473 research outputs found
Compositional sequence labeling models for error detection in learner writing
© 2016 Association for Computational Linguistics. In this paper, we present the first experiments using neural network models for the task of error detection in learner writing. We perform a systematic comparison of alternative compositional architectures and propose a framework for error detection based on bidirectional LSTMs. Experiments on the CoNLL-14 shared task dataset show the model is able to outperform other participants on detecting errors in learner writing. Finally, the model is integrated with a publicly deployed self-assessment system, leading to performance comparable to human annotators
Modeling Global Syntactic Variation in English Using Dialect Classification
This paper evaluates global-scale dialect identification for 14 national
varieties of English as a means for studying syntactic variation. The paper
makes three main contributions: (i) introducing data-driven language mapping as
a method for selecting the inventory of national varieties to include in the
task; (ii) producing a large and dynamic set of syntactic features using
grammar induction rather than focusing on a few hand-selected features such as
function words; and (iii) comparing models across both web corpora and social
media corpora in order to measure the robustness of syntactic variation across
registers
- …