Papers with negative transfer
Efficient Meta Lifelong-Learning with Limited Memory (2020.emnlp-main)
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| Challenge: | Existing natural language learning models fail to continuously learn new tasks as they are re-trained throughout their lifetime. |
| Approach: | They propose a meta-lifelong framework that combines three common lifelong learning principles . they propose to store past examples in episodic memory and replay them at training and inference time . |
| Outcome: | The proposed framework achieves state-of-the-art performance using 1% memory size and narrows the gap with multi-task learning. |
Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection (2024.findings-eacl)
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| Challenge: | Event detection is a crucial information extraction task in many domains, such as Wikipedia or news. |
| Approach: | They propose to use subject-object relations obtained from a rule-based open information extraction system to enhance domain transfer of trigger detection (TD) they combine this enhanced transfer with masked language modeling on the target domain, observing further TD transfer gains. |
| Outcome: | The proposed model improves the transfer of triggers between domains and reduces performance drops when using a low-resource source domain to a high-res target domain. |
Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help? (2021.findings-emnlp)
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| Challenge: | Multilingual Neural Machine Translation (MNMT) trains a single model that supports translation between multiple languages . transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer. |
| Approach: | They propose a hierarchical knowledge distillation approach to train multilingual models . they use typological features and phylogeny to overcome negative transfer issue . |
| Outcome: | The proposed approach avoids negative transfer effect by capitalising on language groups generated according to typological features and phylogeny of languages. |
Soft Representation Learning for Sparse Transfer (P19-1)
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| Challenge: | Using adversarial training, we can “soft-code” shared and private spaces to avoid sparse sharing. |
| Approach: | They propose to use adversarial training to “soft-code” shared and private spaces to avoid the shared space gets too sparse. |
| Outcome: | The proposed architecture avoids sparse sharing of shared and private spaces, and also deals with low-quality input. |
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing? (2022.findings-acl)
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| Challenge: | Extensive experiments on multi-lingual datasets show that our method significantly outperforms multiple baselines and can robustly handle negative transfer. |
| Approach: | They propose to transfer semantic knowledge from rich-resourced languages to low-resource languages by using multilingual transfer learning. |
| Outcome: | The proposed model outperforms baselines and can handle negative transfer. |
How to Parse a Creole: When Martinican Creole Meets French (2022.coling-1)
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| Challenge: | a lack of annotated gold standard data is a major challenge for underresourced languages. |
| Approach: | They propose to use a French treebank to develop a dependency parser for Martinican Creole. |
| Outcome: | The proposed model is based on a French treebank and has 80 Martinican Creole sentences. |
XAutoLM: Efficient Fine-Tuning of Language Models via Meta-Learning and AutoML (2025.emnlp-main)
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Ernesto Luis Estevanell Valladares, Suilan Estevez-Velarde, Yoan Gutierrez, Andrés Montoyo, Ruslan Mitkov
| Challenge: | XAutoLM is a meta-learning-augmented framework that can be used to optimize discriminative and generative LM fine-tuning pipelines. |
| Approach: | They propose a meta-learning-augmented AutoML framework that reuses past experiences to optimize discriminative and generative LM fine-tuning pipelines efficiently. |
| Outcome: | XAutoLM surpasses zero-shot optimizer’s peak F1 on five of six tasks, reduces mean evaluation time of pipelines by up to 4.5x, and uncovers 50% more pipelines above zero- shot Pareto front. |
When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP (2023.acl-long)
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| Challenge: | Multi-task learning (MTL) is a machine learning paradigm where multiple learning tasks are optimized simultaneously, exploiting commonalities and differences across them. |
| Approach: | They propose a parameter-efficient MTL architecture to improve task aggregation and to include loosely related skills from multiple datasets. |
| Outcome: | The proposed architecture outperforms single-task learning (STL) and is expected to outperformed it. |
Zero-shot Cross-domain Dialogue State Tracking via Context-aware Auto-prompting and Instruction-following Contrastive Decoding (2024.emnlp-main)
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| Challenge: | Previous studies have implemented slot-based input improvements, such as schema-driven descriptions and question-answering formats, but still suffer from negative transfer for seen slots and inefficient transfer for unseen slots due to the significant source-target domain gap. |
| Approach: | They propose a framework that generates dynamic, context-aware slot queries to improve model transferability by penalizing deviations from the provided instructions. |
| Outcome: | Experiments on two datasets show that the proposed model performs better than existing models on the restaurant domain. |
Multi-Source Domain Adaptation with Mixture of Experts (D18-1)
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| Challenge: | Existing methods for domain adaptation from multiple sources are designed to transfer supervision from a single source domain. |
| Approach: | They propose to capture the relationship between a target example and different source domains by a point-to-set metric. |
| Outcome: | The proposed method outperforms baselines and can handle negative transfer. |
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference (2023.acl-long)
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Junhao Zheng, Qianli Ma, Shengjie Qiu, Yue Wu, Peitian Ma, Junlong Liu, Huawen Feng, Xichen Shang, Haibin Chen
| Challenge: | Existing studies attribute catastrophic forgetting to fine-tuning, and they retain pre-trained knowledge indiscriminately without identifying what knowledge is transferable. |
| Approach: | They propose a unified objective for fine-tuning to retrieve the causality back from pre-trained data and use it to mitigate negative transfer while preserving knowledge. |
| Outcome: | The proposed method outperforms state-of-the-art fine-tuning methods on commonsense QA datasets and can be implemented as a plug-in module to inflate the performance of existing QA models. |
