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

Neural Discourse Structure for Text Categorization

We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization. Our approach uses a recursive neural network and a newly proposed attention mechanism to compute a representation of the text that focuses on salient content, from the perspective of both RST and the task. Experiments consider variants of the approach and illustrate its strengths and weaknesses.Comment: ACL 2017 camera ready versio

The effect of transparency on recognition of overlapping objects

Are overlapping objects easier to recognize when the objects are transparent or opaque? It is important to know whether the transparency of X-ray images of luggage contributes to the difficulty in searching those images for targets. Transparency provides extra information about objects that would normally be occluded but creates potentially ambiguous depth relations at the region of overlap. Two experiments investigated the threshold durations at which adult participants could accurately name pairs of overlapping objects that were opaque or transparent. In Experiment 1, the transparent displays included monocular cues to relative depth. Recognition of the back object was possible at shorter durations for transparent displays than for opaque displays. In Experiment 2, the transparent displays had no monocular depth cues. There was no difference in the duration at which the back object was recognized across transparent and opaque displays. The results of the two experiments suggest that transparent displays, even though less familiar than opaque displays, do not make object recognition more difficult, and possibly show a benefit. These findings call into question the importance of edge junctions in object recognitio