TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models

• URL: http://arxiv.org/abs/2303.15430v2
• Date: Wed, 29 Mar 2023 04:49:46 GMT
• Title: TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models
• Authors: Md Kamrul Hasan, Md Saiful Islam, Sangwu Lee, Wasifur Rahman, Iftekhar Naim, Mohammed Ibrahim Khan, Ehsan Hoque
• Abstract summary: We propose TextMI as a general, competitive baseline for multimodal behavioral analysis tasks. Our approach significantly reduces model complexity, adds interpretability to the model's decision, and can be applied for a diverse set of tasks.
• Score: 5.668457303716451
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pre-trained large language models have recently achieved ground-breaking performance in a wide variety of language understanding tasks. However, the same model can not be applied to multimodal behavior understanding tasks (e.g., video sentiment/humor detection) unless non-verbal features (e.g., acoustic and visual) can be integrated with language. Jointly modeling multiple modalities significantly increases the model complexity, and makes the training process data-hungry. While an enormous amount of text data is available via the web, collecting large-scale multimodal behavioral video datasets is extremely expensive, both in terms of time and money. In this paper, we investigate whether large language models alone can successfully incorporate non-verbal information when they are presented in textual form. We present a way to convert the acoustic and visual information into corresponding textual descriptions and concatenate them with the spoken text. We feed this augmented input to a pre-trained BERT model and fine-tune it on three downstream multimodal tasks: sentiment, humor, and sarcasm detection. Our approach, TextMI, significantly reduces model complexity, adds interpretability to the model's decision, and can be applied for a diverse set of tasks while achieving superior (multimodal sarcasm detection) or near SOTA (multimodal sentiment analysis and multimodal humor detection) performance. We propose TextMI as a general, competitive baseline for multimodal behavioral analysis tasks, particularly in a low-resource setting.

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