Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts
This work addresses the need for conversational AI in Vietnamese healthcare, but it is incremental as it primarily introduces a new dataset and benchmarks existing methods.
The authors tackled the problem of conversational machine reading comprehension for Vietnamese healthcare texts by creating a new corpus called UIT-ViCoQA, consisting of 10,000 questions over 2,000 conversations, and evaluated baseline models, with the best achieving an F1 score of 45.27%, which is 30.91 points behind human performance.
Machine reading comprehension (MRC) is a sub-field in natural language processing that aims to assist computers understand unstructured texts and then answer questions related to them. In practice, the conversation is an essential way to communicate and transfer information. To help machines understand conversation texts, we present UIT-ViCoQA, a new corpus for conversational machine reading comprehension in the Vietnamese language. This corpus consists of 10,000 questions with answers over 2,000 conversations about health news articles. Then, we evaluate several baseline approaches for conversational machine comprehension on the UIT-ViCoQA corpus. The best model obtains an F1 score of 45.27%, which is 30.91 points behind human performance (76.18%), indicating that there is ample room for improvement. Our dataset is available at our website: http://nlp.uit.edu.vn/datasets/ for research purposes.