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Arul Menezes

9 accepted papers

2026

BhashaKritika: Building Synthetic Pretraining Data at Scale for Indic Languages

AAAI 2026technical

In the context of pretraining of Large Language Models (LLMs), synthetic data has emerged as an alternative for generating high-quality pretraining data at scale. This is particularly beneficial in low resource language settings where the benefits of the recent LLMs have been unevenly distributed ac

Cited by 0SourcePDFScholar
2023

Leveraging GPT-4 for Automatic Translation Post-Editing

EMNLP 2023long findings

While Neural Machine Translation (NMT) represents the leading approach to Machine Translation (MT), the outputs of NMT models still require translation post-editing to rectify errors and enhance quality under critical settings. In this work, we formalize the task of direct translation post-editing w…

Cited by 0SourceScholar
2023

TRIP: Accelerating Document-level Multilingual Pre-training via Triangular Document-level Pre-training on Parallel Data Triplets

EMNLP 2023long findings

Despite the success of multilingual sequence-to-sequence pre-training, most existing approaches rely on document-level monolingual corpora in many different languages, sentence-level bilingual corpora,\footnote{In this paper, we use bilingual corpora to denote parallel corpora with bilingual transla…

Cited by 0SourceScholar
2023

VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing

AAAI 2023technical

Video dubbing aims to translate the original speech in a film or television program into the speech in a target language, which can be achieved with a cascaded system consisting of speech recognition, machine translation and speech synthesis. To ensure the translated speech to be well aligned with t…

2022

Finding Memo: Extractive Memorization in Constrained Sequence Generation Tasks

EMNLP 2022finding

Memorization presents a challenge for several constrained Natural Language Generation (NLG) tasks such as Neural Machine Translation (NMT), wherein the proclivity of neural models to memorize noisy and atypical samples reacts adversely with the noisy (web crawled) datasets. However, previous studies…

2022

SALTED: A Framework for SAlient Long-tail Translation Error Detection

EMNLP 2022finding

Traditional machine translation (MT) metrics provide an average measure of translation quality that is insensitive to the long tail of behavioral problems. Examples include translation of numbers, physical units, dropped content and hallucinations. These errors, which occur rarely and unpredictably…

2021

The Curious Case of Hallucinations in Neural Machine Translation

NAACL 2021long

In this work, we study hallucinations in Neural Machine Translation (NMT), which lie at an extreme end on the spectrum of NMT pathologies. Firstly, we connect the phenomenon of hallucinations under source perturbation to the Long-Tail theory of Feldman, and present an empirically validated hypothesi…