Papers by Ehsan Hoque
Integrating Multimodal Information in Large Pretrained Transformers (2020.acl-main)
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Wasifur Rahman, Md Kamrul Hasan, Sangwu Lee, AmirAli Bagher Zadeh, Chengfeng Mao, Louis-Philippe Morency, Ehsan Hoque
| 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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Liu Pai, Wenyang Gao, Wenjie Dong, Lin Ai, Ziwei Gong, Songfang Huang, Li Zongsheng, Ehsan Hoque, Julia Hirschberg, Yue Zhang
| 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. |