34 citations · 35 across the 4 of their papers we have counts for
8 papers
Word Order Does Matter (And Shuffled Language Models Know It)
Vinit Ravishankar, Mostafa Abdou, Artur Kulmizev +1
Recent studies have shown that language models pretrained and/or fine-tuned on randomly permuted sentences exhibit competitive performance on GLUE, putting into question the import…
The Impact of Positional Encodings on Multilingual Compression
Vinit Ravishankar, Anders Søgaard
In order to preserve word-order information in a non-autoregressive setting, transformer architectures tend to include positional knowledge, by (for instance) adding positional enc…
Attention Can Reflect Syntactic Structure (If You Let It)
Vinit Ravishankar, Artur Kulmizev, Mostafa Abdou +2
Since the popularization of the Transformer as a general-purpose feature encoder for NLP, many studies have attempted to decode linguistic structure from its novel multi-head atten…
The Sensitivity of Language Models and Humans to Winograd Schema Perturbations
Mostafa Abdou, Vinit Ravishankar, Maria Barrett +3
Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of common sens…
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers
Anne Lauscher, Vinit Ravishankar, Ivan Vulić +1
Massively multilingual transformers pretrained with language modeling objectives (e.g., mBERT, XLM-R) have become a de facto default transfer paradigm for zero-shot cross-lingual t…
Do Neural Language Models Show Preferences for Syntactic Formalisms?
Artur Kulmizev, Vinit Ravishankar, Mostafa Abdou +1
Recent work on the interpretability of deep neural language models has concluded that many properties of natural language syntax are encoded in their representational spaces. Howev…