Parameter-Efficient Abstractive Question Answering over Tables or Text

• URL: http://arxiv.org/abs/2204.03357v1
• Date: Thu, 7 Apr 2022 10:56:29 GMT
• Title: Parameter-Efficient Abstractive Question Answering over Tables or Text
• Authors: Vaishali Pal, Evangelos Kanoulas, Maarten de Rijke
• Abstract summary: A long-term ambition of information seeking QA systems is to reason over multi-modal contexts and generate natural answers to user queries. 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.
• Score: 60.86457030988444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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