17,492 research outputs found
Argument Mining with Structured SVMs and RNNs
We propose a novel factor graph model for argument mining, designed for
settings in which the argumentative relations in a document do not necessarily
form a tree structure. (This is the case in over 20% of the web comments
dataset we release.) Our model jointly learns elementary unit type
classification and argumentative relation prediction. Moreover, our model
supports SVM and RNN parametrizations, can enforce structure constraints (e.g.,
transitivity), and can express dependencies between adjacent relations and
propositions. Our approaches outperform unstructured baselines in both web
comments and argumentative essay datasets.Comment: Accepted for publication at ACL 2017. 11 pages, 5 figures. Code at
https://github.com/vene/marseille and data at http://joonsuk.org
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing
We evaluate a semantic parser based on a character-based sequence-to-sequence
model in the context of the SemEval-2017 shared task on semantic parsing for
AMRs. With data augmentation, super characters, and POS-tagging we gain major
improvements in performance compared to a baseline character-level model.
Although we improve on previous character-based neural semantic parsing models,
the overall accuracy is still lower than a state-of-the-art AMR parser. An
ensemble combining our neural semantic parser with an existing, traditional
parser, yields a small gain in performance.Comment: To appear in Proceedings of SemEval, 2017 (camera-ready
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