Abstract:
Automatic Question Generation focuses on generating questions from a span of text is a
significant problem in Natural Language Processing (NLP). Question generation in lowresource
languages
is
under-explored
compared
to
high-resource
languages.
In
the
earlier
work,
all
the
parameters
of
a
pre-trained
multilingual
language
model
were
fine-tuned
to
perform
a
zero-shot
question
generation
and
other
sequence-to-sequence
(S2S)
generation
tasks.
However, such full model fine-tuning is not computationally efficient. Recent
research introduced a neural module called adapter to each Transformer layer of a pretrained
language
model
and
fine-tuned
only
the
adapter
parameters
to
mitigate
this
issue.
In
this study, we explored single task adapter and adapter fusion on the pre-trained
multilingual model mBART to generate questions from Tamil text. Our best model
produced a Rough-1 (F1) score of 16.9. Furthermore, we obtained a similar result with
two variants of adapters called Houlsby adapter [1] and Pfeifer adapter [1], which resemble
the result of adapters for other S2S tasks[2].
Citation:
Purusanth, S. (2022). Exploiting adapters for question generation from Tamil text in A zero - resource setting [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21590