Junhui Li

AAAI Conference 2026 Conference Paper

Evaluating, Synthesizing, and Enhancing for Customer Support Conversation

• Jie Zhu
• Huaixia Dou
• Junhui Li
• Lifan Guo
• Feng Chen
• Chi Zhang
• Fang Kong

Effective customer support requires not only accurate problem-solving but also structured and empathetic communication aligned with professional standards. However, existing dialogue datasets often lack strategic guidance, and real-world service data is difficult to access and annotate. To address this, we introduce the task of Customer Support Conversation (CSC), aimed at training customer service supporters to respond using well-defined support strategies. We propose a structured CSC framework grounded in COPC guidelines, defining five conversational stages and twelve strategies to guide high-quality interactions. Based on this, we construct CSConv, an evaluation dataset of 1,855 real-world customer–agent conversations rewritten using LLMs to reflect deliberate strategy use, and annotated accordingly. Additionally, we develop a role-playing approach that simulates strategy-rich conversations using LLM-powered roles aligned with the CSC framework, resulting in the training dataset RoleCS. Experiments show that fine-tuning strong LLMs on RoleCS significantly improves their ability to generate high-quality, strategy-aligned responses on CSConv. Human evaluations further confirm gains in problem resolution.

EAAI Journal 2026 Journal Article

Forest fire object detection based on multi-task model and extreme weather simulation algorithm

• Ruipeng Han
• Junhui Li
• Yunfei Liu
• Han Liu

EAAI Journal 2025 Journal Article

An efficient multi-task forest fire and smoke detection model

• Cong Chen
• Yunfei Liu
• Chenyu Zhang
• Junhui Li
• Xingliang Chen

EAAI Journal 2025 Journal Article

Attention Guided Class Activation Maps for Boosting Weakly Supervised Semantic Segmentation

• Junhui Li
• Lei Zhu
• Wenwu Wang
• Yin Gong

IROS Conference 2021 Conference Paper

Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators

• Linh Kästner
• Xinlin Zhao
• Teham Buiyan
• Junhui Li
• Zhengcheng Shen
• Jens Lambrecht
• Cornelius Marx

Deep Reinforcement Learning has emerged as an efficient dynamic obstacle avoidance method in highly dynamic environments. It has the potential to replace overly conservative or inefficient navigation approaches. However, integrating Deep Reinforcement Learning into existing navigation systems is still an open frontier due to the myopic nature of Deep-Reinforcement-Learning-based navigation, which hinders its widespread integration into current navigation systems. In this paper, we propose the concept of an intermediate planner to interconnect novel Deep-Reinforcement-Learning-based obstacle avoidance with conventional global planning methods using waypoint generation. Therefore, we integrate different waypoint generators into existing navigation systems and compare the joint system against traditional ones. We found an increased performance in terms of safety, efficiency and path smoothness, especially in highly dynamic environments.

IJCAI Conference 2021 Conference Paper

Improving Context-Aware Neural Machine Translation with Source-side Monolingual Documents

• Linqing Chen
• Junhui Li
• Zhengxian Gong
• Xiangyu Duan
• Boxing Chen
• Weihua Luo
• Min Zhang
• Guodong Zhou

Document context-aware machine translation remains challenging due to the lack of large-scale document parallel corpora. To make full use of source-side monolingual documents for context-aware NMT, we propose a Pre-training approach with Global Context (PGC). In particular, we first propose a novel self-supervised pre-training task, which contains two training objectives: (1) reconstructing the original sentence from a corrupted version; (2) generating a gap sentence from its left and right neighbouring sentences. Then we design a universal model for PGC which consists of a global context encoder, a sentence encoder and a decoder, with similar architecture to typical context-aware NMT models. We evaluate the effectiveness and generality of our pre-trained PGC model by adapting it to various downstream context-aware NMT models. Detailed experimentation on four different translation tasks demonstrates that our PGC approach significantly improves the translation performance of context-aware NMT. For example, based on the state-of-the-art SAN model, we achieve an averaged improvement of 1. 85 BLEU scores and 1. 59 Meteor scores on the four translation tasks.

