Rong Zhang, Revanth Gangi Reddy, Md Arafat Sultan, Vittorio Castelli, Anthony Ferritto, Radu Florian, Efsun Sarioglu Kayi, Salim Roukos, Avi Sil, Todd Ward
| Challenge: | Existing approaches to transfer learning target data to in-domain text . prior work has adapted pre-trained LMs to specific domains . |
| Approach: | They extend the vocabulary of a pretrained language model with domain-specific terms to create synthetic tasks that help it transfer to downstream tasks. |
| Outcome: | The proposed approaches show significant performance gains on extractive reading comprehension, document ranking and duplicate question detection tasks. |
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Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. Smith
| Challenge: | Language models prerained on text from a wide variety of sources form the foundation of today’s NLP. |
| Approach: | They propose to tailor a pretrained model to the domain of a target task by using domain-adaptive pretraining in-domain. |
| Outcome: | The proposed model can be tailored to the domain of a target task and perform well under both high- and low-resource settings. |
Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation (2021.acl-long)
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| Challenge: | Existing methods to train pre-trained models require domain-specific data and computational resources. |
| Approach: | They propose a domain-aware N-gram Adaptor to incorporate unseen and domain-specific words into a generic pretrained model. |
| Outcome: | The proposed model can improve on eight low-resource tasks using limited data with lower computational costs. |
mDAPT: Multilingual Domain Adaptive Pretraining in a Single Model (2021.findings-emnlp)
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| Challenge: | Existing domain-specific multilingual pretraining data is difficult to obtain due to regulations, legislation, or simply a lack of language- and domain- specific text. |
| Approach: | They propose to continue pretraining a language model on domain-specific unlabelled text . this allows for better modelling of text for downstream tasks within the domain . |
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On the Importance of Effectively Adapting Pretrained Language Models for Active Learning (2022.acl-short)
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| Challenge: | Recent active learning approaches in NLP use off-the-shelf pretrained language models (LMs) . a poor training strategy can be catastrophic for AL, authors argue . |
| Approach: | They propose to first adapt the pretrained LM to the target task and then use it for AL. |
| Outcome: | The proposed approach provides substantial data efficiency improvements compared to the standard fine-tuning approach. |
Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains (2021.findings-acl)
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| Challenge: | Large pre-trained models suffer from domain shift and are not optimal for specific domains. |
| Approach: | They propose a general approach to developing small, fast and effective pretrained models for specific domains by adapting off-the-shelf general pretrained model and performing task-agnostic knowledge distillation in target domains. |
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Robust Transfer Learning with Pretrained Language Models through Adapters (2021.acl-short)
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| Challenge: | Existing approaches to transfer learning with pretrained transformer-based language models are not robust and can be adversarial. |
| Approach: | They propose a simple yet effective adapter-based approach to fine-tune language models on downstream tasks. |
| Outcome: | The proposed approach improves stability and adversarial robustness in transfer learning to various downstream tasks. |
Entity Extraction in Low Resource Domains with Selective Pre-training of Large Language Models (2022.emnlp-main)
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| Challenge: | Existing methods to perform named entity recognition (NER) on unlabeled data are difficult to obtain in low-resource domains. |
| Approach: | They propose ways to use unlabeled data for pretraining to improve performance in downstream tasks. |
| Outcome: | The proposed methods outperform models trained on unlabeled data on seven domains. |
Efficient Hierarchical Domain Adaptation for Pretrained Language Models (2022.naacl-main)
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| 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. |
Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation (2022.acl-long)
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| Challenge: | Recent studies have found that the performance of multilingual pretrained models is highly dependent on the availability of monolingual or parallel text in a target language. |
| Approach: | They propose to use bilingual lexicons to synthesize textual or labeled data and combine it with monolingual or parallel text when available. |
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Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks (2021.emnlp-main)
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| 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. |