2,047 research outputs found
Attentional Encoder Network for Targeted Sentiment Classification
Targeted sentiment classification aims at determining the sentimental
tendency towards specific targets. Most of the previous approaches model
context and target words with RNN and attention. However, RNNs are difficult to
parallelize and truncated backpropagation through time brings difficulty in
remembering long-term patterns. To address this issue, this paper proposes an
Attentional Encoder Network (AEN) which eschews recurrence and employs
attention based encoders for the modeling between context and target. We raise
the label unreliability issue and introduce label smoothing regularization. We
also apply pre-trained BERT to this task and obtain new state-of-the-art
results. Experiments and analysis demonstrate the effectiveness and lightweight
of our model.Comment: 7 page
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention
Deep neural networks, including recurrent networks, have been successfully
applied to human activity recognition. Unfortunately, the final representation
learned by recurrent networks might encode some noise (irrelevant signal
components, unimportant sensor modalities, etc.). Besides, it is difficult to
interpret the recurrent networks to gain insight into the models' behavior. To
address these issues, we propose two attention models for human activity
recognition: temporal attention and sensor attention. These two mechanisms
adaptively focus on important signals and sensor modalities. To further improve
the understandability and mean F1 score, we add continuity constraints,
considering that continuous sensor signals are more robust than discrete ones.
We evaluate the approaches on three datasets and obtain state-of-the-art
results. Furthermore, qualitative analysis shows that the attention learned by
the models agree well with human intuition.Comment: 8 pages. published in The International Symposium on Wearable
Computers (ISWC) 201
Bethe Projections for Non-Local Inference
Many inference problems in structured prediction are naturally solved by
augmenting a tractable dependency structure with complex, non-local auxiliary
objectives. This includes the mean field family of variational inference
algorithms, soft- or hard-constrained inference using Lagrangian relaxation or
linear programming, collective graphical models, and forms of semi-supervised
learning such as posterior regularization. We present a method to
discriminatively learn broad families of inference objectives, capturing
powerful non-local statistics of the latent variables, while maintaining
tractable and provably fast inference using non-Euclidean projected gradient
descent with a distance-generating function given by the Bethe entropy. We
demonstrate the performance and flexibility of our method by (1) extracting
structured citations from research papers by learning soft global constraints,
(2) achieving state-of-the-art results on a widely-used handwriting recognition
task using a novel learned non-convex inference procedure, and (3) providing a
fast and highly scalable algorithm for the challenging problem of inference in
a collective graphical model applied to bird migration.Comment: minor bug fix to appendix. appeared in UAI 201
Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input
Non-autoregressive translation (NAT) models, which remove the dependence on
previous target tokens from the inputs of the decoder, achieve significantly
inference speedup but at the cost of inferior accuracy compared to
autoregressive translation (AT) models. Previous work shows that the quality of
the inputs of the decoder is important and largely impacts the model accuracy.
In this paper, we propose two methods to enhance the decoder inputs so as to
improve NAT models. The first one directly leverages a phrase table generated
by conventional SMT approaches to translate source tokens to target tokens,
which are then fed into the decoder as inputs. The second one transforms
source-side word embeddings to target-side word embeddings through
sentence-level alignment and word-level adversary learning, and then feeds the
transformed word embeddings into the decoder as inputs. Experimental results
show our method largely outperforms the NAT baseline~\citep{gu2017non} by
BLEU scores on WMT14 English-German task and BLEU scores on WMT16
English-Romanian task.Comment: AAAI 201
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