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A number of researchers have recently questioned the necessity ofincreasingly complex neural network (NN) architectures. In particular, severalrecent papers have shown that simpler, properly tuned models are at leastcompetitive across several NLP tasks. In this work, we show that this is alsothe case for text generation from structured and unstructured data. We considerneural table-to-text generation and neural question generation (NQG) tasks fortext generation from structured and unstructured data, respectively.Table-to-text generation aims to generate a description based on a given table,and NQG is the task of generating a question from a given passage where thegenerated question can be answered by a certain sub-span of the passage usingNN models. Experimental results demonstrate that a basic attention-basedseq2seq model trained with the exponential moving average technique achievesthe state of the art in both tasks. Code is available athttps://github.com/h-shahidi/2birds-gen.

Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data