A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt


Negation is one of the most fundamental concepts in human cognition and language, and several natural language inference (NLI) probes have been designed to investigate pretrained language models’ ability to detect and reason with negation. However, the existing probing datasets are limited to English only, and do not enable controlled probing of performance in the absence or presence of negation. In response, we present a multilingual (English, Bulgarian, German, French and Chinese) benchmark collection of NLI examples that are grammatical and correctly labeled, as a result of manual inspection and reformulation. We use the benchmark to probe the negation-awareness of multilingual language models and find that models that correctly predict examples with negation cues, often fail to correctly predict their counter-examples without negation cues, even when the cues are irrelevant for semantic inference.
TitelProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
ForlagAssociation for Computational Linguistics
StatusUdgivet - 2021
Begivenhed2021 Conference on Empirical Methods in Natural Language Processing -
Varighed: 7 nov. 202111 nov. 2021


Konference2021 Conference on Empirical Methods in Natural Language Processing

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