Humor Detection

For understanding multimodal language used in expressing humor. Source: UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

PHINC is a parallel corpus of the 13,738 code-mixed English-Hindi sentences and their corresponding translation in English. The translations of sentences are done manually by the annotators. Source: PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation

A dataset of games played in the card game "Cards Against Humanity" (CAH), by human players, derived from the online CAH labs. Each round includes the cards presented to users - a "black" prompt with a blank or question and 10 "white" punchlines as possible responses, and which punchline was picked by a player each round, along with text and metadata. An example prompt is “TSA guidelines now prohibit ___ on airplanes”. Candidate punchlines are “Goblins”, “BATMAN!!!”, “Poor people”, and “The right amount of cocaine”. Importantly, many cards are offensive or politically incorrect. Used to explore human humor preferences. Available upon request from CAH labs: mail@cardsagainsthumanity.com Train/test splits and data processing available in the paper/code: "Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game": https://github.com/ddofer/CAH

A great number of situational comedies (sitcoms) are being regularly made and the task of adding laughter tracks to these is a critical task. Providing an ability to be able to predict whether something will be humorous to the audience is also crucial. In this project, we aim to automate this task. Towards doing so, we annotate an existing sitcom (Big Bang Theory') and use the laughter cues present to obtain a manual annotation for this show. We provide detailed analysis for the dataset design and further evaluate various state of the art baselines for solving this task. We observe that existing LSTM and BERT based networks on the text alone do not perform as well as joint text and video or only video-based networks. Moreover, it is challenging to ascertain that the words attended to while predicting laughter are indeed humorous. Our dataset and analysis provided through this paper is a valuable resource towards solving this interesting semantic and practical task. As an additional contribution, we have developed a novel model for solving this task that is a multi-modal self-attention based model that outperforms currently prevalent models for solving this task.