KDE humor at S em E val-2020 Task 7: A Neural Network Model for Detecting Funniness in Dataset Humicroedit (2020)

Abstract: 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.

## introduction humor, a way of giving entertainment and provoking laughter, is an exceptionally savvy communicative activity. in recent years, quite some attention has been devoted to humor production and perception. what makes us laugh when reading a funny sentence? mostly it depends on the cultural area background of a person. humor cannot be defined in standards as it varies with nation to nation, area to area, people to people. many types of humor require substantial external knowledge such as irony, wordplay, metaphor and sarcasm. these factors make the task of humor recognition difficult. recently, with the advance of deep learning that allows end-to-end training with big data without human intervention of feature selection, humor recognition becomes promising. however detecting humor in news headlines from which one word is chosen to be replaced by another word to make the micro-edited sentences look funny is quite challenging. to address the challenges of humor detection in edited news headlines, @xcite proposed detection of funniness level in dataset named as humicroedit foot_0 , task 7 at semeval-2020. they focus on two related subtasks. task 1 defines regression problem where a system needs to predict how much funny is the edited news headlines (humicroedit) from range 0 to 3. whereas task 2 defines the classification problem where a system need to predict the funniest sentence between two edited headlines. in this paper, we only focus on the humicroedit data for both tasks 1 and 2. no extra data, or other sources is used to get more data. the rest of the paper is structured as follows: section 2 provides a brief overview of prior research. in section 3, we introduce our proposed neural network model. section 4 includes experiments and evaluations as well as the analysis of our proposed method. some concluded remarks and future directions of our work are described in section 5. ## related research humor is omnipresent, universal, elusive event which exists in all societies and cultures and fulfills a range of social, cognitive and emotional functions. the task of automatic humor recognition refers to deciding whether a given sentence expresses a certain degree of humor. in previous researches and studies, mostly the problems related to humor were based on binary classification, or based on selection on linguistic this work is licensed under a creative commons attribution 4.0 international licence. licence details: http:// creativecommons.org/licenses/by/4.0/ . in other researches, with the development of artificial neural networks, many studies utilize the methods for humor recognition. luke de oliveira and alfredo applied recurrent neural network (rnn) to humor detection from reviews in yelp dataset. they also applied convolutional neural networks (cnns) to train a model and the results from model trained with cnns have more accurate humor recognition @xcite . recently, bertero and fung proposed a first-ever attempt to employ a long shortterm memory(lstm) based framework to predict humor in dialogues. they showed how the lstm effectively models the setup-punchline relation reducing the number of false positives and increasing the recall @xcite . in a recent work, chen and lee predicted audience's laughter also using convolutional neural network. their work gets higher detection accuracy and is able to learn essential feature automatically @xcite . among several prominent works, peng-yu chen and von-wun soo build the humor recognizer by using cnns with extensive filter size and number, and the result shows higher accuracy from previous cnns models by conducting experiments on two different datasets. @xcite . ## proposed model in this section, we describe the details of our proposed neural network model. the goal of our proposed approach is to predict how much funny the edited news headlines (humicroedit) is. we tried to train our model after data processing. figure 1 depicts an overview of our proposed model. at first, in a dataset, original sentences were changes by using edit words. then on edited sentences, pre-trained word embedding model was implemented to obtain the high-quality distributed vector representations for our datasets. next, we apply the bidirectional lstms (bilstms) models to extract the higher level feature sequences with sequential information from the edited news headlines embedding. we employ an encoder pre-trained by google news word2vec model to encode each word into 300-dimensional feature vector. these feature vectors are then sent to a bidirectional lstm module. finally, the generated output feature sequences from bidirectional lstms fed into the fully-connected prediction module to determine the prediction. next, we describe each component elaborately. ## conclusion in this paper, we presented our approach for semeval-2020 task 7: assessing humor in edited news headlines. detection of humor is not an easy task. we tried to tackle the problem by employing deep learning techniques. we conducted some experiments using different models for example we implemented simple regression model, multilayer perceptron model but the model that gave better result was kdehumor model. in future, we are planning to incorporate bert model for further improvement.