Qing Zong


2024

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KNOWCOMP POKEMON Team at DialAM-2024: A Two-Stage Pipeline for Detecting Relations in Dialogue Argument Mining
Zihao Zheng | Zhaowei Wang | Qing Zong | Yangqiu Song
Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)

Dialogue Argument Mining(DialAM) is an important branch of Argument Mining(AM). DialAM-2024 is a shared task focusing on dialogue argument mining, which requires us to identify argumentative relations and illocutionary relations among proposition nodes and locution nodes. To accomplish this, we propose a two-stage pipeline, which includes the Two-Step S-Node Prediction Model in Stage 1 and the YA-Node Prediction Model in Stage 2. We also augment the training data in both stages and introduce context in the prediction of Stage 2. We successfully completed the task and achieved good results. Our team KNOWCOMP POKEMON ranked 1st in the ARI Focused score and 4th in the Global Focused score.

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AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation
Zhaowei Wang | Wei Fan | Qing Zong | Hongming Zhang | Sehyun Choi | Tianqing Fang | Xin Liu | Yangqiu Song | Ginny Wong | Simon See
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study. Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored. In this work, we design the framework AbsInstruct to enhance LLMs’ abstraction ability through instruction tuning. The framework builds instructions with in-depth explanations to assist LLMs in capturing the underlying rationale of abstraction. Meanwhile, we introduce a plausibility estimator to select instructions that are more consistent with the abstraction knowledge of LLMs to be aligned. Then, our framework combines abstraction instructions with general-purpose ones to build a hybrid dataset. Extensive experiments and analyses demonstrate that our framework can considerably enhance LLMs’ abstraction ability with strong generalization performance while maintaining their general instruction-following abilities.

2023

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TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Mining
Qing Zong | Zhaowei Wang | Baixuan Xu | Tianshi Zheng | Haochen Shi | Weiqi Wang | Yangqiu Song | Ginny Wong | Simon See
Proceedings of the 10th Workshop on Argument Mining

A main goal of Argument Mining (AM) is to analyze an author’s stance. Unlike previous AM datasets focusing only on text, the shared task at the 10th Workshop on Argument Mining introduces a dataset including both texts and images. Importantly, these images contain both visual elements and optical characters. Our new framework, TILFA (A Unified Framework for Text, Image, and Layout Fusion in Argument Mining), is designed to handle this mixed data. It excels at not only understanding text but also detecting optical characters and recognizing layout details in images. Our model significantly outperforms existing baselines, earning our team, KnowComp, the 1st place in the leaderboard of Argumentative Stance Classification subtask in this shared task.

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KnowComp Submission for WMT23 Sign Language Translation Task
Baixuan Xu | Haochen Shi | Tianshi Zheng | Qing Zong | Weiqi Wang | Zhaowei Wang | Yangqiu Song
Proceedings of the Eighth Conference on Machine Translation

Sign Language Translation (SLT) is a complex task that involves accurately interpreting sign language gestures and translating them into spoken or written language and vice versa. Its primary objective is to facilitate communication between individuals with hearing difficulties using deep learning systems. Existing approaches leverage gloss annotations of sign language gestures to assist the model in capturing the movement and differentiating various gestures. However, constructing a large-scale gloss-annotated dataset is both expensive and impractical to cover multiple languages, and pre-trained generative models cannot be efficiently used due to the lack of textual source context in SLT. To address these challenges, we propose a gloss-free framework for the WMT23 SLT task. Our system primarily consists of a visual extractor for extracting video embeddings and a generator responsible for producing the translated text. We also employ an embedding alignment block that is trained to align the embedding space of the visual extractor with that of the generator. Despite undergoing extensive training and validation, our system consistently falls short of meeting the baseline performance. Further analysis shows that our model’s poor projection rate prevents it from learning diverse visual embeddings. Our codes and model checkpoints are available at https://github.com/HKUST-KnowComp/SLT.

2022

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Glyph Features Matter: A Multimodal Solution for EvaHan in LT4HALA2022
Wei Xinyuan | Liu Weihao | Qing Zong | Zhang Shaoqing | Baotian Hu
Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages

We participate in the LT4HALA2022 shared task EvaHan. This task has two subtasks. Subtask 1 is word segmentation, and subtask 2 is part-of-speech tagging. Each subtask consists of two tracks, a close track that can only use the data and models provided by the organizer, and an open track without restrictions. We employ three pre-trained models, two of which are open-source pre-trained models for ancient Chinese (Siku-Roberta and roberta-classical-chinese), and one is our pre-trained GlyphBERT combined with glyph features. Our methods include data augmentation, data pre-processing, model pretraining, downstream fine-tuning, k-fold cross validation and model ensemble. We achieve competitive P, R, and F1 scores on both our own validation set and the final public test set.