ColBERT: Using BERT Sentence Embedding in Parallel Neural Networks for Computational Humor

TL;DR

This paper introduces a novel neural network approach using BERT sentence embeddings and linguistic humor theory to detect and rate humor in short texts, achieving high accuracy on a new dataset and Spanish tweets.

Contribution

It presents a new parallel neural network architecture leveraging BERT embeddings and a novel humor dataset, advancing humor detection methods.

Findings

F1 score of 0.982 on humor detection

Outperforms state-of-the-art models

Robust across different datasets

Abstract

Automation of humor detection and rating has interesting use cases in modern technologies, such as humanoid robots, chatbots, and virtual assistants. In this paper, we propose a novel approach for detecting and rating humor in short texts based on a popular linguistic theory of humor. The proposed technical method initiates by separating sentences of the given text and utilizing the BERT model to generate embeddings for each one. The embeddings are fed to separate lines of hidden layers in a neural network (one line for each sentence) to extract latent features. At last, the parallel lines are concatenated to determine the congruity and other relationships between the sentences and predict the target value. We accompany the paper with a novel dataset for humor detection consisting of 200,000 formal short texts. In addition to evaluating our work on the novel dataset, we participated in a live machine learning competition focused on rating humor in Spanish tweets. The proposed model obtained F1 scores of 0.982 and 0.869 in the humor detection experiments which outperform general and state-of-the-art models. The evaluation performed on two contrasting settings confirm the strength and robustness of the model and suggests two important factors in achieving high accuracy in the current task: 1) usage of sentence embeddings and 2) utilizing the linguistic structure of humor in designing the proposed model.