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20052025
most citedBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

24.4k citations

Showing 2016 · cs.CLShow all

8 papers · 2 filters

cs.CL201645 cited

Learning to Compose Words into Sentences with Reinforcement Learning

Dani Yogatama, Phil Blunsom, Chris Dyer +2

We use reinforcement learning to learn tree-structured neural networks for computing representations of natural language sentences. In contrast with prior work on tree-structured m…

cs.CL201636 cited

Learning a Natural Language Interface with Neural Programmer

Arvind Neelakantan, Quoc V. Le, Martin Abadi +2

Learning a natural language interface for database tables is a challenging task that involves deep language understanding and multi-step reasoning. The task is often approached by…

cs.CL201611 cited

What Do Recurrent Neural Network Grammars Learn About Syntax?

Adhiguna Kuncoro, Miguel Ballesteros, Lingpeng Kong +3

Recurrent neural network grammars (RNNG) are a recently proposed probabilistic generative modeling family for natural language. They show state-of-the-art language modeling and par…

cs.CL201657 cited

Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition

Hagen Soltau, Hank Liao, Hasim Sak

We present results that show it is possible to build a competitive, greatly simplified, large vocabulary continuous speech recognition system with whole words as acoustic units. We…

cs.CL201610 cited

Very Deep Convolutional Networks for End-to-End Speech Recognition

Yu Zhang, William Chan, Navdeep Jaitly

Sequence-to-sequence models have shown success in end-to-end speech recognition. However these models have only used shallow acoustic encoder networks. In our work, we successively…

cs.CL20165 cited

Evaluating Induced CCG Parsers on Grounded Semantic Parsing

Yonatan Bisk, Siva Reddy, John Blitzer +2

We compare the effectiveness of four different syntactic CCG parsers for a semantic slot-filling task to explore how much syntactic supervision is required for downstream semantic…