565 citations · 591 across the 8 of their papers we have counts for
8 papers
Generating Gender Alternatives in Machine Translation
Sarthak Garg, Mozhdeh Gheini, Clara Emmanuel +3
Machine translation (MT) systems often translate terms with ambiguous gender (e.g., English term "the nurse") into the gendered form that is most prevalent in the systems' training…
Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data
Mozhdeh Gheini, Tatiana Likhomanenko, Matthias Sperber +1
Self-training has been shown to be helpful in addressing data scarcity for many domains, including vision, speech, and language. Specifically, self-training, or pseudo-labeling, la…
Checks and Strategies for Enabling Code-Switched Machine Translation
Thamme Gowda, Mozhdeh Gheini, Jonathan May
Code-switching is a common phenomenon among multilingual speakers, where alternation between two or more languages occurs within the context of a single conversation. While multili…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning
Mozhdeh Gheini, Xuezhe Ma, Jonathan May
A recent family of techniques, dubbed lightweight fine-tuning methods, facilitates parameter-efficient transfer learning by updating only a small set of additional parameters while…
Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation
Mozhdeh Gheini, Xiang Ren, Jonathan May
We study the power of cross-attention in the Transformer architecture within the context of transfer learning for machine translation, and extend the findings of studies into cross…
ParsiNLU: A Suite of Language Understanding Challenges for Persian
Daniel Khashabi, Arman Cohan, Siamak Shakeri +22
Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich l…
A Universal Parent Model for Low-Resource Neural Machine Translation Transfer
Mozhdeh Gheini, Jonathan May
Transfer learning from a high-resource language pair `parent' has been proven to be an effective way to improve neural machine translation quality for low-resource language pairs `…