62 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of CambridgeGB6 papers
- Tallinn University of TechnologyEE5 papers
- University of EdinburghGB5 papers
- University of AmsterdamNL4 papers
- Carnegie Mellon UniversityUS2 papers
- Tsinghua UniversityCN2 papers
- University of ChicagoUS2 papers
- Alibaba Group (United States)US1 paper
- Allen InstituteUS1 paper
- Amazon (United States)US1 paper
- Argonne National LaboratoryUS1 paper
- Artificial Intelligence in Medicine (Canada)CA1 paper
5 papers · 2 filters
Are Pretrained Multilingual Models Equally Fair Across Languages?
Laura Cabello Piqueras, Anders Søgaard
Pretrained multilingual language models can help bridge the digital language divide, enabling high-quality NLP models for lower resourced languages. Studies of multilingual models…
Improving Bilingual Lexicon Induction with Cross-Encoder Reranking
Yaoyiran Li, Fangyu Liu, Ivan Vulić +1
Bilingual lexicon induction (BLI) with limited bilingual supervision is a crucial yet challenging task in multilingual NLP. Current state-of-the-art BLI methods rely on the inducti…
Machine Reading, Fast and Slow: When Do Models "Understand" Language?
Sagnik Ray Choudhury, Anna Rogers, Isabelle Augenstein
Two of the most fundamental challenges in Natural Language Understanding (NLU) at present are: (a) how to establish whether deep learning-based models score highly on NLU benchmark…
Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models
Clara Na, Sanket Vaibhav Mehta, Emma Strubell
Model compression by way of parameter pruning, quantization, or distillation has recently gained popularity as an approach for reducing the computational requirements of modern dee…
Improving Word Translation via Two-Stage Contrastive Learning
Yaoyiran Li, Fangyu Liu, Nigel Collier +2
Word translation or bilingual lexicon induction (BLI) is a key cross-lingual task, aiming to bridge the lexical gap between different languages. In this work, we propose a robust a…