Add to ORKGPretrained language models have shown success in various areas of natural language processing, including reading comprehension tasks. However, when applying machine learning methods to new domains, labeled data may not always be available. To address this, we use supervised pretraining on source-domain data to reduce sample complexity on domain-specific downstream tasks. We evaluate zero-shot performance on domain-specific reading comprehension tasks by combining task transfer with domain adaptation to fine-tune a pretrained model with no labelled data from the target task. Our approach outperforms Domain-Adaptive Pretraining on downstream domain-specific reading comprehension tasks in 3 out of 4 domains.Comment: NAACL 2022 Deep Learning fo...
Pretrained language models have become the standard approach for many NLP tasks due to strong perfor...
Large Language Models (LLMs) have demonstrated impressive zero shot performance on a wide range of N...
Curriculum learning strategies in prior multi-task learning approaches arrange datasets in a difficu...
We propose a multitask pretraining approach ZeroPrompt for zero-shot generalization, focusing on tas...
Prompt-based learning, with its capability to tackle zero-shot and few-shot NLP tasks, has gained mu...
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on...
Supervised deep learning-based approaches have been applied to task-oriented dialog and have proven ...
Large-scale deep learning models have reached previously unattainable performance for various tasks....
Recently, there has been an increasing interest in models that generate natural language explanation...
The performance of a machine learning model trained on labeled data of a (source) domain degrades se...
Recent advances in NLP are brought by a range of large-scale pretrained language models (PLMs). Thes...
2018-11-26Developing intelligent systems for vision and language understanding has long been a cruci...
Pretrained language models (PLMs) have demonstrated remarkable performance in various natural langua...
International audienceLarge language models have recently been shown to attain reasonable zero-shot ...
We introduce a method for improving the structural understanding abilities of language models. Unlik...
Pretrained language models have become the standard approach for many NLP tasks due to strong perfor...
Large Language Models (LLMs) have demonstrated impressive zero shot performance on a wide range of N...
Curriculum learning strategies in prior multi-task learning approaches arrange datasets in a difficu...
We propose a multitask pretraining approach ZeroPrompt for zero-shot generalization, focusing on tas...
Prompt-based learning, with its capability to tackle zero-shot and few-shot NLP tasks, has gained mu...
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on...
Supervised deep learning-based approaches have been applied to task-oriented dialog and have proven ...
Large-scale deep learning models have reached previously unattainable performance for various tasks....
Recently, there has been an increasing interest in models that generate natural language explanation...
The performance of a machine learning model trained on labeled data of a (source) domain degrades se...
Recent advances in NLP are brought by a range of large-scale pretrained language models (PLMs). Thes...
2018-11-26Developing intelligent systems for vision and language understanding has long been a cruci...
Pretrained language models (PLMs) have demonstrated remarkable performance in various natural langua...
International audienceLarge language models have recently been shown to attain reasonable zero-shot ...
We introduce a method for improving the structural understanding abilities of language models. Unlik...
Pretrained language models have become the standard approach for many NLP tasks due to strong perfor...
Large Language Models (LLMs) have demonstrated impressive zero shot performance on a wide range of N...
Curriculum learning strategies in prior multi-task learning approaches arrange datasets in a difficu...