Dingmin Wang


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Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition
Dingmin Wang | Chenghua Lin | Qi Liu | Kam-Fai Wong
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design four modules (for clas- sification and sequence labelling) to jointly extract dialogue states. Experimental results based on the MultiWoz 2.0 dataset validates the superiority of our proposed model in terms of both complexity and scalability when compared to the state-of-the-art methods, especially in the scenario of multi-domain dialogues entangled with many turns of utterances.

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A Template-guided Hybrid Pointer Network for Knowledge-based Task-oriented Dialogue Systems
Dingmin Wang | Ziyao Chen | Wanwei He | Li Zhong | Yunzhe Tao | Min Yang
Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)

Most existing neural network based task-oriented dialog systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability. Inspired by the traditional template-based generation approaches, we propose a template-guided hybrid pointer network for knowledge-based task-oriented dialog systems, which retrieves several potentially relevant answers from a pre-constructed domain-specific conversational repository as guidance answers, and incorporates the guidance answers into both the encoding and decoding processes. Specifically, we design a memory pointer network model with a gating mechanism to fully exploit the semantic correlation between the retrieved answers and the ground-truth response. We evaluate our model on four widely used task-oriented datasets, including one simulated and three manually created datasets. The experimental results demonstrate that the proposed model achieves significantly better performance than the state-of-the-art methods over different automatic evaluation metrics.


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Confusionset-guided Pointer Networks for Chinese Spelling Check
Dingmin Wang | Yi Tay | Li Zhong
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

This paper proposes Confusionset-guided Pointer Networks for Chinese Spell Check (CSC) task. More concretely, our approach utilizes the off-the-shelf confusionset for guiding the character generation. To this end, our novel Seq2Seq model jointly learns to copy a correct character from an input sentence through a pointer network, or generate a character from the confusionset rather than the entire vocabulary. We conduct experiments on three human-annotated datasets, and results demonstrate that our proposed generative model outperforms all competitor models by a large margin of up to 20% F1 score, achieving state-of-the-art performance on three datasets.

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Bridging the Gap: Improve Part-of-speech Tagging for Chinese Social Media Texts with Foreign Words
Dingmin Wang | Meng Fang | Yan Song | Juntao Li
Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)


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A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check
Dingmin Wang | Yan Song | Jing Li | Jialong Han | Haisong Zhang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Chinese spelling check (CSC) is a challenging yet meaningful task, which not only serves as a preprocessing in many natural language processing(NLP) applications, but also facilitates reading and understanding of running texts in peoples’ daily lives. However, to utilize data-driven approaches for CSC, there is one major limitation that annotated corpora are not enough in applying algorithms and building models. In this paper, we propose a novel approach of constructing CSC corpus with automatically generated spelling errors, which are either visually or phonologically resembled characters, corresponding to the OCR- and ASR-based methods, respectively. Upon the constructed corpus, different models are trained and evaluated for CSC with respect to three standard test sets. Experimental results demonstrate the effectiveness of the corpus, therefore confirm the validity of our approach.


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NLPTEA 2017 Shared Task – Chinese Spelling Check
Gabriel Fung | Maxime Debosschere | Dingmin Wang | Bo Li | Jia Zhu | Kam-Fai Wong
Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017)

This paper provides an overview along with our findings of the Chinese Spelling Check shared task at NLPTEA 2017. The goal of this task is to develop a computer-assisted system to automatically diagnose typing errors in traditional Chinese sentences written by students. We defined six types of errors which belong to two categories. Given a sentence, the system should detect where the errors are, and for each detected error determine its type and provide correction suggestions. We designed, constructed, and released a benchmark dataset for this task.