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Haiqing Chen

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4 papers

1 author row

AAAI Conference 2020 Conference Paper

MTSS: Learn from Multiple Domain Teachers and Become a Multi-Domain Dialogue Expert

Shuke Peng
Feng Ji
Zehao Lin
Shaobo Cui
Haiqing Chen
Yin Zhang

How to build a high-quality multi-domain dialogue system is a challenging work due to its complicated and entangled dialogue state space among each domain, which seriously limits the quality of dialogue policy, and further affects the generated response. In this paper, we propose a novel method to acquire a satisfying policy and subtly circumvent the knotty dialogue state representation problem in the multi-domain setting. Inspired by real school teaching scenarios, our method is composed of multiple domain-speciﬁc teachers and a universal student. Each individual teacher only focuses on one speciﬁc domain and learns its corresponding domain knowledge and dialogue policy based on a precisely extracted single domain dialogue state representation. Then, these domainspeciﬁc teachers impart their domain knowledge and policies to a universal student model and collectively make this student model a multi-domain dialogue expert. Experiment results show that our method reaches competitive results with SOTAs in both multi-domain and single domain setting.

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AAAI Conference 2020 Short Paper

Session-Level User Satisfaction Prediction for Customer Service Chatbot in E-Commerce (Student Abstract)

Riheng Yao
Shuangyong Song
Qiudan Li
Chao Wang
Huan Chen
Haiqing Chen
Daniel Dajun Zeng

This paper aims to predict user satisfaction for customer service chatbot in session level, which is of great practical significance yet rather untouched. It requires to explore the relationship between questions and answers across different rounds of interactions, and handle user bias. We propose an approach to model multi-round conversations within one session and take user information into account. Experimental results on a dataset from a real-world industrial customer service chatbot Alime demonstrate the good performance of our proposed model.

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AAAI Conference 2019 Conference Paper

A Deep Cascade Model for Multi-Document Reading Comprehension

Ming Yan
Jiangnan Xia
Chen Wu
Bin Bi
Zhongzhou Zhao
Ji Zhang
Luo Si
Rui Wang

A fundamental trade-off between effectiveness and efficiency needs to be balanced when designing an online question answering system. Effectiveness comes from sophisticated functions such as extractive machine reading comprehension (MRC), while efficiency is obtained from improvements in preliminary retrieval components such as candidate document selection and paragraph ranking. Given the complexity of the real-world multi-document MRC scenario, it is difficult to jointly optimize both in an end-to-end system. To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the documentlevel and paragraph-level ranking of candidate texts to more precise answer extraction with machine reading comprehension. Specifically, irrelevant documents and paragraphs are first filtered out with simple functions for efficiency consideration. Then we jointly train three modules on the remaining texts for better tracking the answer: the document extraction, the paragraph extraction and the answer extraction. Experiment results show that the proposed method outperforms the previous state-of-the-art methods on two large-scale multidocument benchmark datasets, i. e. , TriviaQA and DuReader. In addition, our online system can stably serve typical scenarios with millions of daily requests in less than 50ms.

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IJCAI Conference 2018 Conference Paper

A Weakly Supervised Method for Topic Segmentation and Labeling in Goal-oriented Dialogues via Reinforcement Learning

Ryuichi Takanobu
Minlie Huang
Zhongzhou Zhao
Fenglin Li
Haiqing Chen
Xiaoyan Zhu
Liqiang Nie

Topic structure analysis plays a pivotal role in dialogue understanding. We propose a reinforcement learning (RL) method for topic segmentation and labeling in goal-oriented dialogues, which aims to detect topic boundaries among dialogue utterances and assign topic labels to the utterances. We address three common issues in the goal-oriented customer service dialogues: informality, local topic continuity, and global topic structure. We explore the task in a weakly supervised setting and formulate it as a sequential decision problem. The proposed method consists of a state representation network to address the informality issue, and a policy network with rewards to model local topic continuity and global topic structure. To train the two networks and offer a warm-start to the policy, we firstly use some keywords to annotate the data automatically. We then pre-train the networks on noisy data. Henceforth, the method continues to refine the data labels using the current policy to learn better state representations on the refined data for obtaining a better policy. Results demonstrate that this weakly supervised method obtains substantial improvements over state-of-the-art baselines.

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