| license: mit | |
| task_categories: | |
| - text-classification | |
| language: | |
| - en | |
| pretty_name: Dialogue NLI | |
| size_categories: | |
| - 1M<n<10M | |
| This dataset serves as a convenient way to access the ParlAI dialogue NLI dataset. I do not own the rights and all credit go to the original authors. | |
| # Dialogue Natural Language Inference | |
| Sean Welleck, Jason Weston, Arthur Szlam, Kyunghyun Cho | |
| arxiv link: https://arxiv.org/abs/1811.00671 | |
| Abstract: Consistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natural language inference dataset called Dialogue NLI. We propose a method which demonstrates that a model trained on Dialogue NLI can be used to improve the consistency of a dialogue model, and evaluate the method with human evaluation and with automatic metrics on a suite of evaluation sets designed to measure a dialogue model’s consistency. | |
| ## How to use | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset('xksteven/dialogue_nli', split='train') | |
| ``` | |
| label candidates: | |
| - entailment | |
| - contradiction | |
| - neutral | |
| Train dataset features. | |
| ``` | |
| Dataset({ | |
| features: ['id', 'label', 'premise', 'hypothesis', 'dtype'], | |
| num_rows: 310110 | |
| }) | |
| ``` | |
| ### Citation | |
| ``` | |
| @misc{welleck2019dialogue, | |
| title={Dialogue Natural Language Inference}, | |
| author={Sean Welleck and Jason Weston and Arthur Szlam and Kyunghyun Cho}, | |
| year={2019}, | |
| eprint={1811.00671}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL} | |
| } | |
| ``` | |