2 citations · 4 across the 9 of their papers we have counts for
13 papers · 1 filter
Word Boundary Information Isn't Useful for Encoder Language Models
Edward Gow-Smith, Dylan Phelps, Harish Tayyar Madabushi +2
All existing transformer-based approaches to NLP using subword tokenisation algorithms encode whitespace (word boundary information) through the use of special space symbols (such…
Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information
Kun Zhao, Bohao Yang, Chenghua Lin +3
The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which…
Assessing Linguistic Generalisation in Language Models: A Dataset for Brazilian Portuguese
Rodrigo Wilkens, Leonardo Zilio, Aline Villavicencio
Much recent effort has been devoted to creating large-scale language models. Nowadays, the most prominent approaches are based on deep neural networks, such as BERT. However, they…
Effective Cross-Task Transfer Learning for Explainable Natural Language Inference with T5
Irina Bigoulaeva, Rachneet Sachdeva, Harish Tayyar Madabushi +2
We compare sequential fine-tuning with a model for multi-task learning in the context where we are interested in boosting performance on two tasks, one of which depends on the othe…
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding
Harish Tayyar Madabushi, Edward Gow-Smith, Marcos Garcia +3
This paper presents the shared task on Multilingual Idiomaticity Detection and Sentence Embedding, which consists of two subtasks: (a) a binary classification task aimed at identif…
Sample Efficient Approaches for Idiomaticity Detection
Dylan Phelps, Xuan-Rui Fan, Edward Gow-Smith +3
Deep neural models, in particular Transformer-based pre-trained language models, require a significant amount of data to train. This need for data tends to lead to problems when de…