Publications
CONFERENCE (INTERNATIONAL) Pretraining Sentiment Classifiers with Unlabeled Dialog Data
Toru Shimizu, Hayato Kobayashi, Nobuyuki Shimizu
The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)
July 18, 2018
The huge cost of creating labeled training data is a common problem for supervised learning tasks such as sentiment classification. Recent studies showed that pretraining with unlabeled data via a language model can improve the performance of classification models. In this paper, we take the concept a step further by using a conditional language model, instead of a language model. Specifically, we address a sentiment classification task for a tweet analysis service as a case study and propose a pretraining strategy with unlabeled dialog data (tweet-reply pairs) via an encoder-decoder model. Experimental results show that our strategy can improve the performance of sentiment classifiers and outperform several state-of-the-art strategies including language model pretraining.
Paper :
Pretraining Sentiment Classifiers with Unlabeled Dialog Data
(external link)
PDF : Pretraining Sentiment Classifiers with Unlabeled Dialog Data