activity
20182022
most citedUniversal Dependencies v2: An Evergrowing Multilingual Treebank Collection

331 citations · 359 across the 9 of their papers we have counts for

collaborators

12 papers

cs.CL20222 cited

Yet Another Format of Universal Dependencies for Korean

Yige Chen, Eunkyul Leah Jo, Yundong Yao +4

In this study, we propose a morpheme-based scheme for Korean dependency parsing and adopt the proposed scheme to Universal Dependencies. We present the linguistic rationale that il…

eess.AS2022

Curriculum optimization for low-resource speech recognition

Anastasia Kuznetsova, Anurag Kumar, Jennifer Drexler Fox +1

Modern end-to-end speech recognition models show astonishing results in transcribing audio signals into written text. However, conventional data feeding pipelines may be sub-optima…

cs.CL20218 cited

Evaluating Multiway Multilingual NMT in the Turkic Languages

Jamshidbek Mirzakhalov, Anoop Babu, Aigiz Kunafin +11

Despite the increasing number of large and comprehensive machine translation (MT) systems, evaluation of these methods in various languages has been restrained by the lack of high-…

cs.CL2021

A Large-Scale Study of Machine Translation in the Turkic Languages

Jamshidbek Mirzakhalov, Anoop Babu, Duygu Ataman +13

Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive…

cs.CL2021

What shall we do with an hour of data? Speech recognition for the un- and under-served languages of Common Voice

Francis M. Tyers, Josh Meyer

This technical report describes the methods and results of a three-week sprint to produce deployable speech recognition models for 31 under-served languages of the Common Voice pro…

cs.CL20212 cited

Do RNN States Encode Abstract Phonological Processes?

Miikka Silfverberg, Francis Tyers, Garrett Nicolai +1

Sequence-to-sequence models have delivered impressive results in word formation tasks such as morphological inflection, often learning to model subtle morphophonological details wi…