2,252 research outputs found
Code Prediction by Feeding Trees to Transformers
We advance the state-of-the-art in the accuracy of code prediction (next
token prediction) used in autocomplete systems. First, we report that using the
recently proposed Transformer architecture even out-of-the-box outperforms
previous neural and non-neural systems for code prediction. We then show that
by making the Transformer architecture aware of the syntactic structure of
code, we further increase the margin by which a Transformer-based system
outperforms previous systems. With this, it outperforms the accuracy of an
RNN-based system (similar to Hellendoorn et al. 2018) by 18.3\%, the Deep3
system (Raychev et al 2016) by 14.1\%, and an adaptation of Code2Seq (Alon et
al., 2018) for code prediction by 14.4\%.
We present in the paper several ways of communicating the code structure to
the Transformer, which is fundamentally built for processing sequence data. We
provide a comprehensive experimental evaluation of our proposal, along with
alternative design choices, on a standard Python dataset, as well as on a
Facebook internal Python corpus. Our code and data preparation pipeline will be
available in open source
Image Representations and New Domains in Neural Image Captioning
We examine the possibility that recent promising results in automatic caption
generation are due primarily to language models. By varying image
representation quality produced by a convolutional neural network, we find that
a state-of-the-art neural captioning algorithm is able to produce quality
captions even when provided with surprisingly poor image representations. We
replicate this result in a new, fine-grained, transfer learned captioning
domain, consisting of 66K recipe image/title pairs. We also provide some
experiments regarding the appropriateness of datasets for automatic captioning,
and find that having multiple captions per image is beneficial, but not an
absolute requirement.Comment: 11 Pages, 5 Images, To appear at EMNLP 2015's Vision + Learning
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