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Yiru Wang

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2 papers

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AAAI Conference 2021 Conference Paper

Hierarchical Coherence Modeling for Document Quality Assessment

Dongliang Liao
Jin Xu
Gongfu Li
Yiru Wang

Text coherence plays a key role in document quality assessment. Most existing text coherence methods only focus on the similarity of adjacent sentences. However, local coherence exists in sentences with broader contexts and diverse rhetoric relations, rather than just adjacent sentence similarity. Besides, the high-level text coherence is also an important aspect of document quality. To this end, we propose a hierarchical coherence model for document quality assessment. In our model, we implement the local attention mechanism to capture the location semantics, bilinear tensor layer to measure coherence and max-coherence pooling to acquire highlevel coherence. We evaluate the proposed method on two realistic tasks: news quality judgement and automated essay scoring. Experimental results demonstrate the validity and superiority of our work.

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AAAI Conference 2020 Short Paper

Topic Enhanced Controllable CVAE for Dialogue Generation (Student Abstract)

Yiru Wang
Pengda Si
Zeyang Lei
Yujiu Yang

Neural generation models have shown great potential in conversation generation recently. However, these methods tend to generate uninformative or irrelevant responses. In this paper, we present a novel topic-enhanced controllable CVAE (TEC- CVAE) model to address this issue. On the one hand, the model learns the context-interactive topic knowledge through a novel multi-hop hybrid attention in the encoder. On the other hand, we design a topic-aware controllable decoder to constrain the expression of the stochastic latent variable in the CVAE to reduce irrelevant responses. Experimental results on two public datasets show that the two mechanisms synchronize to improve both relevance and diversity, and the proposed model outperforms other competitive methods.

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