Agree to Disagree: Improving Disagreement Detection with Dual GRUs
This paper presents models for detecting agreement/disagreement in online discussions. In this work we show that by using a Siamese inspired architecture to encode the discussions, we no longer need to rely on hand-crafted features to exploit the meta thread structure. We evaluate our model on existing online discussion corpora ABCD, IAC and AWTP. Experimental results on ABCD dataset show that by fusing lexical and word embedding features, our model achieves the state-of-the-art performance of 0.804 average F1 score. We also show that the model trained on ABCD dataset performs competitively on relatively smaller annotated datasets (IAC and AWTP).
ESSEM @ ACII 2017 [http://acii2017.org/]
Seernet at EmoInt-2017: Tweet Emotion Intensity Estimator
The paper describes experiments on estimating emotion intensity in tweets using a generalized regressor system. The system combines lexical, syntactic and pre- trained word embedding features, trains them on general regressors and finally combines the best performing models to create an ensemble. The proposed system stood 3rd out of 22 systems in the leaderboard of WASSA-2017 Shared Task on Emotion Intensity.
WASSA @ EMNLP 2017 [http://optima.jrc.it/wassa2017/]
Ensemble of Deep Neural Networks for Acoustic Scene Classification
Deep neural networks (DNNs) have recently achieved great success in a multitude of classification tasks. Ensembles of DNNs have been shown to improve the performance. In this paper, we explore the recent state-of-the-art DNNs used for image classification. We modified these DNNs and applied them to the task of acoustic scene classification. We conducted a number of experiments on the TUT Acoustic Scenes 2017 dataset to empirically compare these methods. Finally, we show that the ensemble of these DNNs improves the baseline score for DCASE-2017 Task 1 by 10%.
Detection and Classification of Acoustic Scenes and Events 2017