UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it is an understudied area. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research

Klasifikasi Teks Humor Bahasa Indonesia Memanfaatkan SVM

Dalam dunia humor banyak sekali ditemukan tipe tipe humor yang bervariasi namun dengan satu tujuan yaitu menghibur penikmat humor, klasifikasi terbesar adalah humor verbal dan non verbal, untuk humor non verbal inilah penelitian ini difokuskan, yaitu melakukan klasifikasi humor oneliner, humor sebaris barupa tulisan singkat yang bertujuan menghantarkan sebuah punchline dan premis dalam satu kalimat. Penelitian ini akan berusaha melakukan klasifikasi humor menjadi beberapa kategori dengan menggunakan algoritma SVM dan word2vec, klasifikasi ini nantinya diharapkan memisahkan jenis jenis humor oneliner menjadi beberapa tipe sesuai cara penyajiannya, dengan dataset yang didapat dari komedian komedian profesional dan dilakukan proses pengenalan manual oleh ahli di bidangnya, penelitian ini bertujuan menemukan beberapa kelas humor yang akan menjadi cikal bakal pengenalan komputerisasi humor berbahasa indonesia

Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation

We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at https://www.decomp.net, and will grow over time as additional resources are recast and added from novel sources.Comment: To be presented at EMNLP 2018. 15 page

LT3 at SemEval-2020 Task 7 : comparing feature-based and transformer-based approaches to detect funny headlines

This paper presents two different systems for the SemEval shared task 7 on Assessing Humor in Edited News Headlines, sub-task 1, where the aim was to estimate the intensity of humor generated in edited headlines. Our first system is a feature-based machine learning system that combines different types of information (e.g. word embeddings, string similarity, part-of-speech tags, perplexity scores, named entity recognition) in a Nu Support Vector Regressor (NuSVR). The second system is a deep learning-based approach that uses the pre-trained language model RoBERTa to learn latent features in the news headlines that are useful to predict the funniness of each headline. The latter system was also our final submission to the competition and is ranked seventh among the 49 participating teams, with a root-mean-square error (RMSE) of 0.5253

The contribution of humor in our lives

Humor is a perceptual event connected with one’s sense of self, an expression of a uniquely human capacity to adapt to experiences and situations that may be possible sources of humor. Humor seems to be a powerful coping mechanism used to decrease fear, anxiety, and psychological stress. The appreciation of humor requires a wide area of neural circuits covering attention, working memory, flexible thinking, extraction of word meaning, and positive mood. The cognitive component of humor, which is probably mediated by the dorsolateral cortex, may deteriorate with aging. Laughter and smiling as communication tools may be lost in the early stages of dementia, when the clinical symptoms of dementia appear. The use of humor therapy appears to be an effective non pharmacological intervention contributing to significant increases in happiness and life satisfaction for the elderly people

Humor Detection

Humor is a very complex characteristic concept that defines us as human beings and social entities. Humor is an essential component in personal communication. How to create a method or model to discover the structures behind humor, recognize humor and even extraction of humor remains a challenge because of its subjective nature. Humor also provides valuable information related to linguistic, psychological, neurological and sociological phenomena. However, because of its complexity, humor is still an undefined phenomenon. Because the reaction that make people laugh can hardly be generalized or formalized. For instance, cognitive aspects as well as cultural knowledge, are some of the multi-factorial variables that should be analyzed in order to understand humor\u27s properties. Although it is impossible to understand universal humor characteristics, one can still capture the possible latent structures behind humor. In my work, I will try to uncover several latent semantic structures behind humor, in terms of meaning incongruity, ambiguity, phonetic style and personal affect. In addition to humor recognition, identifying anchors, or which words prompt humor in a sentence, is essential in understanding the phenomenon of humor in language. Proposed technique is created using the concepts of linguistics and it has significant accuracy of over 70+% compared to 23.06% of Word Index power method