2,006 research outputs found
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
We present a large-scale collection of diverse natural language inference
(NLI) datasets that help provide insight into how well a sentence
representation captures distinct types of reasoning. The collection results
from recasting 13 existing datasets from 7 semantic phenomena into a common NLI
structure, resulting in over half a million labeled context-hypothesis pairs in
total. We refer to our collection as the DNC: Diverse Natural Language
Inference Collection. The DNC is available online at https://www.decomp.net,
and will grow over time as additional resources are recast and added from novel
sources.Comment: To be presented at EMNLP 2018. 15 page
Towards a Benchmark of Natural Language Arguments
The connections among natural language processing and argumentation theory
are becoming stronger in the latest years, with a growing amount of works going
in this direction, in different scenarios and applying heterogeneous
techniques. In this paper, we present two datasets we built to cope with the
combination of the Textual Entailment framework and bipolar abstract
argumentation. In our approach, such datasets are used to automatically
identify through a Textual Entailment system the relations among the arguments
(i.e., attack, support), and then the resulting bipolar argumentation graphs
are analyzed to compute the accepted arguments
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