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Invariant Language Modeling

Invariant Language Modeling

Invariant natural language modeling

This framework adapts Invariant Risk Minimization (IRM) to natural language tasks by defining multiple training environments from available data and penalizing features whose predictive power varies across environments. It integrates with standard pretrained Transformer encoders and demonstrates improved robustness on out-of-domain NLI and sentiment benchmarks.

Natural Language
Maturity
Support
C4DT
Inactive
Lab
Unknown
  • Technical

Data Science Lab

Data Science Lab
Robert West

Prof. Robert West

Our research aims to make sense of large amounts of data. Frequently, the data we analyze is collected on the Web, e.g., using server logs, social media, wikis, online news, online games, etc. We distill heaps of raw data into meaningful insights by developing and applying algorithms and techniques in areas including social and information network analysis, machine learning, computational social science, data mining, natural language processing, and human computation.

This page was last edited on 2024-04-16.