Update README.md

README.md

@@ -1,10 +1,33 @@
 ---
 license: afl-3.0
+
+annotations_creators:
+- expert-generated
+language:
+- cn
+language_creators:
+- expert-generated
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
 ---
-# Dataset Summary 
+
+# 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)
 
-# Languages
+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