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RuCoLA: Russian Corpus of Linguistic Acceptability

2022-10-23Code Available1· sign in to hype

Vladislav Mikhailov, Tatiana Shamardina, Max Ryabinin, Alena Pestova, Ivan Smurov, Ekaterina Artemova

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Abstract

Linguistic acceptability (LA) attracts the attention of the research community due to its many uses, such as testing the grammatical knowledge of language models and filtering implausible texts with acceptability classifiers. However, the application scope of LA in languages other than English is limited due to the lack of high-quality resources. To this end, we introduce the Russian Corpus of Linguistic Acceptability (RuCoLA), built from the ground up under the well-established binary LA approach. RuCoLA consists of 9.8k in-domain sentences from linguistic publications and 3.6k out-of-domain sentences produced by generative models. The out-of-domain set is created to facilitate the practical use of acceptability for improving language generation. Our paper describes the data collection protocol and presents a fine-grained analysis of acceptability classification experiments with a range of baseline approaches. In particular, we demonstrate that the most widely used language models still fall behind humans by a large margin, especially when detecting morphological and semantic errors. We release RuCoLA, the code of experiments, and a public leaderboard (rucola-benchmark.com) to assess the linguistic competence of language models for Russian.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
CoLARemBERTMCC0.6Unverified
ItaCoLAmBERTMCC0.36Unverified
ItaCoLAXLM-RMCC0.52Unverified
RuCoLAruBERTMCC0.42Unverified
RuCoLAruGPT-3MCC0.3Unverified
RuCoLAruT5MCC0.25Unverified
RuCoLAmBERTMCC0.15Unverified
RuCoLAXLM-RMCC0.13Unverified
RuCoLAruRoBERTaMCC0.53Unverified
RuCoLARemBERTMCC0.44Unverified

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