# Integrating extracted information from bert and multiple embedding methods with the deep neural network for humour detection

arXiv: https://arxiv.org/abs/2105.05112 · HTML: https://mlsanity.com/paper/2105.05112/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [A Large Self-Annotated Corpus for Sarcasm](https://mlsanity.com/paper/1704.05579/) (2017)
- ["President Vows to Cut <Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines](https://mlsanity.com/paper/1906.00274/) (2019)
- [SemEval-2020 Task 7: Assessing Humor in Edited News Headlines](https://mlsanity.com/paper/2008.00304/) (2020)
- [Who's to say what's funny? A computer using Language Models and Deep Learning, That's Who!](https://mlsanity.com/paper/1705.10272/) (2017)
- [Applying a Pre-trained Language Model to Spanish Twitter Humor Prediction](https://mlsanity.com/paper/1907.03187/) (2019)

## Abstract (arXiv)

Humour detection from sentences has been an interesting and challenging task in the last few years. In attempts to highlight humour detection, most research was conducted using traditional approaches of embedding, e.g., Word2Vec or Glove. Recently BERT sentence embedding has also been used for this task. In this paper, we propose a framework for humour detection in short texts taken from news headlines. Our proposed framework (IBEN) attempts to extract information from written text via the use of different layers of BERT. After several trials, weights were assigned to different layers of the BERT model. The extracted information was then sent to a Bi-GRU neural network as an embedding matrix. We utilized the properties of some external embedding models. A multi-kernel convolution in our neural network was also employed to extract higher-level sentence representations. This framework performed very well on the task of humour detection.