Pengfei Zhu

Also published as: PengFei Zhu


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Retrieval-Augmented Domain Adaptation of Language Models
Benfeng Xu | Chunxu Zhao | Wenbin Jiang | PengFei Zhu | Songtai Dai | Chao Pang | Zhuo Sun | Shuohuan Wang | Yu Sun
Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)

Language models pretrained on general domain corpora usually exhibit considerable degradation when generalizing to downstream tasks of specialized domains. Existing approaches try to construct PLMs for each specific domains either from scratch or through further pretraining, which not only costs substantial resources, but also fails to cover all target domains at various granularity. In this work, we propose RADA, a novel Retrieval-Augmented framework for Domain Adaptation. We first construct a textual corpora that covers the downstream task at flexible domain granularity and resource availability. We employ it as a pluggable datastore to retrieve informative background knowledge, and integrate them into the standard language model framework to augment representations. We then propose a two-level selection scheme to integrate the most relevant information while alleviating irrelevant noises. Specifically, we introduce a differentiable sampling module as well as an attention mechanism to achieve both passage-level and word-level selection. Such a retrieval-augmented framework enables domain adaptation of language models with flexible domain coverage and fine-grained domain knowledge integration. We conduct comprehensive experiments across biomedical, science and legal domains to demonstrate the effectiveness of the overall framework, and its advantage over existing solutions.


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Modeling Multi-turn Conversation with Deep Utterance Aggregation
Zhuosheng Zhang | Jiangtong Li | Pengfei Zhu | Hai Zhao | Gongshen Liu
Proceedings of the 27th International Conference on Computational Linguistics

Multi-turn conversation understanding is a major challenge for building intelligent dialogue systems. This work focuses on retrieval-based response matching for multi-turn conversation whose related work simply concatenates the conversation utterances, ignoring the interactions among previous utterances for context modeling. In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation. In detail, a self-matching attention is first introduced to route the vital information in each utterance. Then the model matches a response with each refined utterance and the final matching score is obtained after attentive turns aggregation. Experimental results show our model outperforms the state-of-the-art methods on three multi-turn conversation benchmarks, including a newly introduced e-commerce dialogue corpus.

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Lingke: a Fine-grained Multi-turn Chatbot for Customer Service
Pengfei Zhu | Zhuosheng Zhang | Jiangtong Li | Yafang Huang | Hai Zhao
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

Traditional chatbots usually need a mass of human dialogue data, especially when using supervised machine learning method. Though they can easily deal with single-turn question answering, for multi-turn the performance is usually unsatisfactory. In this paper, we present Lingke, an information retrieval augmented chatbot which is able to answer questions based on given product introduction document and deal with multi-turn conversations. We will introduce a fine-grained pipeline processing to distill responses based on unstructured documents, and attentive sequential context-response matching for multi-turn conversations.