Program synthesis of general-purpose source code from natural language specifications is challenging due to the need to reason about high-level patterns in the target program and low-level implementation details at the same time. In this work, we present Patois, a system that allows a neural program synthesizer to explicitly interleave high-level and low-level reasoning at every generation step. It accomplishes this by automatically mining common code idioms from a given corpus, incorporating them into the underlying language for neural synthesis, and training a tree-based neural synthesizer to use these idioms during code generation. We evaluate Patois on two complex semantic parsing datasets and show that using learned code idioms improves the synthesizer's accuracy.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

These embeddings are decoded by a single four-layer convolutional network to generate notes from nearly 1000 instruments, 65 pitches per instrument on average and 5 velocities.

To answer your question about the baseline, we experimented with two new sample audio generated by the same (Karplus-Strong) algorithm and tested against Alexa. The result is shown in Table.1. The musical audio does not fool Alexa. Thank you again for your constructive feedback! Currently, we are also trying to activate the wake-word using our adversary.

Neural Information Processing Systems http://nips.cc/

The goal of this work is to build a TTS system which can generate natural speech for a variety of speakers in a data efficient manner.

These embeddings are decoded by a single four-layer convolutional network to generate notes from nearly 1000 instruments, 65 pitches per instrument on average and 5 velocities.

We thank reviewers for constructive comments! We would like to clarify that in [20], they didn't study how to incorporate a debugger component We will add a more detailed discussion in our revision. Table 1, SED still achieves better results than the synthesizer in this case. We will discuss more details in our revision. We will discuss the related work in our revision.

Here we introduce a new paradigm for resolving this ambiguity. We think of program synthesis as a kind of communication between the user and the synthesizer.