TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Open-Domain Question Answering	ELI5	Multi-Inrerleave	Rouge-L	14.63	# 6
Open-Domain Question Answering	ELI5	Multi-Inrerleave	Rouge-1	23.32	# 5
Open-Domain Question Answering	ELI5	Multi-Inrerleave	Rouge-2	4.79	# 5

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Improving Conditioning in Context-Aware Sequence to Sequence Models

21 Nov 2019 · Xinyi Wang, Jason Weston, Michael Auli, Yacine Jernite ·

Neural sequence to sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence. In this work, we focus on cases where generation is conditioned on both a short query and a long context, such as abstractive question answering or document-level translation. We modify the standard sequence-to-sequence approach to make better use of both the query and the context by expanding the conditioning mechanism to intertwine query and context attention. We also introduce a simple and efficient data augmentation method for the proposed model. Experiments on three different tasks show that both changes lead to consistent improvements.

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