8,408 research outputs found
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another neural network with access to connectives, and thus encouraged to
extract similarly salient features for accurate classification. We develop an
adversarial model to enable an adaptive imitation scheme through competition
between the implicit network and a rival feature discriminator. Our method
effectively transfers discriminability of connectives to the implicit features,
and achieves state-of-the-art performance on the PDTB benchmark.Comment: To appear in ACL201
Baseline Detection in Historical Documents using Convolutional U-Nets
Baseline detection is still a challenging task for heterogeneous collections
of historical documents. We present a novel approach to baseline extraction in
such settings, turning out the winning entry to the ICDAR 2017 Competition on
Baseline detection (cBAD). It utilizes deep convolutional nets (CNNs) for both,
the actual extraction of baselines, as well as for a simple form of layout
analysis in a pre-processing step. To the best of our knowledge it is the first
CNN-based system for baseline extraction applying a U-net architecture and
sliding window detection, profiting from a high local accuracy of the candidate
lines extracted. Final baseline post-processing complements our approach,
compensating for inaccuracies mainly due to missing context information during
sliding window detection. We experimentally evaluate the components of our
system individually on the cBAD dataset. Moreover, we investigate how it
generalizes to different data by means of the dataset used for the baseline
extraction task of the ICDAR 2017 Competition on Layout Analysis for
Challenging Medieval Manuscripts (HisDoc). A comparison with the results
reported for HisDoc shows that it also outperforms the contestants of the
latter.Comment: 6 pages, accepted to DAS 201
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