Bowen Tan


2020

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Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach
Bowen Tan | Lianhui Qin | Eric Xing | Zhiting Hu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Given a document and a target aspect (e.g., a topic of interest), aspect-based abstractive summarization attempts to generate a summary with respect to the aspect. Previous studies usually assume a small pre-defined set of aspects and fall short of summarizing on other diverse topics. In this work, we study summarizing on arbitrary aspects relevant to the document, which significantly expands the application of the task in practice. Due to the lack of supervision data, we develop a new weak supervision construction method and an aspect modeling scheme, both of which integrate rich external knowledge sources such as ConceptNet and Wikipedia. Experiments show our approach achieves performance boosts on summarizing both real and synthetic documents given pre-defined or arbitrary aspects.

2019

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Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
Zhiting Hu | Haoran Shi | Bowen Tan | Wentao Wang | Zichao Yang | Tiancheng Zhao | Junxian He | Lianhui Qin | Di Wang | Xuezhe Ma | Zhengzhong Liu | Xiaodan Liang | Wanrong Zhu | Devendra Sachan | Eric Xing
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization, dialog, content manipulation, and so forth. With the design goals of modularity, versatility, and extensibility in mind, Texar extracts common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules and functionalities, and allows arbitrary model architectures and algorithmic paradigms. In Texar, model architecture, inference, and learning processes are properly decomposed. Modules at a high concept level can be freely assembled or plugged in/swapped out. Texar is thus particularly suitable for researchers and practitioners to do fast prototyping and experimentation. The versatile toolkit also fosters technique sharing across different text generation tasks. Texar supports both TensorFlow and PyTorch, and is released under Apache License 2.0 at https://www.texar.io.

2018

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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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Texar: A Modularized, Versatile, and Extensible Toolbox for Text Generation
Zhiting Hu | Zichao Yang | Tiancheng Zhao | Haoran Shi | Junxian He | Di Wang | Xuezhe Ma | Zhengzhong Liu | Xiaodan Liang | Lianhui Qin | Devendra Singh Chaplot | Bowen Tan | Xingjiang Yu | Eric Xing
Proceedings of Workshop for NLP Open Source Software (NLP-OSS)

We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks. Different from many existing toolkits that are specialized for specific applications (e.g., neural machine translation), Texar is designed to be highly flexible and versatile. This is achieved by abstracting the common patterns underlying the diverse tasks and methodologies, creating a library of highly reusable modules and functionalities, and enabling arbitrary model architectures and various algorithmic paradigms. The features make Texar particularly suitable for technique sharing and generalization across different text generation applications. The toolkit emphasizes heavily on extensibility and modularized system design, so that components can be freely plugged in or swapped out. We conduct extensive experiments and case studies to demonstrate the use and advantage of the toolkit.