Papers Open-Domain Dialog
“Open-Domain Dialog” 태그가 달린 논문 60편 · 필터 해제
An Empirical Study on Context Length for Open-Domain Dialog Generation
Transformer-based open-domain dialog models have become increasingly popular in recent years. These models typically represent context as a concatenation of a dialog history. However, there is no criterion to decide how …
Open-Domain DialogSocial Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations
Open-domain dialog involves generating search queries that help obtain relevant knowledge for holding informative conversations. However, it can be challenging to determine what information to retrieve when the user is p…
Open-Domain DialogResponse GenerationDior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog Generation
Current variational dialog models have employed pre-trained language models (PLMs) to parameterize the likelihood and posterior distributions. However, the Gaussian assumption made on the prior distribution is incompatib…
DecoderDiversityOpen-Domain DialogResponse GenerationEnhancing Task Bot Engagement with Synthesized Open-Domain Dialog
Many efforts have been made to construct dialog systems for different types of conversations, such as task-oriented dialog (TOD) and open-domain dialog (ODD). To better mimic human-level conversations that usually fuse v…
Open-Domain DialogOpen-Domain Dialog Evaluation using Follow-Ups Likelihood
Automatic evaluation of open-domain dialogs remains an unsolved problem. Moreover, existing methods do not correlate strongly with human annotations. This paper presents a new automated evaluation method using follow-ups…
Language ModelingLanguage ModellingOpen-Domain DialogInteractive Evaluation of Dialog Track at DSTC9
The ultimate goal of dialog research is to develop systems that can be effectively used in interactive settings by real users. To this end, we introduced the Interactive Evaluation of Dialog Track at the 9th Dialog Syste…
Interactive Evaluation of DialogOpen-Domain DialogResponse GenerationRe2G: Retrieve, Rerank, Generate
As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dram…
Fact CheckingFact VerificationGPUKnowledge Distillation+8Uncovering Surprising Event Boundaries in Narratives
It is important to define meaningful and interpretable automatic evaluation metrics for open-domain dialog research. Standard language generation metrics have been shown to be ineffective for dialog. This paper introduce…
Open-Domain DialogText GenerationGODEL: Large-Scale Pre-Training for Goal-Directed Dialog
We introduce GODEL (Grounded Open Dialogue Language Model), a large pre-trained language model for dialog. In contrast with earlier models such as DialoGPT, GODEL leverages a new phase of grounded pre-training designed t…
Language ModelingLanguage ModellingOpen-Domain DialogCPED: 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+7InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning
Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Instructions have been shown to enable good perf…
Dialogue EvaluationDialogue GenerationIntent DetectionNatural Language Understanding+2Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk
The capability of holding social talk (or casual conversation) and making sense of conversational content requires context-sensitive natural language understanding and reasoning, which cannot be handled efficiently by th…
ChunkingNatural Language UnderstandingOpen-Domain DialogPart-Of-Speech TaggingWhat is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation
Accurate automatic evaluation metrics for open-domain dialogs are in high demand. Existing model-based metrics for system response evaluation are trained on human annotated data, which is cumbersome to collect. In this w…
Dialogue EvaluationOpen-Domain DialogTowards Identifying Social Bias in Dialog Systems: Frame, Datasets, and Benchmarks
The research of open-domain dialog systems has been greatly prospered by neural models trained on large-scale corpora, however, such corpora often introduce various safety problems (e.g., offensive languages, biases, and…
Bias DetectionOpen-Domain DialogTowards Learning Through Open-Domain Dialog
The development of artificial agents able to learn through dialog without domain restrictions has the potential to allow machines to learn how to perform tasks in a similar manner to humans and change how we relate to th…
Open-Domain DialogAn Empirical Study of Topic Transition in Dialogue
Transitioning between topics is a natural component of human-human dialog. Although topic transition has been studied in dialogue for decades, only a handful of corpora based studies have been performed to investigate th…
Open-Domain DialogUser Response and Sentiment Prediction for Automatic Dialogue Evaluation
Automatic evaluation is beneficial for open-domain dialog system development. However, standard word-overlap metrics (BLEU, ROUGE) do not correlate well with human judgements of open-domain dialog systems. In this work w…
Dialogue EvaluationOpen-Domain DialogInvestigating the Impact of Pre-trained Language Models on Dialog Evaluation
Recently, there is a surge of interest in applying pre-trained language models (Pr-LM) in automatic open-domain dialog evaluation. Pr-LMs offer a promising direction for addressing the multi-domain evaluation challenge. …
Dialogue EvaluationOpen-Domain DialogInvestigating Robustness of Dialog Models to Popular Figurative Language Constructs
Humans often employ figurative language use in communication, including during interactions with dialog systems. Thus, it is important for real-world dialog systems to be able to handle popular figurative language constr…
Open-Domain DialogEnhancing Self-Disclosure In Neural Dialog Models By Candidate Re-ranking
Neural language modelling has progressed the state-of-the-art in different downstream Natural Language Processing (NLP) tasks. One such area is of open-domain dialog modelling, neural dialog models based on GPT-2 such as…
Language ModellingOpen-Domain DialogRe-Ranking