139 citations · 373 across the 15 of their papers we have counts for
6 papers · 1 filter
Do Language Embeddings Capture Scales?
Xikun Zhang, Deepak Ramachandran, Ian Tenney +2
Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense, and factual knowledge. One form of knowledge that has not been studied yet in this…
Measuring and Reducing Gendered Correlations in Pre-trained Models
Kellie Webster, Xuezhi Wang, Ian Tenney +6
Pre-trained models have revolutionized natural language understanding. However, researchers have found they can encode artifacts undesired in many applications, such as professions…
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models
Ian Tenney, James Wexler, Jasmijn Bastings +8
We present the Language Interpretability Tool (LIT), an open-source platform for visualization and understanding of NLP models. We focus on core questions about model behavior: Why…
What Happens To BERT Embeddings During Fine-tuning?
Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick +1
While there has been much recent work studying how linguistic information is encoded in pre-trained sentence representations, comparatively little is understood about how these mod…
Asking without Telling: Exploring Latent Ontologies in Contextual Representations
Julian Michael, Jan A. Botha, Ian Tenney
The success of pretrained contextual encoders, such as ELMo and BERT, has brought a great deal of interest in what these models learn: do they, without explicit supervision, learn…
jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models
Yada Pruksachatkun, Phil Yeres, Haokun Liu +5
We introduce jiant, an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks. jiant enables modular and configuration-driven experimen…