|
--- |
|
license: afl-3.0 |
|
|
|
annotations_creators: |
|
- expert-generated |
|
language: |
|
- cn |
|
language_creators: |
|
- expert-generated |
|
multilinguality: |
|
- monolingual |
|
size_categories: |
|
- 1K<n<10K |
|
--- |
|
|
|
# Dataset Card for CANLI |
|
|
|
|
|
### Dataset Summary |
|
[CANLI: The Chinese Causative-Passive Homonymy Disambiguation: an Adversarial Dataset for NLI and a Probing Task](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.460.pdf) |
|
|
|
The disambiguation of causative-passive homonymy (CPH) is potentially tricky for machines, as the causative and the passive |
|
are not distinguished by the sentences syntactic structure. By transforming CPH disambiguation to a challenging natural |
|
language inference (NLI) task, we present the first Chinese Adversarial NLI challenge set (CANLI). We show that the pretrained |
|
transformer model RoBERTa, fine-tuned on an existing large-scale Chinese NLI benchmark dataset, performs poorly on CANLI. |
|
We also employ Word Sense Disambiguation as a probing task to investigate to what extent the CPH feature is captured in |
|
the models internal representation. We find that the models performance on CANLI does not correspond to its internal |
|
representation of CPH, which is the crucial linguistic ability central to the CANLI dataset. |
|
|
|
### Languages |
|
Chinese Mandarin |
|
|
|
# Citation Information |
|
|
|
@inproceedings{xu-markert-2022-chinese, |
|
title = "The {C}hinese Causative-Passive Homonymy Disambiguation: an adversarial Dataset for {NLI} and a Probing Task", |
|
author = "Xu, Shanshan and Markert, Katja", |
|
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", |
|
month = jun, |
|
year = "2022", |
|
address = "Marseille, France", |
|
publisher = "European Language Resources Association", |
|
url = "https://aclanthology.org/2022.lrec-1.460", |
|
pages = "4316--4323", |
|
} |
|
|
