1 citations · 1 across the 2 of their papers we have counts for
6 papers · 1 filter
Comparing Euclidean and Hyperbolic Embeddings on the WordNet Nouns Hypernymy Graph
Sameer Bansal, Adrian Benton
Nickel and Kiela (2017) present a new method for embedding tree nodes in the Poincare ball, and suggest that these hyperbolic embeddings are far more effective than Euclidean embed…
Analyzing ASR pretraining for low-resource speech-to-text translation
Mihaela C. Stoian, Sameer Bansal, Sharon Goldwater
Previous work has shown that for low-resource source languages, automatic speech-to-text translation (AST) can be improved by pretraining an end-to-end model on automatic speech re…
Cross-lingual topic prediction for speech using translations
Sameer Bansal, Herman Kamper, Adam Lopez +1
Given a large amount of unannotated speech in a low-resource language, can we classify the speech utterances by topic? We consider this question in the setting where a small amount…
Pre-training on high-resource speech recognition improves low-resource speech-to-text translation
Sameer Bansal, Herman Kamper, Karen Livescu +2
We present a simple approach to improve direct speech-to-text translation (ST) when the source language is low-resource: we pre-train the model on a high-resource automatic speech…
Low-Resource Speech-to-Text Translation
Sameer Bansal, Herman Kamper, Karen Livescu +2
Speech-to-text translation has many potential applications for low-resource languages, but the typical approach of cascading speech recognition with machine translation is often im…
Towards speech-to-text translation without speech recognition
Sameer Bansal, Herman Kamper, Adam Lopez +1
We explore the problem of translating speech to text in low-resource scenarios where neither automatic speech recognition (ASR) nor machine translation (MT) are available, but we h…