Challenge: | Cross-lingual transfer allows models to perform tasks in languages unseen during training and is often assumed to benefit from increased multilinguality. |
| Approach: | They challenge this assumption by analyzing polysemy disambiguation and lexical semantic change in 28 languages and using confounding factors to account for perceived advantages. |
| Outcome: | The proposed models and benchmarks are compared across 28 languages and show that multilingual training is neither necessary nor beneficial for effective transfer. |