The data and code of ACL 2023 Findings paper "I run as fast as a rabbit, can you? A Multilingual Simile Dialogue Dataset"
https://arxiv.org/abs/2306.05672
The simile in a dialogue scene is very different from the traditional simile in a sentence or a triplet. This paper studies the complex simile phenomena in dialogue and proposes simile dialogue data with both English and Chinese examples. The statistics are as below.
| Category | Ch | En |
|---|---|---|
| Simile | 5,515 | 3,576 |
| Literal | 5,904 | 4,570 |
| Tenor in context | 32.8% | 48.9% |
| Tenor in response | 67.2% | 51.1% |
| Vehicle before Tenor | 5.7% | 0.9% |
| Tenor before Vehicle | 94.3% | 99.1% |
| Ave. context words in simile | 20.76 | 22.22 |
| Ave. response words in simile | 18.86 | 17.83 |
The MSD-v1.0.zip contains five folders corresponding to the five tasks we defined and the data we used in the paper.
1 - Simile Recognition
2 - Simile Interpretation
3 - Simile Generation
4 - Simile Response Retrieval
5 - Simile Response Generation(Completion)
If you use the MSD data for research, please cite our paper "I run as fast as a rabbit, can you? A Multilingual Simile Dialogue Dataset"
@inproceedings{DBLP:conf/acl/MaZZSK023, author = {Longxuan Ma and Weinan Zhang and Shuhan Zhou and Churui Sun and Changxin Ke and Ting Liu}, editor = {Anna Rogers and Jordan L. Boyd{-}Graber and Naoaki Okazaki}, title = {I run as fast as a rabbit, can you? {A} Multilingual Simile Dialogues Datasets}, booktitle = {Findings of the Association for Computational Linguistics: {ACL} 2023, Toronto, Canada, July 9-14, 2023}, pages = {7223--7237}, publisher = {Association for Computational Linguistics}, year = {2023}, url = {https://doi.org/10.18653/v1/2023.findings-acl.453}, doi = {10.18653/v1/2023.findings-acl.453}, timestamp = {Wed, 23 Aug 2023 14:28:15 +0200}, biburl = {https://dblp.org/rec/conf/acl/MaZZSK023.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }