Zhe (Derek) Hu   胡哲

Biography

I am a senior research engineer at Baidu Inc. I received my Master Degree in Computer Engineering from Northeastern University (in Boston). I was a member of Northeastern NLP Group and worked with Prof. Lu Wang (now at UMich). I'm also privileged to work with Prof. Lifu Huang from Virginia Tech.

I'm generally interested in computational linguistics and natural language processing. My goal is to design methods that can understand human languages of various domains, and also generate factual and coherent outputs, with a focus on long-form discourse (e.g., argumentation, summarization). Recently I also work on leveraging LLMs as agent and refining LLMs through instruction tuning, with the application on creative and interactive text generation.

Experience

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

Selected Publications

• MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective
Zhe Hu, Hou Pong Chan, Lifu Huang
EMNLP 2022, short paper
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MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective



Teaching neural models to generate narrative coherent texts is a critical problem. Recent pre-trained language models have achieved promising results, but there is still a gap between human written texts and machine-generated outputs. In this work, we propose a novel multi-task training strategy for long text generation grounded on the cognitive theory of writing, which empowers the model to learn essential subskills needed for writing including planning and reviewing besides end-to-end generation. We extensively evaluate our model on three open-ended generation tasks including story generation, news article writing and argument generation. Experiments show that our model achieves better results on both few-shot and fully-supervised settings than strong baselines, and human evaluations confirm that our model can generate more coherent outputs.

• PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation
Zhe Hu, Hou Pong Chan, Jiachen Liu, Xinyan Xiao, Hua Wu, Lifu Huang
ACL 2022
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PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation



Despite recent progress of pre-trained language models on generating fluent text, existing models still suffer from incoherence in long-form text generation tasks that require proper content control and planning to form a coherent high-level logical flow. In this work, we propose PLANET, a novel generation framework leveraging autoregressive self-attention mechanism to conduct content planning and surface realization dynamically. To guide the generation of output sentences, our framework enriches the Transformer decoder with latent representations to maintain sentence-level semantic plans grounded by bag-of-words. Moreover, we introduce a new coherence-based contrastive learning objective to further improve the coherence of output. Extensive experiments are conducted on two challenging opinion generation tasks including counter-argument generation and opinion article generation. Both automatic and human evaluations show that our method significantly outperforms strong baselines and generates more coherent text with richer contents.

• 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]
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) 2022
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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
EMNLP 2021, short paper
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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.

• An Entity-Driven Framework for Abstractive Summarization
Eva Sharma*, Luyang Huang*, Zhe Hu*, Lu Wang
[*equal contribution]
EMNLP 2019
Errata: Equation (9) in section 2.3 should be: \(L(S_A,S_B^+, S_B^-) = \max \{0, 1 + Coh(S_A, S_B^-) - Coh(S_A, S_B^+)\} \)
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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
ACL 2019
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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

• Program Committee Member:
  • ACL ARR
  • 2023: ACL, EMNLP, AAAI, CoNLL, EMNLP NewSum workshop
  • 2022: EMNLP, AAAI
  • 2021: ACL, EMNLP, NAACL, EACL, AAAI
  • 2020: ACL, EMNLP
  • 2019: EMNLP NewSum workshop