565 citations · 899 across the 42 of their papers we have counts for
7 papers · 1 filter
A Novel Corpus of Discourse Structure in Humans and Computers
Babak Hemmatian, Sheridan Feucht, Rachel Avram +7
We present a novel corpus of 445 human- and computer-generated documents, comprising about 27,000 clauses, annotated for semantic clause types and coherence relations that allow fo…
Does Vision-and-Language Pretraining Improve Lexical Grounding?
Tian Yun, Chen Sun, Ellie Pavlick
Linguistic representations derived from text alone have been criticized for their lack of grounding, i.e., connecting words to their meanings in the physical world. Vision-and-Lang…
Frequency Effects on Syntactic Rule Learning in Transformers
Jason Wei, Dan Garrette, Tal Linzen +1
Pre-trained language models perform well on a variety of linguistic tasks that require symbolic reasoning, raising the question of whether such models implicitly represent abstract…
Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color
Mostafa Abdou, Artur Kulmizev, Daniel Hershcovich +3
Pretrained language models have been shown to encode relational information, such as the relations between entities or concepts in knowledge-bases -- (Paris, Capital, France). Howe…
Do Prompt-Based Models Really Understand the Meaning of their Prompts?
Albert Webson, Ellie Pavlick
Recently, a boom of papers has shown extraordinary progress in zero-shot and few-shot learning with various prompt-based models. It is commonly argued that prompts help models to l…
The MultiBERTs: BERT Reproductions for Robustness Analysis
Thibault Sellam, Steve Yadlowsky, Jason Wei +9
Experiments with pre-trained models such as BERT are often based on a single checkpoint. While the conclusions drawn apply to the artifact tested in the experiment (i.e., the parti…