33,622 research outputs found
CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning
To accelerate software development, much research has been performed to help
people understand and reuse the huge amount of available code resources. Two
important tasks have been widely studied: code retrieval, which aims to
retrieve code snippets relevant to a given natural language query from a code
base, and code annotation, where the goal is to annotate a code snippet with a
natural language description. Despite their advancement in recent years, the
two tasks are mostly explored separately. In this work, we investigate a novel
perspective of Code annotation for Code retrieval (hence called `CoaCor'),
where a code annotation model is trained to generate a natural language
annotation that can represent the semantic meaning of a given code snippet and
can be leveraged by a code retrieval model to better distinguish relevant code
snippets from others. To this end, we propose an effective framework based on
reinforcement learning, which explicitly encourages the code annotation model
to generate annotations that can be used for the retrieval task. Through
extensive experiments, we show that code annotations generated by our framework
are much more detailed and more useful for code retrieval, and they can further
improve the performance of existing code retrieval models significantly.Comment: 10 pages, 2 figures. Accepted by The Web Conference (WWW) 201
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
The Nonlinear autoregressive exogenous (NARX) model, which predicts the
current value of a time series based upon its previous values as well as the
current and past values of multiple driving (exogenous) series, has been
studied for decades. Despite the fact that various NARX models have been
developed, few of them can capture the long-term temporal dependencies
appropriately and select the relevant driving series to make predictions. In
this paper, we propose a dual-stage attention-based recurrent neural network
(DA-RNN) to address these two issues. In the first stage, we introduce an input
attention mechanism to adaptively extract relevant driving series (a.k.a.,
input features) at each time step by referring to the previous encoder hidden
state. In the second stage, we use a temporal attention mechanism to select
relevant encoder hidden states across all time steps. With this dual-stage
attention scheme, our model can not only make predictions effectively, but can
also be easily interpreted. Thorough empirical studies based upon the SML 2010
dataset and the NASDAQ 100 Stock dataset demonstrate that the DA-RNN can
outperform state-of-the-art methods for time series prediction.Comment: International Joint Conference on Artificial Intelligence (IJCAI),
201
Selective Attention for Context-aware Neural Machine Translation
Despite the progress made in sentence-level NMT, current systems still fall
short at achieving fluent, good quality translation for a full document. Recent
works in context-aware NMT consider only a few previous sentences as context
and may not scale to entire documents. To this end, we propose a novel and
scalable top-down approach to hierarchical attention for context-aware NMT
which uses sparse attention to selectively focus on relevant sentences in the
document context and then attends to key words in those sentences. We also
propose single-level attention approaches based on sentence or word-level
information in the context. The document-level context representation, produced
from these attention modules, is integrated into the encoder or decoder of the
Transformer model depending on whether we use monolingual or bilingual context.
Our experiments and evaluation on English-German datasets in different document
MT settings show that our selective attention approach not only significantly
outperforms context-agnostic baselines but also surpasses context-aware
baselines in most cases.Comment: Accepted at NAACL-HLT 201
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