1 code implementation • NAACL 2022 • John Harvill, Roxana Girju, Mark Hasegawa-Johnson
In this paper we focus on patterns of colexification (co-expressions of form-meaning mapping in the lexicon) as an aspect of lexical-semantic organization, and use them to build large scale synset graphs across BabelNet’s typologically diverse set of 499 world languages.
no code implementations • 16 Aug 2023 • Eunseop Yoon, Hee Suk Yoon, Dhananjaya Gowda, SooHwan Eom, Daehyeok Kim, John Harvill, Heting Gao, Mark Hasegawa-Johnson, Chanwoo Kim, Chang D. Yoo
Text-to-Text Transfer Transformer (T5) has recently been considered for the Grapheme-to-Phoneme (G2P) transduction.
no code implementations • 25 May 2023 • Eunseop Yoon, Hee Suk Yoon, John Harvill, Mark Hasegawa-Johnson, Chang D. Yoo
INTapt is trained simultaneously in the following two manners: (1) adversarial training to reduce accent feature dependence between the original input and the prompt-concatenated input and (2) training to minimize CTC loss for improving ASR performance to a prompt-concatenated input.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 3 Feb 2023 • John Harvill, Jarred Barber, Arun Nair, Ramin Pishehvar
Self-supervised representation learning approaches have grown in popularity due to the ability to train models on large amounts of unlabeled data and have demonstrated success in diverse fields such as natural language processing, computer vision, and speech.
no code implementations • 14 Dec 2022 • Hee Suk Yoon, Eunseop Yoon, John Harvill, Sunjae Yoon, Mark Hasegawa-Johnson, Chang D. Yoo
To the best of our knowledge, this is the first attempt to apply mixup in NLP while preserving the meaning of a specific word.