Awesome

The E2E Challenge Dataset

Authors: Jekaterina Novikova, Ondrej Dusek and Verena Rieser

Download Link

Download the full release of the E2E dataset here (ZIP)

Description

The E2E dataset is a new dataset for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area.

The E2E dataset poses new challenges:

1. its human reference texts show more lexical richness and syntactic variation, including discourse phenomena;
2. generating from this set requires content selection.

As such, learning from this dataset promises more natural, varied and less template-like system utterances.

The E2E set was used in the E2E NLG Challenge, which provides an extensive list of results achieved on this data.

Please refer to our SIGDIAL2017 paper for a detailed description of the dataset.

Contents

Files

• trainset.csv – the training set
• devset.csv – the development set
• testset.csv – the challenge test set (meaning representations only)
• testset_w_refs.csv – evaluation test set with reference natural language utterances

CSV Data Fields

• mr – textual meaning representation (MR)
• ref – corresponding natural language utterance (human reference)

Note that several human references correspond to a single MR, i.e., multiple lines contain the same MR.

Citing

If you use this dataset in your work, please cite the following paper:

@inproceedings{novikova2017e2e,
  title={The {E2E} Dataset: New Challenges for End-to-End Generation},
  author={Novikova, Jekaterina and Du{\v{s}}ek, Ondrej and Rieser, Verena},
  booktitle={Proceedings of the 18th Annual Meeting of the Special Interest 
             Group on Discourse and Dialogue},
  address={Saarbr\"ucken, Germany},
  year={2017},
  note={arXiv:1706.09254},
  url={https://arxiv.org/abs/1706.09254},
}

License

Distributed under the Creative Commons 4.0 Attribution-ShareAlike license (CC4.0-BY-SA).

Acknowledgements

This research received funding from the EPSRC projects DILiGENt (EP/M005429/1) and MaDrIgAL (EP/N017536/1).