ABML: attention-based multi-task learning for jointly humor recognition and pun detection

Ren, Lu; Xu, Bo; Lin, Hongfei; Yang, Liang

doi:10.1007/s00500-021-06136-y

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…ABML [ 10 ]: The model unifies the two highly pertinent tasks, including the humor recognition and pun detection. In the ABML model, they design a co-encoder module to capture the common features between the two tasks by weight sharing.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

“…However, manually constructing the features needed for humor recognition is difficult and laborious. With the development of deep-learning-based approaches, a number of methods have been proposed in recent work; for example, Kumar et al [ 8 ] propose a combination of convolutional neural networks (CNN) and long short-term memory (LSTM), with the addition of a highway to enhance performance; Weller et al [ 9 ] proposed the use of the transformer architecture to take advantage of its learning from the context of sentences; Lu Ren et al [ 10 ] proposed to combine humor recognition and pun recognition, training the two tasks jointly, thus enhancing performance. There is also humor recognition through multimodal means [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Multidimensional Latent Semantic Networks for Text Humor Recognition

Xiong

Wang

Huang

et al. 2022

Sensors

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Humor is a special human expression style, an important “lubricant” for daily communication for people; people can convey emotional messages that are not easily expressed through humor. At present, artificial intelligence is one of the popular research domains; “discourse understanding” is also an important research direction, and how to make computers recognize and understand humorous expressions similar to humans has become one of the popular research domains for natural language processing researchers. In this paper, a humor recognition model (MLSN) based on current humor theory and popular deep learning techniques is proposed for the humor recognition task. The model automatically identifies whether a sentence contains humor expression by capturing the inconsistency, phonetic features, and ambiguity of a joke as semantic features. The model was experimented on three publicly available wisecrack datasets and compared with state-of-the-art language models, and the results demonstrate that the proposed model has better humor recognition accuracy and can contribute to the research on discourse understanding.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multidimensional Latent Semantic Networks for Text Humor Recognition

Xiong

Wang

Huang

et al. 2022

Sensors

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“…More recently, following the general trends on many different NLP tasks, the current state-of-theart in this task is achieved by Deep Learning (Ren et al, 2021;Kumar et al, 2022) and LLMs (Devlin et al, 2019;Weller and Seppi, 2019).…”

Section: Humor Recognitionmentioning

confidence: 99%

What do Humor Classifiers Learn? An Attempt to Explain Humor Recognition Models

Inácio,

Wick-pedro,

Goncalo Oliveira

2023

Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities An

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Towards computational systems capable of dealing with complex and general linguistic phenomena, it is essential to understand figurative language, which verbal humor is an instance of. This paper reports state-of-the-art results for Humor Recognition in Portuguese, specifically, an F1-score of 99.6% with a BERTbased classifier. However, following the surprising high performance in such a challenging task, we further analyzed what was actually learned by the classifiers. Our main conclusions were that classifiers based on contentfeatures achieve the best performance, but rely mostly on stylistic aspects of the text, not necessarily related to humor, such as punctuation and question words. On the other hand, for humor-related features, we identified some important aspects, such as the presence of named entities, ambiguity and incongruity.

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Section: Humor Recognitionmentioning

confidence: 99%

Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

2023

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This paper investigates the use of standard and non-standard adverbial markers in modern Chinese literature. In Chinese, adverbials can be derived from many adjectives, adverbs and verbs with the suffix "de". The suffix has a standard and a non-standard written form, both of which are frequently used. Contrastive research on these two competing forms has mostly been qualitative or limited to small text samples. In this first large-scale quantitative study, we present statistics on 346 adverbial types from an 8-million-character text corpus drawn from Chinese literature in the 20th century. We present a semantic analysis of the verbs modified by adverbs with standard and non-standard markers, and a chronological analysis of marker choice among six prominent modern Chinese authors. We show that the non-standard form is more frequently used when the adverbial modifies an emotion verb. Further, we demonstrate that marker choice is correlated to text genre and register, as well as the writing style of the author.

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ABML: attention-based multi-task learning for jointly humor recognition and pun detection

Cited by 7 publications

References 36 publications

Multidimensional Latent Semantic Networks for Text Humor Recognition

Multidimensional Latent Semantic Networks for Text Humor Recognition

What do Humor Classifiers Learn? An Attempt to Explain Humor Recognition Models

Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

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