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Joint Repetition Suppression and Content Moderation of Large Language Models
Natural language generation (NLG) is one of the most impactful fields in NLP, and recent years have witnessed its evolution brought about by large language models (LLMs).
An Evaluation on Large Language Model Outputs: Discourse and Memorization
We present an empirical evaluation of various outputs generated by nine of the most widely-available large language models (LLMs).
USTED: Improving ASR with a Unified Speech and Text Encoder-Decoder
There has been a recent focus on training E2E ASR models that get the performance benefits of external text data without incurring the extra cost of evaluating an external language model at inference time.
Neural Machine Translation For Paraphrase Generation
Training a spoken language understanding system, as the one in Alexa, typically requires a large human-annotated corpus of data.
Neural Machine Translation for Multilingual Grapheme-to-Phoneme Conversion
Grapheme-to-phoneme (G2P) models are a key component in Automatic Speech Recognition (ASR) systems, such as the ASR system in Alexa, as they are used to generate pronunciations for out-of-vocabulary words that do not exist in the pronunciation lexicons (mappings like "e c h o" to "E k oU").
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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