Papers by Keywee Bee
Draft Final Portfolio for UWI- Post Graduate Dipolma in Education
Thesis Chapters by Keywee Bee
Question Generation is a field of research that has been growing in popularity through the years,... more Question Generation is a field of research that has been growing in popularity through the years, as
educators seek to find ways to make test generation a lot easier with the use of Machine Learning
and Artificial Intelligence. In this paper we research how automated end-to-end question generation
that utilizes transformers will generate an understandable question by making an inference on sample
paragraphs. The model is trained in an end-to-end approach where the model focuses on the context
paragraph to understand the sentences and generate a question where the answer is not directly in the
context paragraph.
An inference approach is proposed to find hidden sentences by using discourse analysis and paraphrasing
techniques based on fine tuning transformers to be fed into a model that can generate a question from
the hidden or new sentences. The Stanford parser was also utilized to get a clearer view of the parts of
speech focusing particularly on the verbs, pronouns, and other key entities in the sentence. Experiments
on the context paragraphs was conducted on the SQuAD 1.1 dataset where we attempted to transform
the original input paragraphs using the inference rule. After transforming all sentences, we then fed these
new shortened paragraphs to the transformer model to generate a deeper level understanding question.
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Papers by Keywee Bee
Thesis Chapters by Keywee Bee
educators seek to find ways to make test generation a lot easier with the use of Machine Learning
and Artificial Intelligence. In this paper we research how automated end-to-end question generation
that utilizes transformers will generate an understandable question by making an inference on sample
paragraphs. The model is trained in an end-to-end approach where the model focuses on the context
paragraph to understand the sentences and generate a question where the answer is not directly in the
context paragraph.
An inference approach is proposed to find hidden sentences by using discourse analysis and paraphrasing
techniques based on fine tuning transformers to be fed into a model that can generate a question from
the hidden or new sentences. The Stanford parser was also utilized to get a clearer view of the parts of
speech focusing particularly on the verbs, pronouns, and other key entities in the sentence. Experiments
on the context paragraphs was conducted on the SQuAD 1.1 dataset where we attempted to transform
the original input paragraphs using the inference rule. After transforming all sentences, we then fed these
new shortened paragraphs to the transformer model to generate a deeper level understanding question.