Modeling coherence in ESOL learner texts
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If you have a question about this talk, please contact Ekaterina Kochmar.
To date, few attempts have been made to develop new methods and validate
existing ones for automatic evaluation of discourse coherence in the
noisy domain of learner texts. We present the first systematic analysis
of several methods for assessing coherence under the framework of
automated assessment (AA) of learner free-text responses. We examine the
predictive power of different coherence models by measuring the effect
on performance when combined with an AA system that achieves competitive
results, but does not use discourse coherence features, which are also
strong indicators of a learner’s level of attainment. Additionally, we
identify new techniques that outperform previously developed ones and
improve on the best published result for AA on a publically-available
dataset of English learner free-text examination scripts.
This talk is part of the NLIP Seminar Series series.
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