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Found 36 papers, 19 papers with code
Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement
In an open-domain dialogue system, the consistent persona is a key factor to generate real and coherent dialogues.
KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features
Moreover, to verify the generality of the model, we also conduct experiments on two common sentiment analysis datasets.
Pixel-Level Reasoning Segmentation via Multi-turn Conversations
To establish a benchmark for this novel task, we build a Pixel-level ReasonIng Segmentation Dataset Based on Multi-Turn Conversations (PRIST), comprising 24k utterances from 8. 3k multi-turn conversational scenarios with segmentation targets.
Language Models as Continuous Self-Evolving Data Engineers
Large Language Models (LLMs) have demonstrated remarkable capabilities on various tasks, while the further evolvement is limited to the lack of high-quality training data.
PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in Left-Behind Children
Left-behind children (LBCs), numbering over 66 million in China, face severe mental health challenges due to parental migration for work.
TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM
We validate EKTC on the ED dataset, and the experimental results demonstrate that our framework can enhance the ability of LLMs to generate empathetic responses effectively.
Generative Emotion Cause Explanation in Multimodal Conversations
Multimodal conversation, a crucial form of human communication, carries rich emotional content, making the exploration of the causes of emotions within it a research endeavor of significant importance.
Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts
LLMs have demonstrated commendable performance across diverse domains.
Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering
Multi-hop Question Answering (QA) necessitates complex reasoning by integrating multiple pieces of information to resolve intricate questions.
ChatZero:Zero-shot Cross-Lingual Dialogue Generation via Pseudo-Target Language
Although large language models(LLMs) show amazing capabilities, among various exciting applications discovered for LLMs fall short in other low-resource languages.
Can LLMs Beat Humans in Debating? A Dynamic Multi-agent Framework for Competitive Debate
Competitive debate is a complex task of computational argumentation.
Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective
The emergence of Large Language Models (LLMs), such as the ChatGPT series and LLaMA models, brings new opportunities and challenges, catalyzing a paradigm shift in AC.
A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation
We conduct experiments on Chinese dialogue datasets from five different domains and show that AMD$^2$G achieves superior performance compared to both direct training on the target domain corpus and collective training on all five domain corpora.
Is Mamba Effective for Time Series Forecasting?
For TSF tasks, these characteristics enable Mamba to comprehend hidden patterns as the Transformer and reduce computational overhead compared to the Transformer.
Ranked #60 on
Time Series Forecasting
on ETTh1 (336) Multivariate
LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language
Experiments illustrate that LangGPT significantly enhances the performance of LLMs.
HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy
As LMs grow in size, fine-tuning the full parameters of LMs requires a prohibitively large amount of GPU memory.
STICKERCONV: Generating Multimodal Empathetic Responses from Scratch
Stickers, while widely recognized for enhancing empathetic communication in online interactions, remain underexplored in current empathetic dialogue research, notably due to the challenge of a lack of comprehensive datasets.
MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks
Consequently, our work complements research on the performance of MLLMs in multimodal comprehension tasks, achieving a more comprehensive and holistic evaluation of MLLMs.
T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems
The application of CEs encounters two main challenges: general user preferences and variable ML systems.
RoCar: A Relationship Network-based Evaluation Method for Large Language Models
Large language models (LLMs) have received increasing attention.
Evaluate What You Can't Evaluate: Unassessable Quality for Generated Response
There are risks in using eference-free evaluators based on LLMs to evaluate the quality of dialogue responses.
PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism
We investigate response generation for multi-turn dialogue in generative-based chatbots.
Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts
To tackle this problem, we propose a novel method called Multimodal Probabilistic Fusion Prompts (MultiPoint) that leverages diverse cues from different modalities for multimodal sentiment detection in the few-shot scenario.
Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning
Sparsity of formal knowledge and roughness of non-ontological construction make sparsity problem particularly prominent in Open Knowledge Graphs (OpenKGs).
DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection
Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms.
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation
Building dialogue generation systems in a zero-shot scenario remains a huge challenge, since the typical zero-shot approaches in dialogue generation rely heavily on large-scale pre-trained language generation models such as GPT-3 and T5.
Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks
In this paper, we propose Multi-channel Graph Neural Networks with Sentiment-awareness (MGNNS) for image-text sentiment detection.
A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training
We investigate response selection for multi-turn conversation in retrieval-based chatbots.
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension
Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset.
Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation
We observe that the answer has strong semantic coherence to its question and post, which can be used to guide question generation.
Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning
Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem.
A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness
Emotion cause analysis has been a key topic in natural language processing.
