111 research outputs found
A PDTB-Styled End-to-End Discourse Parser
We have developed a full discourse parser in the Penn Discourse Treebank
(PDTB) style. Our trained parser first identifies all discourse and
non-discourse relations, locates and labels their arguments, and then
classifies their relation types. When appropriate, the attribution spans to
these relations are also determined. We present a comprehensive evaluation from
both component-wise and error-cascading perspectives.Comment: 15 pages, 5 figures, 7 table
System Combination via Quality Estimation for Grammatical Error Correction
Quality estimation models have been developed to assess the corrections made
by grammatical error correction (GEC) models when the reference or
gold-standard corrections are not available. An ideal quality estimator can be
utilized to combine the outputs of multiple GEC systems by choosing the best
subset of edits from the union of all edits proposed by the GEC base systems.
However, we found that existing GEC quality estimation models are not good
enough in differentiating good corrections from bad ones, resulting in a low
F0.5 score when used for system combination. In this paper, we propose GRECO, a
new state-of-the-art quality estimation model that gives a better estimate of
the quality of a corrected sentence, as indicated by having a higher
correlation to the F0.5 score of a corrected sentence. It results in a combined
GEC system with a higher F0.5 score. We also propose three methods for
utilizing GEC quality estimation models for system combination with varying
generality: model-agnostic, model-agnostic with voting bias, and
model-dependent method. The combined GEC system outperforms the state of the
art on the CoNLL-2014 test set and the BEA-2019 test set, achieving the highest
F0.5 scores published to date.Comment: EMNLP 202
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