M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations

Chauhan, Dushyant Singh; Singh, Gopendra Vikram; Majumder, Navonil; Zadeh, Amir; Ekbal, Asif; Bhattacharyya, Pushpak; Morency, Louis–Philippe; Poria, Soujanya

doi:10.1145/3462244.3479959

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…Twenty one (21) humor features have been carefully studied and research evidence of their use in humor computational detection has been presented. The review of the selected forty seven (47) papers exploited in this systematic literature review revealed that the application of different manning methods to the existing dataset results in diverse features.…”

Section: Featuresmentioning

confidence: 99%

“…A dataset is characterized as multimodal when it includes multiple such modalities. TV sitcoms are the most frequently used source for retrieving multimodal datasets by authors (Bertero and Fung 2016;Chauhan et al 2021;Yang et al 2019;Purandare and Litman 2006;Patro et al 2021;Bedi et al 2021). This is because SitCom (Situational Comedy) is a comedy genre that focuses on xed characters carried over many episodes that allow researchers to collect large scale datasets.…”

Section: Multimodalmentioning

confidence: 99%

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Computational Humor Recognition: A Systematic Literature Review

Kalloniatis

Adamidis

2023

Preprint

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Computational humor recognition is considered to be one of the hardest tasks in natural language processing (NLP) since humor is such a particularly complex emotion. There are very few recent studies that offer analysis of certain aspects of computational humor. However, there has been no attempt to study the empirical evidence on computational humor recognition in a systematic way. The aim of this research is to examine computational humor detection from four aspects: datasets, features and algorithms. Therefore, a Systematic Literature Review (SLR) is carried out to present in details the computational techniques for humor identification under these aspects. After posing some research questions, a total of 106 primary papers were recognized as relevant to the objectives of these questions and further detailed analysis was conducted. The study revealed that there is a great number of publicly available annotated humor datasets with many different types of humor instances. Twenty one (21) humor features have been carefully studied and research evidence of their use in humor computational detection is presented. Additionally, a classification of the humor detection approaches was performed and the results are submitted. Finally, the challenges of applying these techniques to humor recognition as well as promising future research directions are discussed.

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Section: Featuresmentioning

confidence: 99%

Section: Multimodalmentioning

confidence: 99%

Computational Humor Recognition: A Systematic Literature Review

Kalloniatis

Adamidis

2023

Preprint

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“…For Open Mic, Mittal et al [42] collected standup comedy recordings and used the audience's laughter to create annotations indicating the degree of humour on a scale from zero to four. Similar to text-only datasets, most multimodal datasets are in English, notable exceptions being the already mentioned MUMOR-ZH [19], MaSaC [40], the Chinese dataset used in [41] and M2H2 [43], which is based on a Hindi TV show.…”

Section: Multimodal Humour Recognitionmentioning

confidence: 99%

“…Besides, our dataset is the first to include annotations according to the HSQ. While other datasets are labelled automatically, for example by using canned laughter [18,38], or by three human annotators [19,43], each video in our database has been labelled by the same 9 annotators. In denotes the overall duration of each dataset, #Spk the number of speakers in it.…”

Section: Comparison With Other Humour Datasetsmentioning

confidence: 99%

Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results

Christ¹,

Amiriparian²,

Kathan³

et al. 2022

Preprint

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Humour is a substantial element of human affect and cognition. Its automatic understanding can facilitate a more naturalistic human-device interaction and the humanisation of artificial intelligence. Current methods of humour detection are solely based on staged data making them inadequate for 'real-world' applications. We address this deficiency by introducing the novel Passau-Spontaneous Football Coach Humour (Passau-SFCH) dataset, comprising of about 11 hours of recordings. The Passau-SFCH dataset is annotated for the presence of humour and its dimensions (sentiment and direction) as proposed in Martin's Humor Style Questionnaire. We conduct a series of experiments, employing pretrained Transformers, convolutional neural networks, and expert-designed features. The performance of each modality (text, audio, video) for spontaneous humour recognition is analysed and their complementarity is investigated. Our findings suggest that for the automatic analysis of humour and its sentiment, facial expressions are most promising, while humour direction can be best modelled via text-based features. The results reveal considerable differences among various subjects, highlighting the individuality of humour usage and style. Further, we observe that a decision-level fusion yields the best recognition result. Finally, we make our code publicly available at https://www.github.com/EIHW/passau-sfch. The Passau-SFCH dataset is available upon request.

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“…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 ]. Unlike these works, we propose a deep learning method approach based on humor linguistics to capture inconsistency features, phonetic features, and ambiguity features in humor, introducing the incongruity of humor caused by semantic inconsistency, lexical, and ambiguity.…”

Section: Introductionmentioning

confidence: 99%

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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M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations

Cited by 10 publications

References 25 publications

Computational Humor Recognition: A Systematic Literature Review

Computational Humor Recognition: A Systematic Literature Review

Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results

Multidimensional Latent Semantic Networks for Text Humor Recognition

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