Mitigate Negative Transfer with Similarity Heuristic Lifelong Prompt Tuning (2024.findings-acl)
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| Challenge: | Existing approaches to improve the performance of lifelong prompt tuning are limited due to inherent challenges. |
| Approach: | They propose a similarity heuristic lifelong prompt tuning framework that partitions tasks into two distinct subsets by harnessing a learnable similarity measure. |
| Outcome: | The proposed framework outperforms state-of-the-art techniques in lifelong learning benchmarks and demonstrates robustness against negative transfer in diverse task sequences. |
SLABERT Talk Pretty One Day: Modeling Second Language Acquisition with BERT (2023.acl-long)
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| Challenge: | NLP literature has not given enough attention to the phenomenon of negative transfer . positive transfer refers to the facilitating effects of one language in acquiring another and negative transfer refer to the negative effects between the learner's native [L1] and target [L2] languages. |
| Approach: | They build a Mutlilingual Age Ordered CHILDES dataset to understand the degree to which native Child-Directed Speech (CDS) can help or conflict with English language acquisition. |
| Outcome: | The proposed model enables us to understand the degree to which native Child-Directed Speech (CDS) can help or conflict with English language acquisition. |
Graceful Forgetting in Generative Language Models (2025.emnlp-main)
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| Challenge: | Recent studies show that pre-trained models do not provide all knowledge needed for fine-tuning tasks. |
| Approach: | They propose a framework to achieve graceful forgetting in generative language models by pre-training a model on large-scale correlating datasets. |
| Outcome: | The proposed framework improves the learning plasticity of the target task by selectively discarding irrelevant knowledge. |
On the Compositional Generalization in Versatile Open-domain Dialogue (2023.acl-long)
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| Challenge: | Existing approaches to multi-task learning suffer from interference among datasets or fail to effectively reuse knowledge and skills learned from other datasets. |
| Approach: | They propose a sparsely activated modular network with a well-rounded set of operators and instantiate each operator with an independent module. |
| Outcome: | The proposed model outperforms state-of-the-art supervised approaches on 4 datasets with only 10% training data thanks to the modular architecture and multi-task learning. |
Meta Distant Transfer Learning for Pre-trained Language Models (2021.emnlp-main)
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| Challenge: | Notable PLMs are available for text classification tasks, but performance of PLM on downstream tasks may be limited by the availability of training set. |
| Approach: | They propose a meta-learning framework to learn the transferable knowledge across tasks using PLMs. |
| Outcome: | The proposed framework outperforms baselines on seven datasets and is task-agnostic and unbiased. |
Joint Geometrical and Statistical Domain Adaptation for Cross-domain Code Vulnerability Detection (2023.emnlp-main)
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| 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. |
Typology Guided Multilingual Position Representations: Case on Dependency Parsing (2023.findings-acl)
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| Challenge: | Recent multilingual models benefit from strong unified semantic representation models, but conflicting linguistic regularities may break the effectiveness of word position features in multilingual learning. |
| Approach: | They propose to combine prior knowledge from typology features and existing position vectors to create a position generation network which combines prior knowledge of a language's position space and typological characterization. |
| Outcome: | The proposed model can achieve the best multilingual parsing results by combining prior knowledge from typology features and existing position vectors. |
C2KD: Cross-layer and Cross-head Knowledge Distillation for Small Language Model-based Recommendation (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) show promise but their size and high inference costs limit deployment on resource-constrained devices. |
| Approach: | They propose a framework to transfer task-relevant knowledge from two complementary dimensions to Large Language Models (LLMs) Large Language models (LLMS) have demonstrated great potential in sequential recommendation tasks . |
| Outcome: | Extensive experiments across diverse model families show that the proposed framework achieves competitive performance compared to LLMs. |
Fine-Tuning Language Models on Multiple Datasets for Citation Intention Classification (2024.findings-emnlp)
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Zeren Shui, Petros Karypis, Daniel Karls, Mingjian Wen, Saurav Manchanda, Ellad Tadmor, George Karypis
| Challenge: | Prior research has shown that pretrained language models (PLMs) can achieve state-of-the-art performance on CIC benchmarks. |
| Approach: | They propose a multi-task learning framework that fine-tunes pretrained language models on a dataset of primary interest together with multiple auxiliary CIC datasets to take advantage of additional supervision signals. |
| Outcome: | The proposed framework outperforms current state-of-the-art models on small datasets while aligning with the best-performing model on a large dataset. |
Dynamic Task Vector Grouping for Efficient Multi-Task Prompt Tuning (2025.findings-acl)
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| Challenge: | Existing approaches transfer the soft prompt to low-source targets by combining all source tasks or a single “high-similar” source task one-time-only. |
| Approach: | They propose a method to group similar source tasks based on two metrics: target similarity and knowledge consistency. |
| Outcome: | The proposed method reduces negative transfer and improves performance on low-source targets. |
LCMA-SRT: Language-Conditional Mixture-of-Experts Adapters for Joint Multilingual Speech Recognition and Translation (2026.acl-long)
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| Challenge: | Existing hierarchical transducers suffer from negative transfer and unstable target-language generation, while training separate models for each direction is computationally prohibitive. |
| Approach: | They propose a hierarchical transducer with language-conditional Mixture-of-Experts adapters to improve multilingual joint automatic speech recognition and speech translation. |
| Outcome: | Experiments on Europarl-ST (9 languages, 72 directions) show that LCMA-SRT improves both ASR and ST within a single joint model, reducing average WER and improving BLEU and COMET over strong hierarchical transducer baselines. |