Lu Chen


2020

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Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks
Yanbin Zhao | Lu Chen | Zhi Chen | Ruisheng Cao | Su Zhu | Kai Yu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Efficient structure encoding for graphs with labeled edges is an important yet challenging point in many graph-based models. This work focuses on AMR-to-text generation – A graph-to-sequence task aiming to recover natural language from Abstract Meaning Representations (AMR). Existing graph-to-sequence approaches generally utilize graph neural networks as their encoders, which have two limitations: 1) The message propagation process in AMR graphs is only guided by the first-order adjacency information. 2) The relationships between labeled edges are not fully considered. In this work, we propose a novel graph encoding framework which can effectively explore the edge relations. We also adopt graph attention networks with higher-order neighborhood information to encode the rich structure in AMR graphs. Experiment results show that our approach obtains new state-of-the-art performance on English AMR benchmark datasets. The ablation analyses also demonstrate that both edge relations and higher-order information are beneficial to graph-to-sequence modeling.

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Neural Graph Matching Networks for Chinese Short Text Matching
Lu Chen | Yanbin Zhao | Boer Lyu | Lesheng Jin | Zhi Chen | Su Zhu | Kai Yu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Chinese short text matching usually employs word sequences rather than character sequences to get better performance. However, Chinese word segmentation can be erroneous, ambiguous or inconsistent, which consequently hurts the final matching performance. To address this problem, we propose neural graph matching networks, a novel sentence matching framework capable of dealing with multi-granular input information. Instead of a character sequence or a single word sequence, paired word lattices formed from multiple word segmentation hypotheses are used as input and the model learns a graph representation according to an attentive graph matching mechanism. Experiments on two Chinese datasets show that our models outperform the state-of-the-art short text matching models.

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Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing
Ruisheng Cao | Su Zhu | Chenyu Yang | Chen Liu | Rao Ma | Yanbin Zhao | Lu Chen | Kai Yu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

One daunting problem for semantic parsing is the scarcity of annotation. Aiming to reduce nontrivial human labor, we propose a two-stage semantic parsing framework, where the first stage utilizes an unsupervised paraphrase model to convert an unlabeled natural language utterance into the canonical utterance. The downstream naive semantic parser accepts the intermediate output and returns the target logical form. Furthermore, the entire training process is split into two phases: pre-training and cycle learning. Three tailored self-supervised tasks are introduced throughout training to activate the unsupervised paraphrase model. Experimental results on benchmarks Overnight and GeoGranno demonstrate that our framework is effective and compatible with supervised training.

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Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking
Su Zhu | Jieyu Li | Lu Chen | Kai Yu
Findings of the Association for Computational Linguistics: EMNLP 2020

Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation. For multi-domain DST, the data sparsity problem is a major obstacle due to increased numbers of state candidates and dialogue lengths. To encode the dialogue context efficiently, we utilize the previous dialogue state (predicted) and the current dialogue utterance as the input for DST. To consider relations among different domain-slots, the schema graph involving prior knowledge is exploited. In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms. Experiment results show that our approach can outperform strong baselines, and the previous state-of-the-art method (SOM-DST) can also be improved by our proposed schema graph.

2019

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DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction
Shun Zheng | Xu Han | Yankai Lin | Peilin Yu | Lu Chen | Ling Huang | Zhiyuan Liu | Wei Xu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Pattern-based labeling methods have achieved promising results in alleviating the inevitable labeling noises of distantly supervised neural relation extraction. However, these methods require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize quickly. To ease the labor-intensive workload of pattern writing and enable the quick generalization to new relation types, we propose a neural pattern diagnosis framework, DIAG-NRE, that can automatically summarize and refine high-quality relational patterns from noise data with human experts in the loop. To demonstrate the effectiveness of DIAG-NRE, we apply it to two real-world datasets and present both significant and interpretable improvements over state-of-the-art methods.

2018

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Towards Universal Dialogue State Tracking
Liliang Ren | Kaige Xie | Lu Chen | Kai Yu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Dialogue state tracker is the core part of a spoken dialogue system. It estimates the beliefs of possible user’s goals at every dialogue turn. However, for most current approaches, it’s difficult to scale to large dialogue domains. They have one or more of following limitations: (a) Some models don’t work in the situation where slot values in ontology changes dynamically; (b) The number of model parameters is proportional to the number of slots; (c) Some models extract features based on hand-crafted lexicons. To tackle these challenges, we propose StateNet, a universal dialogue state tracker. It is independent of the number of values, shares parameters across all slots, and uses pre-trained word vectors instead of explicit semantic dictionaries. Our experiments on two datasets show that our approach not only overcomes the limitations, but also significantly outperforms the performance of state-of-the-art approaches.

