ELM o- NB at S em E val-2020 Task 7: Assessing Sense of Humor in E dited N ews Headlines Using ELM o and NB (2020)

Abstract: Our approach is constructed to improve on a couple of aspects; preprocessing with an emphasis on humor sense detection, using embeddings from state-of-the-art language model(Elmo), and ensembling the results came up with using machine learning model Na ̈ıve Bayes(NB) with a deep learning pre-trained models. Elmo-NB participation has scored (0.5642) on the competition leader board, where results were measured by Root Mean Squared Error (RMSE).

## introduction checking the degree of sentence sense of humor through understanding and analyzing humans natural language and by connecting the text to an intelligent system is considered a critical task @xcite . hence expressing the readers and writers opinions can increase several emotions, we still need to expand the positive texts and establish a way for analyzing it @xcite . humor is considered a great way to the reader, it is resembling therapy @xcite . producing machines that can determine whether the sentence contains some degree of sense of humor or not is gaining a great attention recently @xcite . since social media is taking over most of people's daily life routines , the culture and environment affect the content greatly @xcite . numerous factors have brought increasing attention to real-life tasks such as text classification @xcite @xcite and other text analysis like pun classification @xcite . moreover, @xcite came up with an idea to detect the tweets humorousness using gaussian process. several natural language processing (nlp) applications and tools are proliferating recently @xcite , @xcite , especially with the rise of deep learning (dl) and machine learning (ml) enhancements @xcite @xcite . one of nlp state of the art approaches is elmo language preprocessing model as a pretrained model on general nlp tasks of language modeling (reimers and gurevych, 2019). elmo can be finetuned on specific tasks like next word prediction @xcite , translation @xcite or question answering @xcite and semantic text @xcite . another nlp state of the art is bert @xcite . semeval-2020 competition in task 7 1 has 313 as a total number of participants. the goal of this task is to assess humor in news headlines that have been modified using short edits to make them funny. there are two subtasks as follows: sub-task 1 (funniness estimation): regression problem, the goal is to assign a funniness grade to an edited headline between [0-3], where the systems will be ranked by root mean squared error (rmse). sub-task 2 (funnier of the two): a classification problem, given two different edited versions of the same headline. the goal of this sub task is to predict which is the funnier of the two. systems will be ranked by prediction accuracy. participant "oyx" scored the first place in both subtasks, scoring rmse (0.50157) for subtask 1, and accuracy (0.67237) for subtask 2. in this paper, we have experimented elmo with nb regression to predict the mean funniness of the edited headline in subtask 1. the main target of this task is discovering the atomic change and the tipping between the original and the humorous sentence. as a result, we need to rank the sentences between [0-3]: scores (0) it is not funny, slightly funny score (1), moderately funny score (2), and very funny score (3). the micro-editing made to the sentence to make them funny is defined as replacing a noun by a different noun phrase, an entity with different nouns and a verb with a different verb as in @xcite . elmo with nb is showing an amazing performance in predicting the value of humor in the news headline. the rest of this paper is presented as follows: sections 2 overviews related work. section 3 describes the methodology proposed in this paper. sections 4 overviews results and discusses the most important findings of some experiments and models evaluation. finally, section 5 concludes this research and provides possibilities for future work. ## literature reviews creating a model that is able to judge the sensitivity to be humorist or not is still a critical task. several machine learning (ml) and deep learning (dl) approaches are recommended strongly for working on detecting humorist sentences. for example, using bert dl pretarined model has a significant role in detecting sentiments and emotions in text @xcite . a team @xcite participated in the haha 2019 task used bert as a bi-directional representation and fine-tuned pretrain dataset. they obtained the output layer after training the model with the mean squad error (mse). other researchers @xcite added a new task called shared task between the first and second approaches to explore humour. they focused on experimentally comparing hashtag wars from tv show @midnight. the neural network-based system recorded the higher rank. researchers @xcite proposed a new approach to detect sarcasm. in their experiment, they created a dataset based on quotes goodreads website, which is one of the largest sites for reading book recommendations. they used word embedding with four types lsa, glove, dependency-based, and word2vec. a similar task is what researchers @xcite did recently, where they created a competitive game called funlines. the users can edit the news headlines. the new sentence has some degree for the sense of humour; they set a method to define the funlines and organizing the sentences to categorize (fun, interactive, collaborative, rewarding, and educational). the classification improvements used to check the performance with and without this dataset augmentation. they were showing that using bert gave much better results than using lstm with glove word vectors as a benchmark results. the application provides useful feedback to users, to improve their ability to learn and upgrade the level of humourist sentence. in this way the newly generated dataset is performing better. ## conclusion and future work through this challenge (semeval-2020 competition subtask 1 in task 7), we uses embeddings from language models (elmo) with the naïve bayes (nb) model as the primary baseline. the main focus is using the best text manipulation algorithm, where we recommend using elmo for it is usefulness and its ability to generate "contextualized" word embeddings. our trial is to use the elmo pretrained and then let the ml models make the prediction. where the nb with elmo recorded lowest rmse. for future work, we want to use different ml and dl models with the dataset. also, we plan to use different-dimensional and pre-trained embedding. using the xlnet, with well-spotted parameters could be useful too.