46,319 research outputs found
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
Transfer learning has fundamentally changed the landscape of natural language
processing (NLP) research. Many existing state-of-the-art models are first
pre-trained on a large text corpus and then fine-tuned on downstream tasks.
However, due to limited data resources from downstream tasks and the extremely
large capacity of pre-trained models, aggressive fine-tuning often causes the
adapted model to overfit the data of downstream tasks and forget the knowledge
of the pre-trained model. To address the above issue in a more principled
manner, we propose a new computational framework for robust and efficient
fine-tuning for pre-trained language models. Specifically, our proposed
framework contains two important ingredients: 1. Smoothness-inducing
regularization, which effectively manages the capacity of the model; 2. Bregman
proximal point optimization, which is a class of trust-region methods and can
prevent knowledge forgetting. Our experiments demonstrate that our proposed
method achieves the state-of-the-art performance on multiple NLP benchmarks.Comment: The 58th annual meeting of the Association for Computational
Linguistics (ACL 2020
Introduction (to Special Issue on Tibetan Natural Language Processing)
This introduction surveys research on Tibetan NLP, both in China and in the West, as well as contextualizing the articles contained in the special issue
Neuro-linguistic-programming: a critical review of NLP research and the application of NLP in coaching
The huge popularity of Neuro-Linguistic Programming (NLP) over the past three decades has in some ways mirrored the growth in coaching psychology. This paper is part of a series of four papers in a special issue within ICPR that aims to explore NLP coaching from diverse perspectives, offering personal insights or reviews of evidence. As part of this process a pair of authors were invited to advance the case for and the case against NLP. This paper aims to adopt a critical stance; reviewing the concept of NLP, exploring the claims made by advocates and critically reviewing the evidence from a psychological perspective. In undertaking this review we completed a series of literature searches using a range of discovery tools to identify research papers, based on pre-determined search criteria. This review led us to the conclusion that unique NLP practices are poorly supported by research evidence
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