  A number of researchers have recently questioned the necessity of
increasingly complex neural network (NN) architectures. In particular, several
recent papers have shown that simpler, properly tuned models are at least
competitive across several NLP tasks. In this work, we show that this is also
the case for text generation from structured and unstructured data. We consider
neural table-to-text generation and neural question generation (NQG) tasks for
text 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 the
generated question can be answered by a certain sub-span of the passage using
NN models. Experimental results demonstrate that a basic attention-based
seq2seq model trained with the exponential moving average technique achieves
the state of the art in both tasks. Code is available at
https://github.com/h-shahidi/2birds-gen.
