Dakuo Wang


2019

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Out-of-Domain Detection for Low-Resource Text Classification Tasks
Ming Tan | Yang Yu | Haoyu Wang | Dakuo Wang | Saloni Potdar | Shiyu Chang | Mo Yu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Out-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task. The goal is to detect the OOD cases with limited in-domain (ID) training data, since in machine learning applications we observe that training data is often insufficient. In this work, we propose an OOD-resistant Prototypical Network to tackle this zero-shot OOD detection and few-shot ID classification task. Evaluations on real-world datasets show that the proposed solution outperforms state-of-the-art methods in zero-shot OOD detection task, while maintaining a competitive performance on ID classification task.

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Context-Aware Conversation Thread Detection in Multi-Party Chat
Ming Tan | Dakuo Wang | Yupeng Gao | Haoyu Wang | Saloni Potdar | Xiaoxiao Guo | Shiyu Chang | Mo Yu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In multi-party chat, it is common for multiple conversations to occur concurrently, leading to intermingled conversation threads in chat logs. In this work, we propose a novel Context-Aware Thread Detection (CATD) model that automatically disentangles these conversation threads. We evaluate our model on four real-world datasets and demonstrate an overall im-provement in thread detection accuracy over state-of-the-art benchmarks.

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Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
Haoyu Wang | Ming Tan | Mo Yu | Shiyu Chang | Dakuo Wang | Kun Xu | Xiaoxiao Guo | Saloni Potdar
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Many approaches to extract multiple relations from a paragraph require multiple passes over the paragraph. In practice, multiple passes are computationally expensive and this makes difficult to scale to longer paragraphs and larger text corpora. In this work, we focus on the task of multiple relation extractions by encoding the paragraph only once. We build our solution upon the pre-trained self-attentive models (Transformer), where we first add a structured prediction layer to handle extraction between multiple entity pairs, then enhance the paragraph embedding to capture multiple relational information associated with each entity with entity-aware attention. We show that our approach is not only scalable but can also perform state-of-the-art on the standard benchmark ACE 2005.