We release TaxiNLI dataset associated with TaxiNLI: Taking a ride up the NLU Hill (CoNLL 2020 Talk). Essentially each row in this data corresponds to an NLI example in the MultiNLI dataset (can be identified using pairID and genre). We add 18 features, namely:
- Linguistic: lexical, syntactic, factivity
- Logic:
- negation, boolean, quantifier, conditional, comparative,
- relational reasoning, spatial reasoning, temporal reasoning, causal reasoning, coreference reasoning
- Knowledge: world_knowledge, taxonomic_knowledge
- For Neutral Label: general_q2, subject_q2, object_q2
Apart from that, we also have the following columns:
- Premise and hypothesis: This is the same as the MultiNLI. They are repeated for the sake of completion.
- Label: Similar as MultiNLI
- Annotated Label: This is the label as predicted by our annotators.
These 18 binary features indicate whether a certain kind of reasoning capability is required to perform the inference for that example.
taxinli10k.tsv- This is the main TaxiNLI dataset file with 10071 annotated NLI examples.taxinli10k_MNLIDev.tsv- This is the part which overlaps with MultiNLI Dev dataset. We also include our Naive-Bayes, BERT and ESIM reults in the last three columns.
| Statistics | |
|---|---|
| Total Datapoints | 10071 |
| Overlap with MNLI | 2343 (train), 7728 (dev) |
| Avg. Datapoints per Domain | 1007.1 |
| Datapoints per NLI label | 3374 (C), 3201 (N), 3494 (E) |
| Avg. Categories per example | 1.6 |
| Neutral Example Stats | 3087 (Same topic), 2843 (Same object), 877 (Same subject) |
We also provide the statistics in the following figure grouper per category and label.
For any questions about the work, please contact Somak Aditya ([email protected]).
Kindly cite the CoNLL 2020 version:
@inproceedings{joshi-etal-2020-taxinli,
title = "{T}axi{NLI}: Taking a Ride up the {NLU} Hill",
author = "Joshi, Pratik and
Aditya, Somak and
Sathe, Aalok and
Choudhury, Monojit",
booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.conll-1.4",
pages = "41--55",
abstract = "Pre-trained Transformer-based neural architectures have consistently achieved state-of-the-art performance in the Natural Language Inference (NLI) task. Since NLI examples encompass a variety of linguistic, logical, and reasoning phenomena, it remains unclear as to which specific concepts are learnt by the trained systems and where they can achieve strong generalization. To investigate this question, we propose a taxonomic hierarchy of categories that are relevant for the NLI task. We introduce TaxiNLI, a new dataset, that has 10k examples from the MNLI dataset with these taxonomic labels. Through various experiments on TaxiNLI, we observe that whereas for certain taxonomic categories SOTA neural models have achieved near perfect accuracies{---}a large jump over the previous models{---}some categories still remain difficult. Our work adds to the growing body of literature that shows the gaps in the current NLI systems and datasets through a systematic presentation and analysis of reasoning categories.",
}
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