Arya D. McCarthy
Arya McCarthy is a Research Scientist at Google DeepMind, where he expands the multilingual capabilities of Gemini for a global audience. His research focuses on bridging the gap between languages through structure-grounded machine translation, speech algorithms, and model adaptation for specialized domains.
Learn more: https://aryamccarthy.github.io/
Learn more: https://aryamccarthy.github.io/
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One challenge in speech translation is that plenty of spoken content is long-form, but short units are necessary for obtaining high-quality translations. To address this mismatch, we adapt large language models (LLMs) to split long ASR transcripts into segments that can be independently translated so as to maximize the overall translation quality. We overcome the tendency of hallucination in LLMs by incorporating finite-state constraints during decoding; these eliminate invalid outputs without requiring additional training. We discover that LLMs are adaptable to transcripts containing ASR errors through prompt-tuning or fine-tuning. Relative to a state-of-the-art automatic punctuation baseline, our best LLM improves the average BLEU by 2.9 points for English–German, English–Spanish, and English–Arabic TED talk translation in 9 test sets, just by improving segmentation.
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Meaning to Form: Measuring Systematicity as Information
Tiago Pimentel
Damian Blasi
Ryan Cotterell
Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL) (2019), pp. 1751-1764
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A longstanding debate in semiotics centers on the relationship between linguistic signs and their corresponding semantics: is there an arbitrary relationship between a word form and its meaning, or does some systematic phenomenon pervade? For instance, does the character bigram 'gl' have any systematic relationship to the meaning of words like 'glisten', 'gleam' and 'glow'? In this work, we offer a holistic quantification of the systematicity of the sign using mutual information and recurrent neural networks. We employ these in a data-driven and massively multilingual approach to the question, examining 106 languages. We find a statistically significant reduction in entropy when modeling a word form conditioned on its semantic representation. Encouragingly, we also recover well-attested English examples of systematic affixes. We conclude with the meta-point: Our approximate effect size (measured in bits) is quite small -despite some amount of systematicity between form and meaning, an arbitrary relationship and its resulting benefits dominate human language.
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