159,926 research outputs found
Group Sparse CNNs for Question Classification with Answer Sets
Question classification is an important task with wide applications. However,
traditional techniques treat questions as general sentences, ignoring the
corresponding answer data. In order to consider answer information into
question modeling, we first introduce novel group sparse autoencoders which
refine question representation by utilizing group information in the answer
set. We then propose novel group sparse CNNs which naturally learn question
representation with respect to their answers by implanting group sparse
autoencoders into traditional CNNs. The proposed model significantly outperform
strong baselines on four datasets.Comment: 6, ACL 201
Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering
We propose an unsupervised strategy for the selection of justification
sentences for multi-hop question answering (QA) that (a) maximizes the
relevance of the selected sentences, (b) minimizes the overlap between the
selected facts, and (c) maximizes the coverage of both question and answer.
This unsupervised sentence selection method can be coupled with any supervised
QA approach. We show that the sentences selected by our method improve the
performance of a state-of-the-art supervised QA model on two multi-hop QA
datasets: AI2's Reasoning Challenge (ARC) and Multi-Sentence Reading
Comprehension (MultiRC). We obtain new state-of-the-art performance on both
datasets among approaches that do not use external resources for training the
QA system: 56.82% F1 on ARC (41.24% on Challenge and 64.49% on Easy) and 26.1%
EM0 on MultiRC. Our justification sentences have higher quality than the
justifications selected by a strong information retrieval baseline, e.g., by
5.4% F1 in MultiRC. We also show that our unsupervised selection of
justification sentences is more stable across domains than a state-of-the-art
supervised sentence selection method.Comment: Published at EMNLP-IJCNLP 2019 as long conference paper. Corrected
the name reference for Speer et.al, 201
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Proceedings of QG2010: The Third Workshop on Question Generation
These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge".
QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)
A Convolutional Neural Network for Modelling Sentences
The ability to accurately represent sentences is central to language
understanding. We describe a convolutional architecture dubbed the Dynamic
Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of
sentences. The network uses Dynamic k-Max Pooling, a global pooling operation
over linear sequences. The network handles input sentences of varying length
and induces a feature graph over the sentence that is capable of explicitly
capturing short and long-range relations. The network does not rely on a parse
tree and is easily applicable to any language. We test the DCNN in four
experiments: small scale binary and multi-class sentiment prediction, six-way
question classification and Twitter sentiment prediction by distant
supervision. The network achieves excellent performance in the first three
tasks and a greater than 25% error reduction in the last task with respect to
the strongest baseline
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