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Found 5 papers, 1 papers with code
How Much Do Modifications to Transformer Language Models Affect Their Ability to Learn Linguistic Knowledge?
Recent progress in large pretrained language models (LMs) has led to a growth of analyses examining what kinds of linguistic knowledge are encoded by these models.
Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities
We find that treating syntactic predictability independently from lexical predictability indeed results in larger estimates of garden path.
Human acceptability judgements for extractive sentence compression
Recent approaches to English-language sentence compression rely on parallel corpora consisting of sentence-compression pairs.
Evaluating Grammaticality in Seq2seq Models with a Broad Coverage HPSG Grammar: A Case Study on Machine Translation
We explore whether such output belongs to a formal and realistic grammar, by employing the English Resource Grammar (ERG), a broad coverage, linguistically precise HPSG-based grammar of English.
Evaluating Syntactic Properties of Seq2seq Output with a Broad Coverage HPSG: A Case Study on Machine Translation
We explore whether such output belongs to a formal and realistic grammar, by employing the English Resource Grammar (ERG), a broad coverage, linguistically precise HPSG-based grammar of English.
