Raymond Chi-Wing Wong


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Automatic Data Visualization Generation from Chinese Natural Language Questions
Yan Ge | Victor Junqiu Wei | Yuanfeng Song | Jason Chen Zhang | Raymond Chi-Wing Wong
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Data visualization has emerged as an effective tool for getting insights from massive datasets. Due to the hardness of manipulating the programming languages of data visualization, automatic data visualization generation from natural languages (Text-to-Vis) is becoming increasingly popular. Despite the plethora of research effort on the English Text-to-Vis, studies have yet to be conducted on data visualization generation from questions in Chinese. Motivated by this, we propose a Chinese Text-to-Vis dataset in the paper and demonstrate our first attempt to tackle this problem. Our model integrates multilingual BERT as the encoder, boosts the cross-lingual ability, and infuses the n-gram information into our word representation learning. Our experimental results show that our dataset is challenging and deserves further research.


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Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech
Yuanfeng Song | Di Jiang | Weiwei Zhao | Qian Xu | Raymond Chi-Wing Wong | Qiang Yang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

Language model is a vital component in modern automatic speech recognition (ASR) systems. Since “one-size-fits-all” language model works suboptimally for conversational speeches, language model adaptation (LMA) is considered as a promising solution for solving this problem. In order to compare the state-of-the-art LMA techniques and systematically demonstrate their effect in conversational speech recognition, we develop a novel toolkit named Chameleon, which includes the state-of-the-art cache-based and topic-based LMA techniques. This demonstration does not only vividly visualize underlying working mechanisms of a variety of the state-of-the-art LMA models but also provide an interface for the user to customize the hyperparameters of them. With this demonstration, the audience can experience the effect of LMA in an interactive and real-time fashion. We wish this demonstration would inspire more research on better language model techniques for ASR.