Papers by Siqu Long
CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection (2021.findings-acl)
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Henry Weld, Guanghao Huang, Jean Lee, Tongshu Zhang, Kunze Wang, Xinghong Guo, Siqu Long, Josiah Poon, Caren Han
| Challenge: | Existing toxic language detection models focus on the single utterance level without deeper understanding of context. |
| Approach: | They propose a dataset for in-game toxic language detection enabling joint intent classification and slot filling analysis, which is the core task of Natural Language Understanding (NLU). |
| Outcome: | The proposed framework handles utterance and token-level patterns, and rich contextual chatting history. |
Understanding Attention for Vision-and-Language Tasks (2022.coling-1)
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| Challenge: | Attention mechanism has been used in Vision-and-Language (VL) tasks to bridge the semantic gap between visual and textual clues. |
| Approach: | They conduct a comprehensive analysis on understanding the role of attention alignment by looking into attention score calculation methods and checking how it represents the visual region’s and textual token’s significance for the global assessment. |
| Outcome: | The attention score calculation methods represent visual region’s and textual token’s significance for the global assessment. |
Detect All Abuse! Toward Universal Abusive Language Detection Models (2020.coling-main)
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| Challenge: | Existing work on online abusive language detection focused on detecting a single abusive language problem in a domain, like Twitter, but none of them was successfully transferable to general ALD in different online communities. |
| Approach: | They propose a generic ALD framework that can address multiple types of ALD tasks across different domains and use a textual graph embedding to analyse the user’s linguistic behaviour. |
| Outcome: | The proposed framework surpasses the current state-of-the-art ALD algorithms across seven datasets covering multiple aspects of abusive language and different online community domains. |
Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis (2022.coling-1)
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| Challenge: | Document Layout Analysis tasks rely on visual cues to understand documents . traditional deep learning-based methods fail to recognize the layout and components of unstructured documents based on the document structure and the boundaries of each layout region. |
| Approach: | They propose a way to harmonize and integrate heterogeneous aspects for Document Layout Analysis by using graph convolutional networks to enhance each aspect of features. |
| Outcome: | The proposed task is based on three widely used datasets: PubLayNet, FUNSD, and DocBank. |
VICTR: Visual Information Captured Text Representation for Text-to-Vision Multimodal Tasks (2020.coling-main)
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| Challenge: | Existing text-to-image generation models focus on generating high resolution images and neglect understanding text descriptions. |
| Approach: | They propose a visual contextual text representation which captures rich visual semantic information of objects from text input. |
| Outcome: | The proposed visual contextual text representation improves on the state-of-the-art models. |