IJCAI Conference 2021 Conference Paper

Improving Text Generation with Dynamic Masking and Recovering

• Zhidong Liu
• Junhui Li
• Muhua Zhu

Due to different types of inputs, diverse text generation tasks may adopt different encoder-decoder frameworks. Thus most existing approaches that aim to improve the robustness of certain generation tasks are input-relevant, and may not work well for other generation tasks. Alternatively, in this paper we present a universal approach to enhance the language representation for text generation on the base of generic encoder-decoder frameworks. This is done from two levels. First, we introduce randomness by randomly masking some percentage of tokens on the decoder side when training the models. In this way, instead of using ground truth history context, we use its corrupted version to predict the next token. Then we propose an auxiliary task to properly recover those masked tokens. Experimental results on several text generation tasks including machine translation (MT), AMR-to-text generation, and image captioning show that the proposed approach can significantly improve over competitive baselines without using any task-specific techniques. This suggests the effectiveness and generality of our proposed approach.

AAAI Conference 2021 Conference Paper

Multi-modal Multi-label Emotion Recognition with Heterogeneous Hierarchical Message Passing

• Dong Zhang
• Xincheng Ju
• Wei Zhang
• Junhui Li
• Shoushan Li
• Qiaoming Zhu
• Guodong Zhou

As an important research issue in affective computing community, multi-modal emotion recognition has become a hot topic in the last few years. However, almost all existing studies perform multiple binary classification for each emotion with focus on complete time series data. In this paper, we focus on multi-modal emotion recognition in a multilabel scenario. In this scenario, we consider not only the label-to-label dependency, but also the feature-to-label and modality-to-label dependencies. Particularly, we propose a heterogeneous hierarchical message passing network to effectively model above dependencies. Furthermore, we propose a new multi-modal multi-label emotion dataset based on partial time-series content to show predominant generalization of our model. Detailed evaluation demonstrates the effectiveness of our approach.

AAAI Conference 2019 Conference Paper

Generating Multiple Diverse Responses for Short-Text Conversation

• Jun Gao
• Wei Bi
• Xiaojiang Liu
• Junhui Li
• Shuming Shi

Neural generative models have become popular and achieved promising performance on short-text conversation tasks. They are generally trained to build a 1-to-1 mapping from the input post to its output response. However, a given post is often associated with multiple replies simultaneously in real applications. Previous research on this task mainly focuses on improving the relevance and informativeness of the top one generated response for each post. Very few works study generating multiple accurate and diverse responses for the same post. In this paper, we propose a novel response generation model, which considers a set of responses jointly and generates multiple diverse responses simultaneously. A reinforcement learning algorithm is designed to solve our model. Experiments on two short-text conversation tasks validate that the multiple responses generated by our model obtain higher quality and larger diversity compared with various state-ofthe-art generative models.

IJCAI Conference 2019 Conference Paper

Modeling Source Syntax and Semantics for Neural AMR Parsing

• DongLai Ge
• Junhui Li
• Muhua Zhu
• Shoushan Li

Sequence-to-sequence (seq2seq) approaches formalize Abstract Meaning Representation (AMR) parsing as a translation task from a source sentence to a target AMR graph. However, previous studies generally model a source sentence as a word sequence but ignore the inherent syntactic and semantic information in the sentence. In this paper, we propose two effective approaches to explicitly modeling source syntax and semantics into neural seq2seq AMR parsing. The first approach linearizes source syntactic and semantic structure into a mixed sequence of words, syntactic labels, and semantic labels, while in the second approach we propose a syntactic and semantic structure-aware encoding scheme through a self-attentive model to explicitly capture syntactic and semantic relations between words. Experimental results on an English benchmark dataset show that our two approaches achieve significant improvement of 3. 1% and 3. 4% F1 scores over a strong seq2seq baseline.