Papers by Wasifur Rahman

2 papers
Integrating Multimodal Information in Large Pretrained Transformers (2020.acl-main)

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Challenge: Recent Transformer-based contextual word representations have shown state-of-the-art performance in multiple disciplines within NLP.
Approach: They propose an attachment to BERT and XLNet that allows them to accept multimodal nonverbal data during fine-tuning.
Outcome: The proposed attachment allows BERT and XLNet to accept multimodal nonverbal data during fine-tuning.
UR-FUNNY: A Multimodal Language Dataset for Understanding Humor (D19-1)

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Challenge: Humor is a unique and creative communicative behavior often displayed during social interactions.
Approach: They present a dataset that allows to model multimodal language used in expressing humor using text, visual and acoustic communication.
Outcome: The proposed framework opens the door to understanding multimodal language used in expressing humor.

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