metadata
license: cc-by-4.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: string
- name: config
dtype: string
splits:
- name: train
num_bytes: 4691316
num_examples: 25232
- name: validation
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num_examples: 4624
- name: test
num_bytes: 1224540
num_examples: 7216
download_size: 956275
dataset_size: 6717734
https://github.com/ruixiangcui/WikiResNLI_NatResNLI
@inproceedings{cui-etal-2023-failure,
title = "What does the Failure to Reason with {``}Respectively{''} in Zero/Few-Shot Settings Tell Us about Language Models?",
author = "Cui, Ruixiang and
Lee, Seolhwa and
Hershcovich, Daniel and
S{\o}gaard, Anders",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.489",
pages = "8786--8800",
abstract = "Humans can effortlessly understand the coordinate structure of sentences such as {``}Niels Bohr and Kurt Cobain were born in Copenhagen and Seattle, *respectively*{''}. In the context of natural language inference (NLI), we examine how language models (LMs) reason with respective readings (Gawron and Kehler, 2004) from two perspectives: syntactic-semantic and commonsense-world knowledge. We propose a controlled synthetic dataset WikiResNLI and a naturally occurring dataset NatResNLI to encompass various explicit and implicit realizations of {``}respectively{''}. We show that fine-tuned NLI models struggle with understanding such readings without explicit supervision. While few-shot learning is easy in the presence of explicit cues, longer training is required when the reading is evoked implicitly, leaving models to rely on common sense inferences. Furthermore, our fine-grained analysis indicates models fail to generalize across different constructions. To conclude, we demonstrate that LMs still lag behind humans in generalizing to the long tail of linguistic constructions.",
}