Papers by Ehsan Hoque

3 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.
Hitting your MARQ: Multimodal ARgument Quality Assessment in Long Debate Video (2021.emnlp-main)

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Challenge: Current literature mostly considers textual content while assessing argument quality, and it is limited to datasets containing short text sequences (18-48 words).
Approach: They propose a set of interpretable debate centric features that are inspired by theories of argument quality and propose MARQ model that summarizes the multimodal signals on long debate videos.
Outcome: The proposed model outperforms baseline models with an error rate reduction of 22.7% on the argument quality prediction task and achieves 81.91% accuracy.
A Survey on Open Information Extraction from Rule-based Model to Large Language Model (2024.findings-emnlp)

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Challenge: Open Information Extraction (OpenIE) is a key NLP task aimed at extracting structured information from unstructured text sources.
Approach: They propose to categorize OpenIE into rule-based, neural, and pre-trained large language models and discuss each within a chronological framework.
Outcome: The paper categorizes OpenIE approaches into rule-based, neural, and pre-trained large language models, discussing each within a chronological framework.

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