Discriminative Approach to Fill-in-the-Blank Quiz Generation for Language Learners
Article Structure
Abstract
We propose discriminative methods to generate semantic distractors of fill-in-the-blank quiz for language learners using a large-scale language learners’ corpus.
Introduction
Proposed Method
To generate distractors, we first need to decide which word to be blanked.
Evaluation with Native-Speakers
In this experiment, we evaluate the reliability of generated distractors.
Topics
generative models
Appears in 4 sentences as: generative model (1) generative models (3)
In Discriminative Approach to Fill-in-the-Blank Quiz Generation for Language Learners
- We rank the candidates by a generative model to consider the surrounding context (e.g.Page 2, “Proposed Method”
- With respect to H, our discriminative models achieve from 0.12 to 0.2 higher agreement than baselines, indicating that the discriminative models can generate sound distractors more effectively than generative models .Page 4, “Evaluation with Native-Speakers”
- The lower H on generative models may be because the distractors are semantically too close to the target (correct answer) as following examples:Page 4, “Evaluation with Native-Speakers”
- As a result, the quiz from generative models is not reliable since both published and issued are correct.Page 4, “Evaluation with Native-Speakers”
See all papers in Proc. ACL 2013 that mention generative models.
See all papers in Proc. ACL that mention generative models.
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