Search Results for author:
Found 20 papers, 10 papers with code
C^3KG: A Chinese Commonsense Conversation Knowledge Graph
Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps.
Exploring the latent space of diffusion models directly through singular value decomposition
Despite the groundbreaking success of diffusion models in generating high-fidelity images, their latent space remains relatively under-explored, even though it holds significant promise for enabling versatile and interpretable image editing capabilities.
ViMo: Generating Motions from Casual Videos
Although humans have the innate ability to imagine multiple possible actions from videos, it remains an extraordinary challenge for computers due to the intricate camera movements and montages.
Towards a Client-Centered Assessment of LLM Therapists by Client Simulation
To this end, we adopt LLMs to simulate clients and propose ClientCAST, a client-centered approach to assessing LLM therapists by client simulation.
Multi-level Contrastive Learning for Script-based Character Understanding
In this work, we tackle the scenario of understanding characters in scripts, which aims to learn the characters' personalities and identities from their utterances.
NarrativePlay: Interactive Narrative Understanding
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
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
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.
Distinguishability Calibration to In-Context Learning
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.
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation
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.
Predicting Ejection Fraction from Chest X-rays Using Computer Vision for Diagnosing Heart Failure
We studied a dataset of 3488 CXRs from the MIMIC CXR-jpg (MCR) dataset.
MIMO Is All You Need : A Strong Multi-In-Multi-Out Baseline for Video Prediction
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.
Fine-grained Contrastive Learning for Definition Generation
Recently, pre-trained transformer-based models have achieved great success in the task of definition generation (DG).
Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognition
Our module can work seamlessly with the existing action classification model.
C3KG: A Chinese Commonsense Conversation Knowledge Graph
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
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
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.
From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video Forecasting
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.
Towards an Online Empathetic Chatbot with Emotion Causes
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
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.
