2 citations · 2 across the 2 of their papers we have counts for
6 papers
The SIGMORPHON 2022 Shared Task on Morpheme Segmentation
Khuyagbaatar Batsuren, Gábor Bella, Aryaman Arora +10
The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, deriva…
NeMo Inverse Text Normalization: From Development To Production
Yang Zhang, Evelina Bakhturina, Kyle Gorman +1
Inverse text normalization (ITN) converts spoken-domain automatic speech recognition (ASR) output into written-domain text to improve the readability of the ASR output. Many state-…
Detecting Objectifying Language in Online Professor Reviews
Angie Waller, Kyle Gorman
Student reviews often make reference to professors' physical appearances. Until recently RateMyProfessors.com, the website of this study's focus, used a design feature to encourage…
Is the Best Better? Bayesian Statistical Model Comparison for Natural Language Processing
Piotr Szymański, Kyle Gorman
Recent work raises concerns about the use of standard splits to compare natural language processing models. We propose a Bayesian statistical model comparison technique which uses…
What Kind of Language Is Hard to Language-Model?
Sabrina J. Mielke, Ryan Cotterell, Kyle Gorman +2
How language-agnostic are current state-of-the-art NLP tools? Are there some types of language that are easier to model with current methods? In prior work (Cotterell et al., 2018)…
Minimally Supervised Written-to-Spoken Text Normalization
Ke Wu, Kyle Gorman, Richard Sproat
In speech-applications such as text-to-speech (TTS) or automatic speech recognition (ASR), \emph{text normalization} refers to the task of converting from a \emph{written} represen…