Humor Detection: A Transformer Gets the Last Laugh

Weller, Orion; Seppi, Kevin

doi:10.18653/v1/d19-1372

Cited by 79 publications

(52 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Transformer architectures trained on language modeling have been recently adapted to downstream tasks demonstrating state-of-the-art performance (Weller and Seppi, 2019;Gupta and Durrett, 2019;Maronikolakis et al, 2020). In this paper, we adapt and subsequently combine transformers with external linguistic information for complaint prediction.…”

Section: Transformer-based Modelsmentioning

confidence: 99%

Complaint Identification in Social Media with Transformer Networks

Jin¹,

Αλέτρας²

2020

Proceedings of the 28th International Conference on Computational Linguistics

View full text Add to dashboard Cite

Complaining is a speech act extensively used by humans to communicate a negative inconsistency between reality and expectations. Previous work on automatically identifying complaints in social media has focused on using feature-based and task-specific neural network models. Adapting state-of-the-art pre-trained neural language models and their combinations with other linguistic information from topics or sentiment for complaint prediction has yet to be explored. In this paper, we evaluate a battery of neural models underpinned by transformer networks which we subsequently combine with linguistic information. Experiments on a publicly available data set of complaints demonstrate that our models outperform previous state-of-the-art methods by a large margin achieving a macro F1 up to 87.

show abstract

Section: Transformer-based Modelsmentioning

confidence: 99%

Complaint Identification in Social Media with Transformer Networks

Jin¹,

Αλέτρας²

2020

Proceedings of the 28th International Conference on Computational Linguistics

View full text Add to dashboard Cite

show abstract

“…Recently released models such as BERT (Devlin et al, 2018) and RoBERTa (Liu et al, 2020), exploit the use of pre-training and bidirectional transformers to enable efficient solutions obtaining state-of-the-art performance. Pre-trained embeddings significantly outperform the previous state-of-the-art in similar problems such as humor detection (Weller and Seppi, 2019), and subjectivity detection (Pant et al, 2020).…”

Section: Introductionmentioning

confidence: 98%

Sarcasm Detection using Context Separators in Online Discourse

Dadu¹,

Pant²

2020

Proceedings of the Second Workshop on Figurative Language Processing

View full text Add to dashboard Cite

Sarcasm is an intricate form of speech, where meaning is conveyed implicitly. Being a convoluted form of expression, detecting sarcasm is an assiduous problem. The difficulty in recognition of sarcasm has many pitfalls, including misunderstandings in everyday communications, which leads us to an increasing focus on automated sarcasm detection. In the second edition of the Figurative Language Processing (FigLang 2020) workshop, the shared task of sarcasm detection released two datasets, containing responses along with their context sampled from Twitter and Reddit.In this work, we use RoBERT a large to detect sarcasm in both the datasets. We further assert the importance of context in improving the performance of contextual word embedding based models by using three different types of inputs -Response-only, Context-Response, and Context-Response (Separated). We show that our proposed architecture performs competitively for both the datasets. We also show that the addition of a separation token between context and target response results in an improvement of 5.13% in the F1-score in the Reddit dataset.

show abstract

“…Early work on humor recognition (Mihalcea and Strapparava, 2005) proposed heuristic-based humor-specific stylistic features, for example alliteration, antonymy, and adult slang. More recent work (Yang et al, 2015;Chen and Soo, 2018;Weller and Seppi, 2019) regarded the problem as a text classification task, and adopted statistical machine learning methods and neural networks to train models on humor datasets. However, only few of the deep learning methods have tried to establish a connection between humor recognition and humor theories.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition

Xie¹,

Li²,

Pu³

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

View full text Add to dashboard Cite

Humor recognition has been widely studied as a text classification problem using data-driven approaches. However, most existing work does not examine the actual joke mechanism to understand humor. We break down any joke into two distinct components: the set-up and the punchline, and further explore the special relationship between them. Inspired by the incongruity theory of humor, we model the setup as the part developing semantic uncertainty, and the punchline disrupting audience expectations. With increasingly powerful language models, we were able to feed the set-up along with the punchline into the GPT-2 language model, and calculate the uncertainty and surprisal values of the jokes. By conducting experiments on the SemEval 2021 Task 7 dataset, we found that these two features have better capabilities of telling jokes from non-jokes, compared with existing baselines.

show abstract

Humor Detection: A Transformer Gets the Last Laugh

Cited by 79 publications

References 13 publications

Complaint Identification in Social Media with Transformer Networks

Complaint Identification in Social Media with Transformer Networks

Sarcasm Detection using Context Separators in Online Discourse

Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition

Contact Info

Product

Resources

About