論 文Papers

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), 2018/7

Category:

自然言語処理 (Natural Language Processing)

Abstract:
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.
Download:

Pretraining Sentiment Classifiers with Unlabeled Dialog Data(外部サイト/External Site Link)