Graph-Based Semi-Supervised Learning for Natural Language Understanding
Zimeng Qiu, Eunah Cho, Xiaochun Ma, William M. Campbell · 2019
Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data.Development of many natural language understanding (NLU) applications has a challenge where unlabeled data is relatively abundant while labeled data is rather limited.In this work, we propose transductive graphbased semi-supervised learning models as well as their inductive variants for NLU.We evaluate the approach's applicability using publicly available NLU data and models.In order to find similar utterances and construct a graph, we use a paraphrase detection model.Results show that applying the inductive graph-based semi-supervised learning can improve the error rate of the NLU model by 5%.