  Machine generation of Arithmetic Word Problems (AWPs) is challenging as they
express quantities and mathematical relationships and need to be consistent.
ML-solvers require a large annotated training set of consistent problems with
language variations. Exploiting domain-knowledge is needed for consistency
checking whereas LSTM-based approaches are good for producing text with
language variations. Combining these we propose a system, OLGA, to generate
consistent word problems of TC (Transfer-Case) type, involving object transfers
among agents. Though we provide a dataset of consistent 2-agent TC-problems for
training, only about 36% of the outputs of an LSTM-based generator are found
consistent. We use an extension of TC-Ontology, proposed by us previously, to
determine the consistency of problems. Among the remaining 64%, about 40% have
minor errors which we repair using the same ontology. To check consistency and
for the repair process, we construct an instance-specific representation (ABox)
of an auto-generated problem. We use a sentence classifier and BERT models for
this task. The training set for these LMs is problem-texts where sentence-parts
are annotated with ontology class-names. As three-agent problems are longer,
the percentage of consistent problems generated by an LSTM-based approach drops
further. Hence, we propose an ontology-based method that extends consistent
2-agent problems into consistent 3-agent problems. Overall, our approach
generates a large number of consistent TC-type AWPs involving 2 or 3 agents. As
ABox has all the information of a problem, any annotations can also be
generated. Adopting the proposed approach to generate other types of AWPs is
interesting future work.
