1 citations · 2 across the 4 of their papers we have counts for
7 papers
Automating Behavioral Testing in Machine Translation
Javier Ferrando, Matthias Sperber, Hendra Setiawan +2
Behavioral testing in NLP allows fine-grained evaluation of systems by examining their linguistic capabilities through the analysis of input-output behavior. Unfortunately, existin…
Accurate Knowledge Distillation with n-best Reranking
Hendra Setiawan
We propose utilizing n-best reranking to enhance Sequence-Level Knowledge Distillation (Kim and Rush, 2016) where we extract pseudo-labels for student model's training data from to…
Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data
Mozhdeh Gheini, Tatiana Likhomanenko, Matthias Sperber +1
Self-training has been shown to be helpful in addressing data scarcity for many domains, including vision, speech, and language. Specifically, self-training, or pseudo-labeling, la…
End-to-End Speech Translation for Code Switched Speech
Orion Weller, Matthias Sperber, Telmo Pires +4
Code switching (CS) refers to the phenomenon of interchangeably using words and phrases from different languages. CS can pose significant accuracy challenges to NLP, due to the oft…
Consistent Transcription and Translation of Speech
Matthias Sperber, Hendra Setiawan, Christian Gollan +2
The conventional paradigm in speech translation starts with a speech recognition step to generate transcripts, followed by a translation step with the automatic transcripts as inpu…
Variational Neural Machine Translation with Normalizing Flows
Hendra Setiawan, Matthias Sperber, Udhay Nallasamy +1
Variational Neural Machine Translation (VNMT) is an attractive framework for modeling the generation of target translations, conditioned not only on the source sentence but also on…