4 research outputs found
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests
To assess the knowledge proficiency of a learner, multiple choice question is
an efficient and widespread form in standard tests. However, the composition of
the multiple choice question, especially the construction of distractors is
quite challenging. The distractors are required to both incorrect and plausible
enough to confuse the learners who did not master the knowledge. Currently, the
distractors are generated by domain experts which are both expensive and
time-consuming. This urges the emergence of automatic distractor generation,
which can benefit various standard tests in a wide range of domains. In this
paper, we propose a question and answer guided distractor generation (EDGE)
framework to automate distractor generation. EDGE consists of three major
modules: (1) the Reforming Question Module and the Reforming Passage Module
apply gate layers to guarantee the inherent incorrectness of the generated
distractors; (2) the Distractor Generator Module applies attention mechanism to
control the level of plausibility. Experimental results on a large-scale public
dataset demonstrate that our model significantly outperforms existing models
and achieves a new state-of-the-art.Comment: accepted by COLING202
Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions
In this paper, we propose a novel configurable framework to automatically
generate distractive choices for open-domain cloze-style multiple-choice
questions, which incorporates a general-purpose knowledge base to effectively
create a small distractor candidate set, and a feature-rich learning-to-rank
model to select distractors that are both plausible and reliable. Experimental
results on datasets across four domains show that our framework yields
distractors that are more plausible and reliable than previous methods. This
dataset can also be used as a benchmark for distractor generation in the
future.Comment: To appear at AAAI 202
Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension
In reading comprehension, generating sentence-level distractors is a
significant task, which requires a deep understanding of the article and
question. The traditional entity-centered methods can only generate word-level
or phrase-level distractors. Although recently proposed neural-based methods
like sequence-to-sequence (Seq2Seq) model show great potential in generating
creative text, the previous neural methods for distractor generation ignore two
important aspects. First, they didn't model the interactions between the
article and question, making the generated distractors tend to be too general
or not relevant to question context. Second, they didn't emphasize the
relationship between the distractor and article, making the generated
distractors not semantically relevant to the article and thus fail to form a
set of meaningful options. To solve the first problem, we propose a
co-attention enhanced hierarchical architecture to better capture the
interactions between the article and question, thus guide the decoder to
generate more coherent distractors. To alleviate the second problem, we add an
additional semantic similarity loss to push the generated distractors more
relevant to the article. Experimental results show that our model outperforms
several strong baselines on automatic metrics, achieving state-of-the-art
performance. Further human evaluation indicates that our generated distractors
are more coherent and more educative compared with those distractors generated
by baselines.Comment: 8 pages, 3 figures. Accepted by AAAI202