7,227 research outputs found
Temporal Attention-Gated Model for Robust Sequence Classification
Typical techniques for sequence classification are designed for
well-segmented sequences which have been edited to remove noisy or irrelevant
parts. Therefore, such methods cannot be easily applied on noisy sequences
expected in real-world applications. In this paper, we present the Temporal
Attention-Gated Model (TAGM) which integrates ideas from attention models and
gated recurrent networks to better deal with noisy or unsegmented sequences.
Specifically, we extend the concept of attention model to measure the relevance
of each observation (time step) of a sequence. We then use a novel gated
recurrent network to learn the hidden representation for the final prediction.
An important advantage of our approach is interpretability since the temporal
attention weights provide a meaningful value for the salience of each time step
in the sequence. We demonstrate the merits of our TAGM approach, both for
prediction accuracy and interpretability, on three different tasks: spoken
digit recognition, text-based sentiment analysis and visual event recognition.Comment: Accepted by CVPR 201
Character-Aware Neural Language Models
We describe a simple neural language model that relies only on
character-level inputs. Predictions are still made at the word-level. Our model
employs a convolutional neural network (CNN) and a highway network over
characters, whose output is given to a long short-term memory (LSTM) recurrent
neural network language model (RNN-LM). On the English Penn Treebank the model
is on par with the existing state-of-the-art despite having 60% fewer
parameters. On languages with rich morphology (Arabic, Czech, French, German,
Spanish, Russian), the model outperforms word-level/morpheme-level LSTM
baselines, again with fewer parameters. The results suggest that on many
languages, character inputs are sufficient for language modeling. Analysis of
word representations obtained from the character composition part of the model
reveals that the model is able to encode, from characters only, both semantic
and orthographic information.Comment: AAAI 201
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