4 citations · 6 across the 7 of their papers we have counts for
10 papers · 1 filter
Testing Pre-trained Language Models' Understanding of Distributivity via Causal Mediation Analysis
Pangbo Ban, Yifan Jiang, Tianran Liu +1
To what extent do pre-trained language models grasp semantic knowledge regarding the phenomenon of distributivity? In this paper, we introduce DistNLI, a new diagnostic dataset for…
Probing for Understanding of English Verb Classes and Alternations in Large Pre-trained Language Models
David K. Yi, James V. Bruno, Jiayu Han +2
We investigate the extent to which verb alternation classes, as described by Levin (1993), are encoded in the embeddings of Large Pre-trained Language Models (PLMs) such as BERT, R…
Language Models Use Monotonicity to Assess NPI Licensing
Jaap Jumelet, Milica Denić, Jakub Szymanik +2
We investigate the semantic knowledge of language models (LMs), focusing on (1) whether these LMs create categories of linguistic environments based on their semantic monotonicity…
A multilabel approach to morphosyntactic probing
Naomi Tachikawa Shapiro, Amandalynne Paullada, Shane Steinert-Threlkeld
We introduce a multilabel probing task to assess the morphosyntactic representations of word embeddings from multilingual language models. We demonstrate this task with multilingua…
A Masked Segmental Language Model for Unsupervised Natural Language Segmentation
C. M. Downey, Fei Xia, Gina-Anne Levow +1
Segmentation remains an important preprocessing step both in languages where "words" or other important syntactic/semantic units (like morphemes) are not clearly delineated by whit…
Linguistically-Informed Transformations (LIT): A Method for Automatically Generating Contrast Sets
Chuanrong Li, Lin Shengshuo, Leo Z. Liu +3
Although large-scale pretrained language models, such as BERT and RoBERTa, have achieved superhuman performance on in-distribution test sets, their performance suffers on out-of-di…