8,648 research outputs found
Combining Fact Extraction and Verification with Neural Semantic Matching Networks
The increasing concern with misinformation has stimulated research efforts on
automatic fact checking. The recently-released FEVER dataset introduced a
benchmark fact-verification task in which a system is asked to verify a claim
using evidential sentences from Wikipedia documents. In this paper, we present
a connected system consisting of three homogeneous neural semantic matching
models that conduct document retrieval, sentence selection, and claim
verification jointly for fact extraction and verification. For evidence
retrieval (document retrieval and sentence selection), unlike traditional
vector space IR models in which queries and sources are matched in some
pre-designed term vector space, we develop neural models to perform deep
semantic matching from raw textual input, assuming no intermediate term
representation and no access to structured external knowledge bases. We also
show that Pageview frequency can also help improve the performance of evidence
retrieval results, that later can be matched by using our neural semantic
matching network. For claim verification, unlike previous approaches that
simply feed upstream retrieved evidence and the claim to a natural language
inference (NLI) model, we further enhance the NLI model by providing it with
internal semantic relatedness scores (hence integrating it with the evidence
retrieval modules) and ontological WordNet features. Experiments on the FEVER
dataset indicate that (1) our neural semantic matching method outperforms
popular TF-IDF and encoder models, by significant margins on all evidence
retrieval metrics, (2) the additional relatedness score and WordNet features
improve the NLI model via better semantic awareness, and (3) by formalizing all
three subtasks as a similar semantic matching problem and improving on all
three stages, the complete model is able to achieve the state-of-the-art
results on the FEVER test set.Comment: AAAI 201
Insight Centre for Data Analytics (DCU) at TRECVid 2014: instance search and semantic indexing tasks
Insight-DCU participated in the instance search (INS) and semantic indexing (SIN) tasks in 2014. Two very different approaches were submitted for instance search, one based on features extracted using pre-trained deep convolutional neural networks (CNNs), and another based on local SIFT features, large vocabulary visual bag-of-words aggregation, inverted index-based lookup, and geometric verification on the top-N retrieved results. Two interactive runs and two automatic runs were submitted, the best interactive runs achieved a mAP of 0.135 and the best automatic 0.12. Our semantic indexing runs were based also on using convolutional neural network features, and on Support Vector Machine classifiers with linear and RBF kernels. One run was submitted to the main task, two to the no annotation task, and one to the progress task. Data for the no-annotation task was gathered from Google Images and ImageNet. The main task run has achieved a mAP of 0.086, the best no-annotation runs had a close performance to the main run by achieving a mAP of 0.080, while the progress run had 0.043
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