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Conversational Response Generation
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Given an input conversation, generate a natural-looking text reply to the last conversation element.
Image credit: DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization
Grounded and Transparent Response Generation for Conversational Information-Seeking Systems
Explainability for Transparent Conversational Information-Seeking
Towards Filling the Gap in Conversational Search: From Passage Retrieval to Conversational Response Generation
PLACES: Prompting Language Models for Social Conversation Synthesis
DialoGen: Generalized Long-Range Context Representation for Dialogue Systems
CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI
Using Synthetic Data for Conversational Response Generation in Low-resource Settings
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models
Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization
DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances
Conversations with Search Engines: SERP-based Conversational Response Generation
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
MASS: Masked Sequence to Sequence Pre-training for Language Generation
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
A Diversity-Promoting Objective Function for Neural Conversation Models
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