  Despite significant progress in Natural Language Generation for Indian
languages (IndicNLP), there is a lack of datasets around complex structured
tasks such as semantic parsing. One reason for this imminent gap is the
complexity of the logical form, which makes English to multilingual translation
difficult. The process involves alignment of logical forms, intents and slots
with translated unstructured utterance. To address this, we propose an
Inter-bilingual Seq2seq Semantic parsing dataset IE-SEMPARSE for 11 distinct
Indian languages. We highlight the proposed task's practicality, and evaluate
existing multilingual seq2seq models across several train-test strategies. Our
experiment reveals a high correlation across performance of original
multilingual semantic parsing datasets (such as mTOP, multilingual TOP and
multiATIS++) and our proposed IE-SEMPARSE suite.
