Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing

WS 2019 Chunyang XiaoChristoph TeichmannKonstantine Arkoudas

While sequence-to-sequence (seq2seq) models achieve state-of-the-art performance in many natural language processing tasks, they can be too slow for real-time applications. One performance bottleneck is predicting the most likely next token over a large vocabulary; methods to circumvent this bottleneck are a current research topic... (read more)

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