  A long-term ambition of information seeking QA systems is to reason over
multi-modal contexts and generate natural answers to user queries. Today,
memory intensive pre-trained language models are adapted to downstream tasks
such as QA by fine-tuning the model on QA data in a specific modality like
unstructured text or structured tables. To avoid training such memory-hungry
models while utilizing a uniform architecture for each modality,
parameter-efficient adapters add and train small task-specific bottle-neck
layers between transformer layers. In this work, we study parameter-efficient
abstractive QA in encoder-decoder models over structured tabular data and
unstructured textual data using only 1.5% additional parameters for each
modality. We also ablate over adapter layers in both encoder and decoder
modules to study the efficiency-performance trade-off and demonstrate that
reducing additional trainable parameters down to 0.7%-1.0% leads to comparable
results. Our models out-perform current state-of-the-art models on tabular QA
datasets such as Tablesum and FeTaQA, and achieve comparable performance on a
textual QA dataset such as NarrativeQA using significantly less trainable
parameters than fine-tuning.
