Open-Domain Dialog
32 papers with code • 1 benchmarks • 11 datasets
Datasets
Most implemented papers
Large-Scale Transfer Learning for Natural Language Generation
Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks.
A Multi-Turn Emotionally Engaging Dialog Model
Open-domain dialog systems (also known as chatbots) have increasingly drawn attention in natural language processing.
Hierarchical Reinforcement Learning for Open-Domain Dialog
Open-domain dialog generation is a challenging problem; maximum likelihood training can lead to repetitive outputs, models have difficulty tracking long-term conversational goals, and training on standard movie or online datasets may lead to the generation of inappropriate, biased, or offensive text.
ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers
Automatic speech recognition (ASR) via call is essential for various applications, including AI for contact center (AICC) services.
USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation
The lack of meaningful automatic evaluation metrics for dialog has impeded open-domain dialog research.
Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation
Our experiments show that CMADE achieves 89. 2% accuracy in the dialog comparison task.
Probing Neural Dialog Models for Conversational Understanding
The predominant approach to open-domain dialog generation relies on end-to-end training of neural models on chat datasets.
Are Neural Open-Domain Dialog Systems Robust to Speech Recognition Errors in the Dialog History? An Empirical Study
Large end-to-end neural open-domain chatbots are becoming increasingly popular.
ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation
ProphetNet is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks.
CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation
However, existing methods for empathetic response generation usually either consider only one empathy factor or ignore the hierarchical relationships between different factors, leading to a weak ability of empathy modeling.