Learning Answer Generation using Supervision from Automatic Question
  Answering Evaluators
Matteo Gabburo Siddhant Garg Rik Koncel-Kedziorski Alessandro Moschitti
Recent studies show that sentence-level extractive QA, i.e., based on Answer
Sentence Selection (AS2), is outperformed by Generation-based QA (GenQA)
models, which generate answers using the top-k answer sentences ranked by AS2
models (a la retrieval-augmented generation style). In this paper, we propose a
novel training paradigm for GenQA using supervision from automatic QA
evaluation models (GAVA). Specifically, we propose three strategies to transfer
knowledge from these QA evaluation models to a GenQA model: (i) augmenting
training data with answers generated by the GenQA model and labelled by GAVA
(either statically, before training, or (ii) dynamically, at every training
epoch); and (iii) using the GAVA score for weighting the generator loss during
the learning of the GenQA model. We evaluate our proposed methods on two
academic and one industrial dataset, obtaining a significant improvement in
answering accuracy over the previous state of the art.