Using a natural language understanding system for commonsense comprehension is getting increasing attention from researchers. Current multi-purpose state-of-the-art models suffer on commonsense validation and explanation tasks. We have adopted one of the state-of-the-art models and proposing a method to boost the performance of the model in commonsense related tasks.

This paper describes our contribution to SemEval-2020 Task 7: Assessing Humor in Edited News Headlines. Here we present a method based on a deep neural network. In recent years, quite some attention has been devoted to humor production and perception. Our team KDEhumor employs recurrent neural network models including Bi-Directional LSTMs (BiLSTMs). Moreover, we utilize the state-of-the-art pre-trained sentence embedding techniques. We analyze the performance of our method and demonstrate the contribution of each component of our architecture.

A meme is a pictorial representation of an idea or theme. In the age of emerging volume of social media platforms, memes are spreading rapidly from person to person and becoming a trending ways of opinion expression. However, due to the multimodal characteristics of meme contents, detecting and analyzing the underlying emotion of a meme is a formidable task. In this paper, we present our approach for detecting the emotion of a meme defined in the SemEval-2020 Task 8. Our team CSECU_KDE_MA employs an attention-based neural network model to tackle the problem. Upon extracting the text contents from a meme using an optical character reader (OCR), we represent it using the distributed representation of words. Next, we perform the convolution based on multiple kernel sizes to obtain the higher-level feature sequences. The feature sequences are then fed into the attentive time-distributed bidirectional LSTM model to learn the long-term dependencies effectively. Experimental results show that our proposed neural model obtained competitive performance among the participants’ systems.

In recent years, with the development of social network services and video distribution services, there has been a sharp increase in offensive posts. In this paper, we present our approach for detecting hate speech in tweets defined in the SemEval- 2020 Task 12. Our system precise classification by using features extracted from two different layers of a pre-trained model, the BERT-large, and ensemble them.

Stance detection in twitter aims at mining user stances expressed in a tweet towards a single or multiple target entities. To tackle this problem, most of the prior studies have been explored the traditional deep learning models, e.g., LSTM and GRU. However, in compared to these traditional approaches, recently proposed densely connected Bi-LSTM and nested LSTMs architectures effectively address the vanishing-gradient and overfitting problems as well as dealing with long-term dependencies. In this paper, we propose a neural ensemble model that adopts the strengths of these two LSTM variants to learn better long-term dependencies, where each module coupled with an attention mechanism that amplifies the contribution of important elements in the final representation. We also employ a multi-kernel convolution on top of them to extract the higher-level tweet representations. Results of extensive experiments on single and multi-target stance detection datasets show that our proposed method achieves substantial improvement over the current state-of-the-art deep learning based methods.

In the age of emerging volume of microblog platforms, especially twitter, hate speech propagation is now of great concern. However, due to the brevity of tweets and informal user generated contents, detecting and analyzing hate speech on twitter is a formidable task. In this paper, we present our approach for detecting hate speech in tweets defined in the SemEval-2019 Task 5. Our team KDEHatEval employs different neural network models including multi-kernel convolution (MKC), nested LSTMs (NLSTMs), and multi-layer perceptron (MLP) in a unified architecture. Moreover, we utilize the state-of-the-art pre-trained sentence embedding models including DeepMoji, InferSent, and BERT for effective tweet representation. We analyze the performance of our method and demonstrate the contribution of each component of our architecture.

This paper describes our approach to SemEval-2018 Task1: Estimation of Affects in Tweet for 1a and 2a. Our team KDE-AFFECT employs several methods including one-dimensional Convolutional Neural Network for n-grams, together with word embedding and other preprocessing such as vocabulary unification and Emoji conversion into four emotional words.