26,253 research outputs found
Using Regular Languages to Explore the Representational Capacity of Recurrent Neural Architectures
The presence of Long Distance Dependencies (LDDs) in sequential data poses
significant challenges for computational models. Various recurrent neural
architectures have been designed to mitigate this issue. In order to test these
state-of-the-art architectures, there is growing need for rich benchmarking
datasets. However, one of the drawbacks of existing datasets is the lack of
experimental control with regards to the presence and/or degree of LDDs. This
lack of control limits the analysis of model performance in relation to the
specific challenge posed by LDDs. One way to address this is to use synthetic
data having the properties of subregular languages. The degree of LDDs within
the generated data can be controlled through the k parameter, length of the
generated strings, and by choosing appropriate forbidden strings. In this
paper, we explore the capacity of different RNN extensions to model LDDs, by
evaluating these models on a sequence of SPk synthesized datasets, where each
subsequent dataset exhibits a longer degree of LDD. Even though SPk are simple
languages, the presence of LDDs does have significant impact on the performance
of recurrent neural architectures, thus making them prime candidate in
benchmarking tasks.Comment: International Conference of Artificial Neural Networks (ICANN) 201
Recurrent Memory Networks for Language Modeling
Recurrent Neural Networks (RNN) have obtained excellent result in many
natural language processing (NLP) tasks. However, understanding and
interpreting the source of this success remains a challenge. In this paper, we
propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only
amplifies the power of RNN but also facilitates our understanding of its
internal functioning and allows us to discover underlying patterns in data. We
demonstrate the power of RMN on language modeling and sentence completion
tasks. On language modeling, RMN outperforms Long Short-Term Memory (LSTM)
network on three large German, Italian, and English dataset. Additionally we
perform in-depth analysis of various linguistic dimensions that RMN captures.
On Sentence Completion Challenge, for which it is essential to capture sentence
coherence, our RMN obtains 69.2% accuracy, surpassing the previous
state-of-the-art by a large margin.Comment: 8 pages, 6 figures. Accepted at NAACL 201
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