136,521 research outputs found
Using edit distance to analyse errors in a natural language to logic translation corpus
We have assembled a large corpus of student submissions to an automatic grading system, where the subject matter involves the translation of natural language sentences into propositional logic. Of the 2.3 million translation instances in the corpus, 286,000 (approximately 12%) are categorized as being in error. We want to understand the nature of the errors that students make, so that we can develop tools and supporting infrastructure that help students with the problems that these errors represent.
With this aim in mind, this paper describes an analysis of a significant proportion of the data, using edit distance between incorrect answers and their corresponding correct solutions, and the associated edit sequences, as a means of organising the data and detecting categories of errors. We demonstrate that a large proportion of errors can be accounted for by means of a small number of relatively simple error types, and that the method draws attention to interesting phenomena in the data set
S4Net: Single Stage Salient-Instance Segmentation
We consider an interesting problem-salient instance segmentation in this
paper. Other than producing bounding boxes, our network also outputs
high-quality instance-level segments. Taking into account the
category-independent property of each target, we design a single stage salient
instance segmentation framework, with a novel segmentation branch. Our new
branch regards not only local context inside each detection window but also its
surrounding context, enabling us to distinguish the instances in the same scope
even with obstruction. Our network is end-to-end trainable and runs at a fast
speed (40 fps when processing an image with resolution 320x320). We evaluate
our approach on a publicly available benchmark and show that it outperforms
other alternative solutions. We also provide a thorough analysis of the design
choices to help readers better understand the functions of each part of our
network. The source code can be found at
\url{https://github.com/RuochenFan/S4Net}
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