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Bo Zou

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2 papers

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AAAI Conference 2023 Short Paper

Improving Dialogue Intent Classification with a Knowledge-Enhanced Multifactor Graph Model (Student Abstract)

Huinan Xu
Jinhui Pang
Shuangyong Song
Bo Zou

Although current Graph Neural Network (GNN) based models achieved good performances in Dialogue Intent Classification (DIC), they leaf the inherent domain-specific knowledge out of consideration, leading to the lack of ability of acquiring fine-grained semantic information. In this paper, we propose a Knowledge-Enhanced Multifactor Graph (KEMG) Model for DIC. We firstly present a knowledge-aware utterance encoder with the help of a domain-specific knowledge graph, fusing token-level and entity-level semantic information, then design a heterogeneous dialogue graph encoder by explicitly modeling several factors that matter to contextual modeling of dialogues. Experiment results show that our proposed method outperforms other GNN-based methods on a dataset collected from a real-world online customer service dialogue system on the e-commerce website, JD.

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AAAI Conference 2022 Short Paper

SimCTC: A Simple Contrast Learning Method of Text Clustering (Student Abstract)

Chen Li
Xiaoguang Yu
Shuangyong Song
Jia Wang
Bo Zou
Xiaodong He

This paper presents SimCTC, a simple contrastive learning (CL) method that greatly advances the state-of-the-art text clustering models. In SimCTC, a pre-trained BERT model first maps the input sequence to the representation space, which is then followed by three different loss function heads: Clustering head, Instance-CL head and Cluster-CL head. Experimental results on multiple benchmark datasets demonstrate that SimCTC remarkably outperforms 6 competitive text clustering methods with 1%-6% improvement on Accuracy (ACC) and 1%-4% improvement on Normalized Mutual Information (NMI). Moreover, our results also show that the clustering performance can be further improved by setting an appropriate number of clusters in the cluster-level objective.

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