Papers Conversational Response Generation
“Conversational Response Generation” 태그가 달린 논문 22편 · 필터 해제
OmniResponse: Online Multimodal Conversational Response Generation in Dyadic Interactions
In this paper, we introduce Online Multimodal Conversational Response Generation (OMCRG), a novel task that aims to online generate synchronized verbal and non-verbal listener feedback, conditioned on the speaker's multi…
Audio-Visual SynchronizationConversational Response GenerationLarge Language ModelMultimodal Large Language Model+1Grounded and Transparent Response Generation for Conversational Information-Seeking Systems
While previous conversational information-seeking (CIS) research has focused on passage retrieval, reranking, and query rewriting, the challenge of synthesizing retrieved information into coherent responses remains. The …
Conversational Response GenerationPassage RetrievalRerankingResponse Generation+1Explainability for Transparent Conversational Information-Seeking
The increasing reliance on digital information necessitates advancements in conversational search systems, particularly in terms of information transparency. While prior research in conversational information-seeking has…
Conversational Response GenerationConversational SearchTowards Filling the Gap in Conversational Search: From Passage Retrieval to Conversational Response Generation
Research on conversational search has so far mostly focused on query rewriting and multi-stage passage retrieval. However, synthesizing the top retrieved passages into a complete, relevant, and concise response is still …
Conversational Response GenerationConversational SearchPassage RetrievalResponse Generation+1PLACES: Prompting Language Models for Social Conversation Synthesis
Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns. A promising direction to tackle this problem is to generate …
Conversational Response GenerationDialoGen: Generalized Long-Range Context Representation for Dialogue Systems
Long-range context modeling is crucial to both dialogue understanding and generation. The most popular method for dialogue context representation is to concatenate the last-$k$ utterances in chronological order. However,…
Conversational Response GenerationDecoderDialogue GenerationDialogue Understanding+1CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI
Human language expression is based on the subjective construal of the situation instead of the objective truth conditions, which means that speakers' personalities and emotions after cognitive processing have an importan…
Chinese Sentiment AnalysisConversational Response GenerationDialog Act ClassificationDialogue Generation+7Using Synthetic Data for Conversational Response Generation in Low-resource Settings
Response generation is a task in natural language processing (NLP) where a model is trained to respond to human statements. Conversational response generators take this one step further with the ability to respond within…
Conversational Response GenerationData AugmentationResponse GenerationRedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models
Text representation models are prone to exhibit a range of societal biases, reflecting the non-controlled and biased nature of the underlying pretraining data, which consequently leads to severe ethical issues and even b…
Conversational Response GenerationResponse GenerationLanguage Model as an Annotator: Exploring DialoGPT for Dialogue Summarization
Current dialogue summarization systems usually encode the text with a number of general semantic features (e.g., keywords and topics) to gain more powerful dialogue modeling capabilities. However, these features are obta…
Conversational Response GenerationLanguage ModelingLanguage ModellingResponse GenerationDialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances
Recent advances in pre-trained language models have significantly improved neural response generation. However, existing methods usually view the dialogue context as a linear sequence of tokens and learn to generate the …
Conversational Response GenerationResponse GenerationPEDNet: A Persona Enhanced Dual Alternating Learning Network for Conversational Response Generation
Endowing a chatbot with a personality is essential to deliver more realistic conversations. Various persona-based dialogue models have been proposed to generate personalized and diverse responses by utilizing predefined …
ChatbotConversational Response GenerationDecoderResponse GenerationPALM: Pre-training an Autoencoding\&Autoregressive Language Model for Context-conditioned Generation
Self-supervised pre-training, such as BERT, MASS and BART, has emerged as a powerful technique for natural language understanding and generation. Existing pre-training techniques employ autoencoding and/or autoregressive…
Abstractive Text SummarizationConversational Response GenerationDenoisingGenerative Question Answering+8DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation
We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a…
Conversational Response GenerationResponse GenerationConversations with Search Engines: SERP-based Conversational Response Generation
In this paper, we address the problem of answering complex information needs by conversing conversations with search engines, in the sense that users can express their queries in natural language, and directly receivethe…
Conversational Response GenerationConversational SearchResponse GenerationPALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
Self-supervised pre-training, such as BERT, MASS and BART, has emerged as a powerful technique for natural language understanding and generation. Existing pre-training techniques employ autoencoding and/or autoregressive…
Abstractive Text SummarizationConversational Response GenerationDenoisingGenerative Question Answering+9DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a…
Conversational Response GenerationResponse GenerationLearning to Abstract for Memory-augmented Conversational Response Generation
Neural generative models for open-domain chit-chat conversations have become an active area of research in recent years. A critical issue with most existing generative models is that the generated responses lack informat…
Conversational Response GenerationDiversityInformativenessResponse Generation+1MASS: Masked Sequence to Sequence Pre-training for Language Generation
Pre-training and fine-tuning, e.g., BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource downstream tasks. Inspired by the su…
Conversational Response GenerationDecoderLanguage ModelingMachine Translation+6Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
Responses generated by neural conversational models tend to lack informativeness and diversity. We present Adversarial Information Maximization (AIM), an adversarial learning strategy that addresses these two related but…
Conversational Response GenerationDiversityInformativeness