  Response generation is a task in natural language processing (NLP) where a
model is trained to respond to human statements. Conversational response
generators take this one step further with the ability to respond within the
context of previous responses. While there are existing techniques for training
such models, they all require an abundance of conversational data which are not
always available for low-resource languages. In this research, we make three
contributions. First, we released the first Filipino conversational dataset
collected from a popular Philippine online forum, which we named the PEx
Conversations Dataset. Second, we introduce a data augmentation (DA)
methodology for Filipino data by employing a Tagalog RoBERTa model to increase
the size of the existing corpora. Lastly, we published the first Filipino
conversational response generator capable of generating responses related to
the previous 3 responses. With the supplementary synthetic data, we were able
to improve the performance of the response generator by up to 12.2% in
BERTScore, 10.7% in perplexity, and 11.7% in content word usage as compared to
training with zero synthetic data.
