5 citations · 10 across the 7 of their papers we have counts for
8 papers
Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization
Zilu Tang, Rajen Chatterjee, Sarthak Garg
Machine Translation (MT) is undergoing a paradigm shift, with systems based on fine-tuned large language models (LLM) becoming increasingly competitive with traditional encoder-dec…
Speech is More Than Words: Do Speech-to-Text Translation Systems Leverage Prosody?
Ioannis Tsiamas, Matthias Sperber, Andrew Finch +1
The prosody of a spoken utterance, including features like stress, intonation and rhythm, can significantly affect the underlying semantics, and as a consequence can also affect it…
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…
Efficient Inference For Neural Machine Translation
Yi-Te Hsu, Sarthak Garg, Yi-Hsiu Liao +1
Large Transformer models have achieved state-of-the-art results in neural machine translation and have become standard in the field. In this work, we look for the optimal combinati…
Learning to Relate from Captions and Bounding Boxes
Sarthak Garg, Joel Ruben Antony Moniz, Anshu Aviral +1
In this work, we propose a novel approach that predicts the relationships between various entities in an image in a weakly supervised manner by relying on image captions and object…
Jointly Learning to Align and Translate with Transformer Models
Sarthak Garg, Stephan Peitz, Udhyakumar Nallasamy +1
The state of the art in machine translation (MT) is governed by neural approaches, which typically provide superior translation accuracy over statistical approaches. However, on th…
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces
Barun Patra, Joel Ruben Antony Moniz, Sarthak Garg +2
Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned bilingual lexicons or on distribution matching, often with an assumption about the isomet…
Compression and Localization in Reinforcement Learning for ATARI Games
Joel Ruben Antony Moniz, Barun Patra, Sarthak Garg
Deep neural networks have become commonplace in the domain of reinforcement learning, but are often expensive in terms of the number of parameters needed. While compressing deep ne…