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Structured Dialogue Policy with Graph Neural Networks
Lu Chen | Bowen Tan | Sishan Long | Kai Yu
Proceedings of the 27th International Conference on Computational Linguistics

Recently, deep reinforcement learning (DRL) has been used for dialogue policy optimization. However, many DRL-based policies are not sample-efficient. Most recent advances focus on improving DRL optimization algorithms to address this issue. Here, we take an alternative route of designing neural network structure that is better suited for DRL-based dialogue management. The proposed structured deep reinforcement learning is based on graph neural networks (GNN), which consists of some sub-networks, each one for a node on a directed graph. The graph is defined according to the domain ontology and each node can be considered as a sub-agent. During decision making, these sub-agents have internal message exchange between neighbors on the graph. We also propose an approach to jointly optimize the graph structure as well as the parameters of GNN. Experiments show that structured DRL significantly outperforms previous state-of-the-art approaches in almost all of the 18 tasks of the PyDial benchmark.

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Cost-Sensitive Active Learning for Dialogue State Tracking
Kaige Xie | Cheng Chang | Liliang Ren | Lu Chen | Kai Yu
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

Dialogue state tracking (DST), when formulated as a supervised learning problem, relies on labelled data. Since dialogue state annotation usually requires labelling all turns of a single dialogue and utilizing context information, it is very expensive to annotate all available unlabelled data. In this paper, a novel cost-sensitive active learning framework is proposed based on a set of new dialogue-level query strategies. This is the first attempt to apply active learning for dialogue state tracking. Experiments on DSTC2 show that active learning with mixed data query strategies can effectively achieve the same DST performance with significantly less data annotation compared to traditional training approaches.

2017

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On-line Dialogue Policy Learning with Companion Teaching
Lu Chen | Runzhe Yang | Cheng Chang | Zihao Ye | Xiang Zhou | Kai Yu
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

On-line dialogue policy learning is the key for building evolvable conversational agent in real world scenarios. Poor initial policy can easily lead to bad user experience and consequently fail to attract sufficient users for policy training. A novel framework, companion teaching, is proposed to include a human teacher in the dialogue policy training loop to address the cold start problem. Here, dialogue policy is trained using not only user’s reward, but also teacher’s example action as well as estimated immediate reward at turn level. Simulation experiments showed that, with small number of human teaching dialogues, the proposed approach can effectively improve user experience at the beginning and smoothly lead to good performance with more user interaction data.

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Affordable On-line Dialogue Policy Learning
Cheng Chang | Runzhe Yang | Lu Chen | Xiang Zhou | Kai Yu
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

The key to building an evolvable dialogue system in real-world scenarios is to ensure an affordable on-line dialogue policy learning, which requires the on-line learning process to be safe, efficient and economical. But in reality, due to the scarcity of real interaction data, the dialogue system usually grows slowly. Besides, the poor initial dialogue policy easily leads to bad user experience and incurs a failure of attracting users to contribute training data, so that the learning process is unsustainable. To accurately depict this, two quantitative metrics are proposed to assess safety and efficiency issues. For solving the unsustainable learning problem, we proposed a complete companion teaching framework incorporating the guidance from the human teacher. Since the human teaching is expensive, we compared various teaching schemes answering the question how and when to teach, to economically utilize teaching budget, so that make the online learning process affordable.

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Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning
Lu Chen | Xiang Zhou | Cheng Chang | Runzhe Yang | Kai Yu
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Hand-crafted rules and reinforcement learning (RL) are two popular choices to obtain dialogue policy. The rule-based policy is often reliable within predefined scope but not self-adaptable, whereas RL is evolvable with data but often suffers from a bad initial performance. We employ a companion learning framework to integrate the two approaches for on-line dialogue policy learning, in which a pre-defined rule-based policy acts as a “teacher” and guides a data-driven RL system by giving example actions as well as additional rewards. A novel agent-aware dropout Deep Q-Network (AAD-DQN) is proposed to address the problem of when to consult the teacher and how to learn from the teacher’s experiences. AAD-DQN, as a data-driven student policy, provides (1) two separate experience memories for student and teacher, (2) an uncertainty estimated by dropout to control the timing of consultation and learning. Simulation experiments showed that the proposed approach can significantly improve both safetyand efficiency of on-line policy optimization compared to other companion learning approaches as well as supervised pre-training using static dialogue corpus.

2016

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Clustering for Simultaneous Extraction of Aspects and Features from Reviews
Lu Chen | Justin Martineau | Doreen Cheng | Amit Sheth
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Recurrent Polynomial Network for Dialogue State Tracking with Mismatched Semantic Parsers
Qizhe Xie | Kai Sun | Su Zhu | Lu Chen | Kai Yu
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Hyper-parameter Optimisation of Gaussian Process Reinforcement Learning for Statistical Dialogue Management
Lu Chen | Pei-Hao Su | Milica Gašić
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2014

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Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy
Justin Martineau | Lu Chen | Doreen Cheng | Amit Sheth
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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The SJTU System for Dialog State Tracking Challenge 2
Kai Sun | Lu Chen | Su Zhu | Kai Yu
Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)