14 citations · 31 across the 15 of their papers we have counts for
15 papers
One Model, Multiple Goals: Adaptive Multi-Objective Learning for E-commerce Dialogue Systems
Mingzhe Li, Jing Xiang, Enguo Zhou +5
Dialogue systems in e-commerce scenarios often need to satisfy multiple objectives: accurately reasoning over user profiles (e.g., eligibility, credit limit) to ensure correct deci…
Flipping Knowledge Distillation: Leveraging Small Models' Expertise to Enhance LLMs in Text Matching
Mingzhe Li, Jing Xiang, Qishen Zhang +2
Knowledge distillation typically involves transferring knowledge from a Large Language Model (LLM) to a Smaller Language Model (SLM). However, in tasks such as text matching, fine-…
Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey
Zirui Song, Bin Yan, Yuhan Liu +4
Large Language Models (LLMs) have demonstrated remarkable success in various tasks such as natural language understanding, text summarization, and machine translation. However, the…
Write Summary Step-by-Step: A Pilot Study of Stepwise Summarization
Xiuying Chen, Shen Gao, Mingzhe Li +3
Nowadays, neural text generation has made tremendous progress in abstractive summarization tasks. However, most of the existing summarization models take in the whole document all…
Flexible and Adaptable Summarization via Expertise Separation
Xiuying Chen, Mingzhe Li, Shen Gao +5
A proficient summarization model should exhibit both flexibility -- the capacity to handle a range of in-domain summarization tasks, and adaptability -- the competence to acquire n…
Multi-Intent Attribute-Aware Text Matching in Searching
Mingzhe Li, Xiuying Chen, Jing Xiang +6
Text matching systems have become a fundamental service in most searching platforms. For instance, they are responsible for matching user queries to relevant candidate items, or re…
Improving the Robustness of Summarization Systems with Dual Augmentation
Xiuying Chen, Guodong Long, Chongyang Tao +4
A robust summarization system should be able to capture the gist of the document, regardless of the specific word choices or noise in the input. In this work, we first explore the…
A Topic-aware Summarization Framework with Different Modal Side Information
Xiuying Chen, Mingzhe Li, Shen Gao +5
Automatic summarization plays an important role in the exponential document growth on the Web. On content websites such as CNN.com and WikiHow.com, there often exist various kinds…
Learning towards Selective Data Augmentation for Dialogue Generation
Xiuying Chen, Mingzhe Li, Jiayi Zhang +6
As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples…
Towards Personalized Review Summarization by Modeling Historical Reviews from Customer and Product Separately
Xin Cheng, Shen Gao, Yuchi Zhang +5
Review summarization is a non-trivial task that aims to summarize the main idea of the product review in the E-commerce website. Different from the document summary which only need…
EZInterviewer: To Improve Job Interview Performance with Mock Interview Generator
Mingzhe Li, Xiuying Chen, Weiheng Liao +4
Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock intervie…
Scientific Paper Extractive Summarization Enhanced by Citation Graphs
Xiuying Chen, Mingzhe Li, Shen Gao +3
In a citation graph, adjacent paper nodes share related scientific terms and topics. The graph thus conveys unique structure information of document-level relatedness that can be u…
Towards Improving Faithfulness in Abstractive Summarization
Xiuying Chen, Mingzhe Li, Xin Gao +1
Despite the success achieved in neural abstractive summarization based on pre-trained language models, one unresolved issue is that the generated summaries are not always faithful…
Sketching Merge Trees for Scientific Data Visualization
Mingzhe Li, Sourabh Palande, Lin Yan +1
Merge trees are a type of topological descriptors that record the connectivity among the sublevel sets of scalar fields. They are among the most widely used topological tools in vi…
Using GGNN to recommend log statement level
Mingzhe Li, Jianrui Pei, Jin He +4
In software engineering, log statement is an important part because programmers can't access to users' program and they can only rely on log message to find the root of bugs. The m…