  The Data-to-Text task aims to generate human-readable text for describing
some given structured data enabling more interpretability. However, the typical
generation task is confined to a few particular domains since it requires
well-aligned data which is difficult and expensive to obtain. Using
partially-aligned data is an alternative way of solving the dataset scarcity
problem. This kind of data is much easier to obtain since it can be produced
automatically. However, using this kind of data induces the over-generation
problem posing difficulties for existing models, which tends to add unrelated
excerpts during the generation procedure. In order to effectively utilize
automatically annotated partially-aligned datasets, we extend the traditional
generation task to a refined task called Partially-Aligned Data-to-Text
Generation (PADTG) which is more practical since it utilizes automatically
annotated data for training and thus considerably expands the application
domains. To tackle this new task, we propose a novel distant supervision
generation framework. It firstly estimates the input data's supportiveness for
each target word with an estimator and then applies a supportiveness adaptor
and a rebalanced beam search to harness the over-generation problem in the
training and generation phases respectively. We also contribute a
partially-aligned dataset (The data and source code of this paper can be
obtained from https://github.com/fuzihaofzh/distant_supervision_nlg by sampling
sentences from Wikipedia and automatically extracting corresponding KB triples
for each sentence from Wikidata. The experimental results show that our
framework outperforms all baseline models as well as verify the feasibility of
utilizing partially-aligned data.
