68 citations · 96 across the 12 of their papers we have counts for
12 papers · 1 filter
Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation
Vivek Iyer, Bhavitvya Malik, Pavel Stepachev +3
Despite the recent popularity of Large Language Models (LLMs) in Machine Translation (MT), their performance in low-resource languages (LRLs) still lags significantly behind Neural…
Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation
Mateusz Klimaszewski, Piotr Andruszkiewicz, Alexandra Birch
The rise of Modular Deep Learning showcases its potential in various Natural Language Processing applications. Parameter-efficient fine-tuning (PEFT) modularity has been shown to w…
Code-Switched Language Identification is Harder Than You Think
Laurie Burchell, Alexandra Birch, Robert P. Thompson +1
Code switching (CS) is a very common phenomenon in written and spoken communication but one that is handled poorly by many natural language processing applications. Looking to the…
Prosody in Cascade and Direct Speech-to-Text Translation: a case study on Korean Wh-Phrases
Giulio Zhou, Tsz Kin Lam, Alexandra Birch +1
Speech-to-Text Translation (S2TT) has typically been addressed with cascade systems, where speech recognition systems generate a transcription that is subsequently passed to a tran…
Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets
Nikita Moghe, Arnisa Fazla, Chantal Amrhein +5
Recent machine translation (MT) metrics calibrate their effectiveness by correlating with human judgement but without any insights about their behaviour across different error type…
Code-Switching with Word Senses for Pretraining in Neural Machine Translation
Vivek Iyer, Edoardo Barba, Alexandra Birch +2
Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation (NMT), with many state-of-the-art (SOTA) NMT systems struggling to handle polysemous words…