Open-Domain Dialog Edit Task
Natural Language Processing
About
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Datasets
Greatest papers with code
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.
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.
Hierarchical Reinforcement Learning for Open-Domain Dialog
• natashamjaques/neural_chat
• 
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.
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
• natashamjaques/neural_chat
•
Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment.
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems
NeurIPS 2019
• natashamjaques/neural_chat
•
To investigate the strengths of this novel metric and interactive evaluation in comparison to state-of-the-art metrics and human evaluation of static conversations, we perform extended experiments with a set of models, including several that make novel improvements to recent hierarchical dialog generation architectures through sentiment and semantic knowledge distillation on the utterance level.
USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation
ACL 2020
• shikib/usr
•
The lack of meaningful automatic evaluation metrics for dialog has impeded open-domain dialog research.
Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References
The aim of this paper is to mitigate the shortcomings of automatic evaluation of open-domain dialog systems through multi-reference evaluation.
Probing Neural Dialog Models for Conversational Understanding
WS 2020
• AbdulSaleh/dialog-probing
•
The predominant approach to open-domain dialog generation relies on end-to-end training of neural models on chat datasets.
Unsupervised Evaluation of Interactive Dialog with DialoGPT
• shikib/fed
•
It is important to define meaningful and interpretable automatic evaluation metrics for open-domain dialog research.
Large-Scale Transfer Learning for Natural Language Generation
ACL 2019
• atselousov/transformer_chatbot_experiments
•
Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks.