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Found 4 papers, 2 papers with code
How Much Context Does My Attention-Based ASR System Need?
For the task of speech recognition, the use of more than 30 seconds of acoustic context during training is uncommon, and under-investigated in literature.
Leveraging Cross-Utterance Context For ASR Decoding
While external language models (LMs) are often incorporated into the decoding stage of automated speech recognition systems, these models usually operate with limited context.
MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation
This work introduces MTCue, a novel neural machine translation (NMT) framework that interprets all context (including discrete variables) as text.
Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification
We present two convolutional neural networks for predicting the complexity of words and phrases in context on a continuous scale.
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