Recent Transformer-based contextual word representations, including BERT and XLNet, have shown state-of-the-art performance in multiple disciplines within NLP. Fine-tuning the trained contextual models on task-specific datasets has been the key to achieving superior performance downstream. While fine-tuning these pre-trained models is straightforward for lexical applications (applications with only language modality), it is not trivial for multimodal language (a growing area in NLP focused on modeling face-to-face communication). More specifically, this is due to the fact that pre-trained models don’t have the necessary components to accept two extra modalities of vision and acoustic. In this paper, we proposed an attachment to BERT and XLNet called Multimodal Adaptation Gate (MAG). MAG allows BERT and XLNet to accept multimodal nonverbal data during fine-tuning. It does so by generating a shift to internal representation of BERT and XLNet; a shift that is conditioned on the visual and acoustic modalities. In our experiments, we study the commonly used CMU-MOSI and CMU-MOSEI datasets for multimodal sentiment analysis. Fine-tuning MAG-BERT and MAG-XLNet significantly boosts the sentiment analysis performance over previous baselines as well as language-only fine-tuning of BERT and XLNet. On the CMU-MOSI dataset, MAG-XLNet achieves human-level multimodal sentiment analysis performance for the first time in themore »
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). Many 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 high complexity of
pre-trained models, aggressive fine-tuning of-
ten causes the fine-tuned model to overfit the
training data of downstream tasks and fail to
generalize to unseen data. To address such an
issue in a principled manner, we propose a new
learning framework for robust and efficient
fine-tuning for pre-trained models to attain
better generalization performance. The pro-
posed framework contains two important in-
gredients: 1. Smoothness-inducing regulariza-
tion, which effectively manages the complex-
ity of the model; 2. Bregman proximal point
optimization, which is an instance of trust-
region methods and can prevent aggressive up-
dating. Our experiments show that the pro-
posed framework achieves new state-of-the-art
performance on a number of NLP tasks includ-
ing GLUE, SNLI, SciTail and ANLI. More-
over, it also outperforms the state-of-the-art T5
model, which is the largest pre-trained model
containing 11 billion parameters, on GLUE.
- Award ID(s):
- 1717916
- Publication Date:
- NSF-PAR ID:
- 10162625
- Journal Name:
- Annual Meeting of the Association for Computational Linguistics
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.18653/v1/2020.acl-main.197
