Targeted Syntactic Evaluation of Language Models. Marvin, R. & Linzen, T. August, 2018. arXiv:1808.09031 [cs]![Targeted Syntactic Evaluation of Language Models [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex We present a dataset for evaluating the grammaticality of the predictions of a language model. We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence. The sentence pairs represent different variations of structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and negative polarity items. We expect a language model to assign a higher probability to the grammatical sentence than the ungrammatical one. In an experiment using this data set, an LSTM language model performed poorly on many of the constructions. Multi-task training with a syntactic objective (CCG supertagging) improved the LSTM's accuracy, but a large gap remained between its performance and the accuracy of human participants recruited online. This suggests that there is considerable room for improvement over LSTMs in capturing syntax in a language model.
@misc{marvin_targeted_2018,
title = {Targeted {Syntactic} {Evaluation} of {Language} {Models}},
url = {http://arxiv.org/abs/1808.09031},
doi = {10.48550/arXiv.1808.09031},
abstract = {We present a dataset for evaluating the grammaticality of the predictions of a language model. We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence. The sentence pairs represent different variations of structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and negative polarity items. We expect a language model to assign a higher probability to the grammatical sentence than the ungrammatical one. In an experiment using this data set, an LSTM language model performed poorly on many of the constructions. Multi-task training with a syntactic objective (CCG supertagging) improved the LSTM's accuracy, but a large gap remained between its performance and the accuracy of human participants recruited online. This suggests that there is considerable room for improvement over LSTMs in capturing syntax in a language model.},
urldate = {2022-09-02},
publisher = {arXiv},
author = {Marvin, Rebecca and Linzen, Tal},
month = aug,
year = {2018},
note = {arXiv:1808.09031 [cs]},
keywords = {Computer Science - Computation and Language},
}
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