Attention-Based LSTM for Automatic Evaluation of Press Conferences

Shengzhou Yi, Koshiro Mochitomi, Isao Suzuki, Xueting Wang, Toshihiko Yamasaki

Abstract: We propose an approach to automatically predict the evaluation of the consultant for press conferences, using text only. The proposed approach includes a word representation model and a language model for automatic evaluation. The word representation model consists of token embedding using ELMo and type embedding. The language model we used is an LSTM with a self-attention mechanism. We collected seven publicly available press conference videos, and all the Q&A pairs between the journalists and the speakers were annotated by a professional consultant. As a result, we achieved an average accuracy of 57.6% for the prediction of 11 evaluation criterions.