Clustering Vietnamese Conversations From Facebook Page To Build Training Dataset For Chatbot
This work addresses the data scarcity problem for chatbot developers, particularly for Vietnamese language applications, but it is incremental as it applies existing methods like PhoBERT and clustering to a new data source.
The authors tackled the challenge of obtaining realistic and large training data for chatbots by developing a tool to extract and cluster Vietnamese conversations from Facebook Messenger, using PhoBERT for feature extraction and clustering algorithms to organize the data, which saved significant time and effort in dataset creation.
The biggest challenge of building chatbots is training data. The required data must be realistic and large enough to train chatbots. We create a tool to get actual training data from Facebook messenger of a Facebook page. After text preprocessing steps, the newly obtained dataset generates FVnC and Sample dataset. We use the Retraining of BERT for Vietnamese (PhoBERT) to extract features of our text data. K-Means and DBSCAN clustering algorithms are used for clustering tasks based on output embeddings from PhoBERT$_{base}$. We apply V-measure score and Silhouette score to evaluate the performance of clustering algorithms. We also demonstrate the efficiency of PhoBERT compared to other models in feature extraction on the Sample dataset and wiki dataset. A GridSearch algorithm that combines both clustering evaluations is also proposed to find optimal parameters. Thanks to clustering such a number of conversations, we save a lot of time and effort to build data and storylines for training chatbot.