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Found 8 papers, 2 papers with code
Human Perception in Natural Language Generation
We ask subjects whether they perceive as human-produced a bunch of texts, some of which are actually human-written, while others are automatically generated.
SemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic Knowledge
We report the results of the SemEval 2022 Task 3, PreTENS, on evaluation the acceptability of simple sentences containing constructions whose two arguments are presupposed to be or not to be in an ordered taxonomic relation.
That Looks Hard: Characterizing Linguistic Complexity in Humans and Language Models
We then show the effectiveness of linguistic features when explicitly leveraged by a regression model for predicting sentence complexity and compare its results with the ones obtained by a fine-tuned neural language model.
Sentence Complexity in Context
We study the influence of context on how humans evaluate the complexity of a sentence in English.
What Makes My Model Perplexed? A Linguistic Investigation on Neural Language Models Perplexity
This paper presents an investigation aimed at studying how the linguistic structure of a sentence affects the perplexity of two of the most popular Neural Language Models (NLMs), BERT and GPT-2.
How Do BERT Embeddings Organize Linguistic Knowledge?
Several studies investigated the linguistic information implicitly encoded in Neural Language Models.
On the Nature of BERT: Correlating Fine-Tuning and Linguistic Competence
Several studies in the literature on the interpretation of Neural Language Models (NLM) focus on the linguistic generalization abilities of pre-trained models.
How about Time? Probing a Multilingual Language Model for Temporal Relations
This paper presents a comprehensive set of probing experiments using a multilingual language model, XLM-R, for temporal relation classification between events in four languages.
