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Wenbin Jiang

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6 papers

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AAAI Conference 2026 Conference Paper

Dynamic Cognitive Planning for Cognitive-Functional Dialogue: A Case Study in Emotional Support Conversation

Jiaqi Liu
Yankun Yang
Jiakang Xu
Zhongqiang Du
Wenbin Jiang

Cognitive-functional dialogues, such as those for persuasion, consultation, and question-answering, are prevalent throughout human social interaction. The core difference between these dialogues and casual chat lies in their objective: to guide a person's cognitive and psychological state toward a predetermined one. Existing conversational technologies perform poorly in handling such dialogues. The fundamental reason is that the transformation of human cognitive psychology follows specific patterns, yet existing technologies neither account for these patterns nor possess cognitive guidance planning based on them. This deficiency makes it difficult for dialogues to achieve their intended cognitive-functional goals effectively. To address this, we propose a dynamic cognitive planning method (DyCoP). By modeling the long-term evolution of a user's cognitive psychology during the dialogue process, this method dynamically generates dialogue guidance plans that align with the principles of cognitive-psychological evolution. This allows for the generation of appropriate dialogue responses based on prior user psychology and the immediate conversational context, thereby achieving cognitive-functional goals more efficiently and accurately. Simultaneously, we constructed an evaluation framework for cognitive-functional dialogues and constructed a richly annotated emotional support conversation dataset. Comprehensive automatic and human evaluations show that our proposed DyCoP method demonstrates significant advantages over existing baseline models.

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EAAI Journal 2025 Journal Article

A search method for fractured-vuggy reservoir inter-well connectivity path based on multi-modal multi-agent

Wenbin Jiang
Dongmei Zhang
Hong Cao
Xiaofeng Wang

The complex geological structure of carbonate reservoirs and the intricate fracture-vuggy configurations obscure inter-well connectivity, making its evaluation challenging. Conventional studies primarily rely on seismic static data to delineate fracture-vuggy reservoirs, but the limited recognition accuracy hampers the precise characterization of inter-well connectivity and the spatial configuration of fractures and vugs. To address this, this study constructs a 3D (Three-Dimensional) search environment and use multi-modal static and dynamic data and proposes a multi-agent connected channel search model based on deep reinforcement learning. The model treats multiphase fluid as an agent and incorporates Swin Transformer (Shift Window Transformer) to extract large-scale fracture features from seismic data, providing global prior information for path search. A Graph Attention Network is established based on dynamic response relationships to extract spatial geological features, while a multi-head self-attention mechanism captures real-time fluid interactions in various directions. The model fuses multi-modal features, including seismic attributes and production data, to generate decisions and automatically search for inter-well connectivity channels. Experiments were conducted using the WE1 and WE5 well groups from the fault-controlled karst reservoirs in the Tahe oilfield, with results compared against tracer tests. The findings demonstrate that the proposed model's automatic search paths closely align with seismic data and tracer test results, effectively capturing the spatial distribution of fractures and vugs across different scales. This validates the model's effectiveness in evaluating inter-well connectivity in complex carbonate reservoirs.

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EAAI Journal 2024 Journal Article

A dual-branch fracture attribute fusion network based on prior knowledge

Wenbin Jiang
Dongmei Zhang
Gang Hui

The Tarim Basin region harbors abundant carbonate reservoirs in which fractures throughout the structures play a crucial role in facilitating the transportation of oil and gas. The degree and distribution of fracture development play a crucial role in achieving a high yield and stable production. Seismic attribute analysis is a widely used and efficacious approach for examining fractures in reservoirs. However, different seismic attributes characterize fractures at different scales, and any single seismic attribute may be of poor quality, making it difficult to describe reservoir channel media comprehensively. In the present study, the seismic attribute volume was sliced from three perspectives to obtain complete geological spatial structural information of the seismic volume. This was accomplished using a shifted window (Swin) transformer module guided by a t-distribution before extracting and capturing the prior information and global contextual cues of the seismic attributes. Additionally, a dual-branch fusion (DBFusion) network that combined a convolutional neural network and a Swin transformer to extract the spatial geological structural features of seismic attributes was used to facilitate a comprehensive integration of local and global contextual information. Through the DBFusion network modeling, the network comprehensively extracted local information and integrated complementary global information. Taking three-dimensional seismic data from a unit in the Tarim Basin as an example, this study explores the fusion of three attributes: coherence, ant tracking, and curvature. When compared to individual attributes, the fused attributes can comprehensively leverage the advantages of all three seismic attributes in reflecting fractures. This results in a more precise representation of fractures of varying sizes in the fused output. It was discovered through comparative experiments that the DBFusion network proposed in this paper exhibits the best performance compared to other models.

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AAAI Conference 2023 Conference Paper

Inferential Knowledge-Enhanced Integrated Reasoning for Video Question Answering

Jianguo Mao
Wenbin Jiang
Hong Liu
Xiangdong Wang
Yajuan Lyu

Recently, video question answering has attracted growing attention. It involves answering a question based on a fine-grained understanding of video multi-modal information. Most existing methods have successfully explored the deep understanding of visual modality. We argue that a deep understanding of linguistic modality is also essential for answer reasoning, especially for videos that contain character dialogues. To this end, we propose an Inferential Knowledge-Enhanced Integrated Reasoning method. Our method consists of two main components: 1) an Inferential Knowledge Reasoner to generate inferential knowledge for linguistic modality inputs that reveals deeper semantics, including the implicit causes, effects, mental states, etc. 2) an Integrated Reasoning Mechanism to enhance video content understanding and answer reasoning by leveraging the generated inferential knowledge. Experimental results show that our method achieves significant improvement on two mainstream datasets. The ablation study further demonstrates the effectiveness of each component of our approach.

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AAAI Conference 2020 Conference Paper

Capturing Sentence Relations for Answer Sentence Selection with Multi-Perspective Graph Encoding

Zhixing Tian
Yuanzhe Zhang
Xinwei Feng
Wenbin Jiang
Yajuan Lyu
Kang Liu
Jun Zhao

This paper focuses on the answer sentence selection task. Unlike previous work, which only models the relation between the question and each candidate sentence, we propose Multi-Perspective Graph Encoder (MPGE) to take the relations among the candidate sentences into account and capture the relations from multiple perspectives. By utilizing MPGE as a module, we construct two answer sentence selection models which are based on traditional representation and pre-trained representation, respectively. We conduct extensive experiments on two datasets, WikiQA and SQuAD. The results show that the proposed MPGE is effective for both types of representation. Moreover, the overall performance of our proposed model surpasses the state-of-the-art on both datasets. Additionally, we further validate the robustness of our method by the adversarial examples of AddSent and AddOneSent.

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IJCAI Conference 2015 Conference Paper

Joint Learning of Constituency and Dependency Grammars by Decomposed Cross-Lingual Induction

Wenbin Jiang
Qun Liu
Thepchai Supnithi

Cross-lingual induction aims to acquire for one language some linguistic structures resorting to annotations from another language. It works well for simple structured predication problems such as part-of-speech tagging and dependency parsing, but lacks of significant progress for more complicated problems such as constituency parsing and deep semantic parsing, mainly due to the structural non-isomorphism between languages. We propose a decomposed projection strategy for crosslingual induction, where cross-lingual projection is performed in unit of fundamental decisions of the structured predication. Compared with the structured projection that projects the complete structures, decomposed projection achieves better adaptation of non-isomorphism between languages and efficiently acquires the structured information across languages, thus leading to better performance. For joint cross-lingual induction of constituency and dependency grammars, decomposed cross-lingual induction achieves very significant improvement in both constituency and dependency grammar induction.

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