ECNU at S em E val-2020 Task 7: Assessing Humor in Edited News Headlines Using B i LSTM with Attention (2020)

Abstract: In this paper we describe our system submitted to SemEval 2020 Task 7: “Assessing Humor in Edited News Headlines”. We participated in all subtasks, in which the main goal is to predict the mean funniness of the edited headline given the original and the edited headline. Our system involves two similar sub-networks, which generate vector representations for the original and edited headlines respectively. And then we do a subtract operation of the outputs from two sub-networks to predict the funniness of the edited headline.

## introduction humor can be defined as the aspiration of provoking laughter and provides amusement from expressions intended @xcite . the task of humor recognition refers to determining whether a sentence in a given context contains some level of humorous content. the semeval 2020 task 7 @xcite aims to automatically computes the funniness of edited news headlines which are generated using an insertion of a single-word noun or verb to replace an existing entity or single-word noun or verb in original headline @xcite . there are two sub-tasks in task 7. the sub-task 1 is to predict the mean funniness of the edited headline given the original and edited headline. the sub-task 2 is based on sub-task 1, which aims to determine which version of edits makes the headline more humorous given the original headline and two edited versions. in prior studies, humor recognition has been approached as a binary classification problem. traditional classification algorithms like svm and naive bayes @xcite , and a deep learning cnn architecture @xcite are adopted to distinguish between humorous and non-humorous texts. however, humor is not just a binary concept and it occurs in various intensities. in addition, in the past, the research objective for humor recognition is a sentence or text. however, it is interesting to study how short edits applied to a text can turn it from non-funny to funny, which can help us focus on the humorous effects of atomic changes and pointing out the key difference between non-humorous and humorous text. in this paper, we present our funniness prediction system which is mainly based on bidirectional lstm @xcite neural networks with attention mechanism @xcite . besides, we show some features related to humor and then analyze the effectiveness of different configurations of our system. ## system description we develop a framework including two similar sub-networks whose inputs are the sequence of tokens in the original headline and edited headline respectively. then two outputs of sub-networks and other text features are combined together to predict the mean funniness of the edited headline. figure 1 represents the network structure of our overall model, with two similar sub-networks displayed in two sides. the highlighted word, in original headline is the replaced word, and in edited headline is the replacement word. specifically, each sub-network contains the token representation layer using pre-trained word embeddings, this work is licensed under a creative commons attribution 4.0 international license. license details: http:// creativecommons.org/licenses/by/4.0/ . bilstm layer and attention layer. we apply bilstm to obtain contextual token representations. we also use attention mechanism in order to get the headline representations. ## results since sub-task 2 is based on the predictions of sub-task 1 to a great extent, we only report the results of sub-task 1. as described in section 2.1, we trained the model based on glove embeddings and bert pre-trained embeddings. ## conclusion in this paper, we present a deep learning model which contains two similar sub-networks. we design a two-layers bilstm with attention mechanism model, whose inputs are the original headlines and edited headlines. and then we predict the funniness of the edited headline by a subtract operation between the outputs of the two sub-networks mentioned above and concatenating the hand-crafted features. the experimental results show this method can assess the intensity of humor to some extent. news headlines are short and concise, and humor often comes from phrases and common sense. in semeval 2020 task 7, there are all sorts of ironies in edited headlines, but no effective nlp tools can recognize them so far. in the future, we consider to introduce external knowledge to model headlines and improve the humor recognition performance.