Zhe Hu   胡哲

Biography

I am a senior research engineer at Baidu Inc. I received my Master Degree in Computer Engineering from Northeastern University. I was a member of Northeastern NLP Group and worked with Prof. Lu Wang (now at UMich) on text generation and abstractive summarization.

Research Interests

I'm generally interested in natural language processing. My goal is to design methods that can understand human languages from large-scale data of various domains, and also generate rich and coherent outputs of high quality and controllability. Recently, I'm working on topics including:

• style-controlled dialogue generation and long-form text generation;
• coherent and high-quality summary extraction and generation;
• natural language inference and textual semantic similarity modeling;

Experience

• 03/2020 - Present: Senior Research Engineer at Baidu
• 06/2019 - 03/2020: Research Engineer at Baidu
• 03/2019 - 05/2019: Research Assistant at Khoury College of Computer Sciences of Northeastern University, working with Prof. Lu Wang

Publications

• Controllable Dialogue Generation with Disentangled Multi-grained Style Specification and Attribute Consistency Reward
Zhe Hu*, Zhiwei Cao*, Hou Pong Chan, Jiachen Liu, Xinyan Xiao, Jinsong Su, Hua Wu
[*equal contribution]
In arXiv, 2021
arXiv Abstract
×
Controllable Dialogue Generation with Disentangled Multi-grained Style Specification and Attribute Consistency Reward



Controllable text generation is an appealing but challenging task, which allows users to specify particular attributes of the generated responses. In this paper, we propose a controllable dialogue generation model to steer the response generation under multi-attribute constraints. Specifically, we first define and categorize the commonly-used control attributes into global and local ones, which possess different granularities of effects on response generation. Then, we significantly extend the conventional Seq2seq framework by introducing a novel two-stage decoder, which first uses a multi-grained style specification layer to impose the stylistic constraints and determine the word-level control states of responses based on the attributes, and then employs a response generation layer to generate final responses maintaining both semantic relevancy to the contexts and fidelity to the attributes. Furthermore, we train our model with an attribute consistency reward to promote response control with explicit supervision signals. Extensive experiments and in-depth analyses on two datasets indicate that our model can significantly outperform competitive baselines in terms of response quality, content diversity and controllability.

• Context-Aware Interaction Network for Question Matching
Zhe Hu, Zuohui Fu, Yu Yin, Gerard de Melo
In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021, To Appear.
arXiv Abstract
×
Context-Aware Interaction Network for Question Matching.



Impressive milestones have been achieved in text matching by adopting a cross-attention mechanism to capture pertinent semantic connections between two sentences. However, these cross-attention mechanisms focus on word-level links between the two inputs, neglecting the importance of contextual information. We propose a context-aware interaction network (COIN) to properly align two sequences and infer their semantic relationship. Specifically, each interaction block includes (1) a context-aware cross-attention mechanism to effectively integrate contextual information, and (2) a gate fusion layer to flexibly interpolate aligned representations. We apply multiple stacked interaction blocks to produce alignments at different levels and gradually refine the attention results. Experiments on two question matching datasets and detailed analyses confirm the effectiveness of our model.

• Enhanced Sentence Alignment Network for Efficient Short Text Matching
Zhe Hu, Zuohui Fu, Cheng Peng, Weiwei Wang
In Proceedings of EMNLP 2020 Workshop on Noisy User-generated Text (EMNLP Workshop), short paper, 2020.
PDF Slides Abstract
×
Enhanced Sentence Alignment Network for Efficient Short Text Matching.



Cross-sentence attention has been widely applied in text matching, in which model learns the aligned information between two intermediate sequence representations to capture their semantic relationship. However, the intermediate representations are generated based on the preceding layers and the models may suffer from error propagation and unstable matching, especially when multiple attention layers are used. In this paper, we propose an enhanced sentence alignment network with simple gated feature augmentation, where the model is able to flexibly integrate both original word and contextual features to improve the cross-sentence attention. Moreover, our model is less complex with fewer parameters compared to many state-of-the-art structures. Experiments on three benchmark datasets validate our model capacity for text matching.

• An Entity-Driven Framework for Abstractive Summarization
Eva Sharma*, Luyang Huang*, Zhe Hu*, Lu Wang
[*equal contribution]
In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.
Errata: Equation (9) in section 2.3 should be: \(L(S_A,S_B^+, S_B^-) = \max \{0, 1 + Coh(S_A, S_B^-) - Coh(S_A, S_B^+)\} \)
arXiv Poster Code Demo Abstract
×
An Entity-Driven Framework for Abstractive Summarization.



Abstractive summarization systems aim to produce more coherent and concise summaries than their extractive counterparts. Popular neural models have achieved impressive results for single-document summarization, yet their outputs are often incoherent and unfaithful to the input. In this paper, we introduce SENECA, a novel System for ENtitydrivEn Coherent Abstractive summarization framework that leverages entity information to generate informative and coherent abstracts. Our framework takes a two-step approach: (1) an entity-aware content selection module first identifies salient sentences from the input, then (2) an abstract generation module conducts cross-sentence information compression and abstraction to generate the final summary, which is trained with rewards to promote coherence, conciseness, and clarity. The two components are further connected using reinforcement learning. Automatic evaluation shows that our model significantly outperforms previous state-of-the-art on ROUGE and our proposed coherence measures on New York Times and CNN/Daily Mail datasets. Human judges further rate our system summaries as more informative and coherent than those by popular summarization models.

• Argument Generation with Retrieval, Planning, and Realization
Xinyu Hua, Zhe Hu, Lu Wang
In Proceedings of the Association for Computational Linguistics (ACL), 2019.
arXiv Code Demo Abstract
×
Argument Generation with Retrieval, Planning, and Realization.



Automatic argument generation is an appealing but challenging task. In this paper, we study the specific problem of counter-argument generation, and present a novel framework, CANDELA. It consists of a powerful retrieval system and a novel two-step generation model, where a text planning decoder first decides on the main talking points and a proper language style for each sentence, then a content realization decoder reflects the decisions and constructs an informative paragraph-level argument. Furthermore, our generation model is empowered by a retrieval system indexed with 12 million articles collected from Wikipedia and popular English news media, which provides access to high-quality content with diversity. Automatic evaluation on a large-scale dataset collected from Reddit shows that our model yields significantly higher BLEU, ROUGE, and METEOR scores than the state-of-the-art and non-trivial comparisons. Human evaluation further indicates that our system arguments are more appropriate for refutation and richer in content.

Education

• M.Sc in Computer Engineering, Northeastern University
• B.Eng in Communication Engineering, Beijing University of Posts and Telecommunications (BUPT)

Services

• EMNLP 2021, ACL 2021, EMNLP 2020, ACL 2020, EMNLP 2019 NewSum workshop