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Shaolei Wang

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2 papers

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AAAI Conference 2020 Conference Paper

Multi-Task Self-Supervised Learning for Disfluency Detection

Shaolei Wang
Wangxiang Che
Qi Liu
Pengda Qin
Ting Liu
William Yang Wang

Most existing approaches to disﬂuency detection heavily rely on human-annotated data, which is expensive to obtain in practice. To tackle the training data bottleneck, we investigate methods for combining multiple self-supervised tasksi. e. , supervised tasks where data can be collected without manual labeling. First, we construct large-scale pseudo training data by randomly adding or deleting words from unlabeled news data, and propose two self-supervised pre-training tasks: (i) tagging task to detect the added noisy words. (ii) sentence classiﬁcation to distinguish original sentences from grammatically-incorrect sentences. We then combine these two tasks to jointly train a network. The pre-trained network is then ﬁne-tuned using human-annotated disﬂuency detection training data. Experimental results on the commonly used English Switchboard test set show that our approach can achieve competitive performance compared to the previous systems (trained using the full dataset) by using less than 1% (1000 sentences) of the training data. Our method trained on the full dataset signiﬁcantly outperforms previous methods, reducing the error by 21% on English Switchboard.

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IJCAI Conference 2018 Conference Paper

Joint Extraction of Entities and Relations Based on a Novel Graph Scheme

Shaolei Wang
Yue Zhang
Wanxiang Che
Ting Liu

Both entity and relation extraction can benefit from being performed jointly, allowing each task to correct the errors of the other. Most existing neural joint methods extract entities and relations separately and achieve joint learning through parameter sharing, leading to a drawback that information between output entities and relations cannot be fully exploited. In this paper, we convert the joint task into a directed graph by designing a novel graph scheme and propose a transition-based approach to generate the directed graph incrementally, which can achieve joint learning through joint decoding. Our method can model underlying dependencies not only between entities and relations, but also between relations. Experiments on NewYork Times (NYT) corpora show that our approach outperforms the state-of-the-art methods.

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