  This paper introduces a novel training model, self-training from self-memory
(STSM) in data-to-text generation (DTG), allowing the model to self-train on
subsets, including self-memory as outputs inferred directly from the trained
models and/or the new data. The quality of self-memory is validated by two
models, data-to-text (D2T) and text-to-data (T2D), by two pre-defined
conditions: (1) the appearance of all source values in the outputs of the D2T
model and (2) the ability to convert back to source data in the outputs in the
T2D model. We utilize a greedy algorithm to generate shorter D2T outputs if
they contain all source values. Subsequently, we use the T2D model to confirm
that these outputs can capture input relationships by demonstrating their
capacity to convert text back into data. With 30% of the dataset, we can train
the D2T model with a competitive performance compared to full training in the
same setup. We experiment with our model on two datasets, E2E NLG and DART.
STSM offers the D2T model a generalization capability from its subset memory
while reducing training data volume. Ultimately, we anticipate that this paper
will contribute to continual learning solutions that adapt to new training
data, incorporating it as a form of self-memory in DTG tasks. The curated
dataset is publicly available at: https://github.com/hoangthangta/STSM.
