Search Results for author: Li Zheng

Found 5 papers, 1 papers with code

Reverse Multi-Choice Dialogue Commonsense Inference with Graph-of-Thought

1 code implementation23 Dec 2023 Li Zheng, Hao Fei, Fei Li, Bobo Li, Lizi Liao, Donghong Ji, Chong Teng

With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of comprehending user queries and intentions.

Question Answering

A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition

no code implementations8 Aug 2023 Li Zheng, Fei Li, Yuyang Chai, Chong Teng, Donghong Ji

The joint task of Dialog Sentiment Classification (DSC) and Act Recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog simultaneously.

Contrastive Learning feature selection +2

ECQED: Emotion-Cause Quadruple Extraction in Dialogs

no code implementations6 Jun 2023 Li Zheng, Donghong Ji, Fei Li, Hao Fei, Shengqiong Wu, Jingye Li, Bobo Li, Chong Teng

The existing emotion-cause pair extraction (ECPE) task, unfortunately, ignores extracting the emotion type and cause type, while these fine-grained meta-information can be practically useful in real-world applications, i. e., chat robots and empathic dialog generation.

Emotion-Cause Pair Extraction

Data-driven topology optimization of spinodoid metamaterials with seamlessly tunable anisotropy

no code implementations31 Dec 2020 Li Zheng, Siddhant Kumar, Dennis M. Kochmann

We present a two-scale topology optimization framework for the design of macroscopic bodies with an optimized elastic response, which is achieved by means of a spatially-variant cellular architecture on the microscale.

Computational Engineering, Finance, and Science

AddGraph_ Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN

no code implementations (IJCAI 2019 Li Zheng, Zhenpeng Li, Jian Li, Zhao Li, and Jun Gao

Anomaly detection in dynamic graphs becomes very critical in many different application scenarios, e. g., recommender systems, while it also raises huge challenges due to the high flexible nature of anomaly and lack of sufficient labelled data.

Anomaly Detection Edge Detection +1

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