Search Results for author: Yanran Li

Found 17 papers, 10 papers with code

C^3KG: A Chinese Commonsense Conversation Knowledge Graph

1 code implementation Findings (ACL) 2022 Dawei Li, Yanran Li, Jiayi Zhang, Ke Li, Chen Wei, Jianwei Cui, Bin Wang

Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps.

Multi-level Contrastive Learning for Script-based Character Understanding

1 code implementation20 Oct 2023 Dawei Li, Hengyuan Zhang, Yanran Li, Shiping Yang

In this work, we tackle the scenario of understanding characters in scripts, which aims to learn the characters' personalities and identities from their utterances.

Contrastive Learning

NarrativePlay: Interactive Narrative Understanding

no code implementations2 Oct 2023 Runcong Zhao, Wenjia Zhang, Jiazheng Li, Lixing Zhu, Yanran Li, Yulan He, Lin Gui

In this paper, we introduce NarrativePlay, a novel system that allows users to role-play a fictional character and interact with other characters in narratives such as novels in an immersive environment.

CharacterChat: Learning towards Conversational AI with Personalized Social Support

1 code implementation20 Aug 2023 Quan Tu, Chuanqi Chen, Jinpeng Li, Yanran Li, Shuo Shang, Dongyan Zhao, Ran Wang, Rui Yan

In our modern, fast-paced, and interconnected world, the importance of mental well-being has grown into a matter of great urgency.

Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning

no code implementations9 Jun 2023 Hengyuan Zhang, Dawei Li, Yanran Li, Chenming Shang, Chufan Shi, Yong Jiang

The standard definition generation task requires to automatically produce mono-lingual definitions (e. g., English definitions for English words), but ignores that the generated definitions may also consist of unfamiliar words for language learners.

Machine Translation

Distinguishability Calibration to In-Context Learning

1 code implementation13 Feb 2023 Hongjing Li, Hanqi Yan, Yanran Li, Li Qian, Yulan He, Lin Gui

When using prompt-based learning for text classification, the goal is to use a pre-trained language model (PLM) to predict a missing token in a pre-defined template given an input text, which can be mapped to a class label.

In-Context Learning Language Modelling +3

BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation

no code implementations17 Jan 2023 Xiangyu Qin, Zhiyu Wu, Jinshi Cui, Tingting Zhang, Yanran Li, Jian Luan, Bin Wang, Li Wang

Accordingly, we propose a novel paradigm, i. e., exploring contextual information and dialogue structure information in the fine-tuning step, and adapting the PLM to the ERC task in terms of input text, classification structure, and training strategy.

Emotion Recognition in Conversation text-classification +1

MIMO Is All You Need : A Strong Multi-In-Multi-Out Baseline for Video Prediction

1 code implementation9 Dec 2022 Shuliang Ning, Mengcheng Lan, Yanran Li, Chaofeng Chen, Qian Chen, Xunlai Chen, Xiaoguang Han, Shuguang Cui

The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to predict the next frame in a recursive manner.

Video Prediction

Fine-grained Contrastive Learning for Definition Generation

1 code implementation2 Oct 2022 Hengyuan Zhang, Dawei Li, Shiping Yang, Yanran Li

Recently, pre-trained transformer-based models have achieved great success in the task of definition generation (DG).

Contrastive Learning Representation Learning

C3KG: A Chinese Commonsense Conversation Knowledge Graph

1 code implementation6 Apr 2022 Dawei Li, Yanran Li, Jiayi Zhang, Ke Li, Chen Wei, Jianwei Cui, Bin Wang

Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps.

MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation

1 code implementation ACL 2022 Quan Tu, Yanran Li, Jianwei Cui, Bin Wang, Ji-Rong Wen, Rui Yan

Applying existing methods to emotional support conversation -- which provides valuable assistance to people who are in need -- has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user's instant mental state; (b) most of them focus on expressing empathy in the response(s) rather than gradually reducing user's distress.

SharpContour: A Contour-based Boundary Refinement Approach for Efficient and Accurate Instance Segmentation

no code implementations CVPR 2022 Chenming Zhu, Xuanye Zhang, Yanran Li, Liangdong Qiu, Kai Han, Xiaoguang Han

Contour-based models are efficient and generic to be incorporated with any existing segmentation methods, but they often generate over-smoothed contour and tend to fail on corner areas.

Instance Segmentation Segmentation +1

From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video Forecasting

no code implementations21 Jul 2021 Mengcheng Lan, Shuliang Ning, Yanran Li, Qian Chen, Xunlai Chen, Xiaoguang Han, Shuguang Cui

Despite video forecasting has been a widely explored topic in recent years, the mainstream of the existing work still limits their models with a single prediction space but completely neglects the way to leverage their model with multi-prediction spaces.

Video Prediction

Towards an Online Empathetic Chatbot with Emotion Causes

no code implementations11 May 2021 Yanran Li, Ke Li, Hongke Ning, Xiaoqiang Xia, Yalong Guo, Chen Wei, Jianwei Cui, Bin Wang

Existing emotion-aware conversational models usually focus on controlling the response contents to align with a specific emotion class, whereas empathy is the ability to understand and concern the feelings and experience of others.


Writing Polishment with Simile: Task, Dataset and A Neural Approach

1 code implementation15 Dec 2020 Jiayi Zhang, Zhi Cui, Xiaoqiang Xia, Yalong Guo, Yanran Li, Chen Wei, Jianwei Cui

In this paper, we propose a new task of Writing Polishment with Simile (WPS) to investigate whether machines are able to polish texts with similes as we human do.

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