“Cheese!”: a Corpus of Face-to-face French Interactions. A Case Study for Analyzing Smiling and Conversational Humor (2020.lrec-1)
Copied to clipboard
| Challenge: | Cheese! is a conversational corpus containing 11 mixed and non-mixed dyadic interactions lasting around 15 minutes each. |
| Approach: | They propose to use a conversational corpus to compare smiling behavior in American English and French conversations to conduct a cross-cultural comparison. |
| Outcome: | The proposed study examines the relationship between smile and humor in conversational interactions between American English and French participants. |
Similar Papers
PACO: a Corpus to Analyze the Impact of Common Ground in Spontaneous Face-to-Face Interaction (2020.lrec-1)
Copied to clipboard
| Challenge: | PAC0 is a conversational corpus of 15 face-to-face interactions lasting around 20 min each. |
| Approach: | They have created a conversational corpus of 15 face-to-face dyadic interactions lasting around 20 min each. |
| Outcome: | The compared corpus consists of 15 face-to-face dyadic interactions lasting around 20 min each. |
HAHA 2019 Dataset: A Corpus for Humor Analysis in Spanish (2020.lrec-1)
Copied to clipboard
| Challenge: | 30,000 Spanish tweets were crowd-annotated with humor value and funniness score . the corpus contains approximately 38.6% of humorous tweets with an average score of 2.04 in a scale from 1 to 5 for the humorous tweet. |
| Approach: | They develop a corpus of 30,000 Spanish tweets crowd-annotated with humor value and funniness score. |
| Outcome: | The results obtained from the 30,000 tweets in the Spanish language are encouraging. |
StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a new multimodal dataset of stand-up comedies is proposed to improve humor detection . the dataset is the biggest available for this type of task, and the most diverse . |
| Approach: | They propose a method to enhance the automatic laughter detection based on Audio Speech Recognition errors. |
| Outcome: | The proposed method improves existing models of humor detection by using audio speech recognition errors. |
Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes (D19-1)
Copied to clipboard
| Challenge: | Humor plays important role in human communication, which makes it important problem for natural language processing. |
| Approach: | They propose a novel annotation scheme to give scenarios of how humor arises in text . they report reasonable agreement between annotators and analyze the dataset . |
| Outcome: | The proposed scheme gives scenarios of how humor arises in text . it contains key words that trigger humor, character relationship, scene, and humor categories . |
Humor Detection in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System (L18-1)
Copied to clipboard
| Challenge: | a growing number of social media users are using code-mixing to detect humor . linguistics researchers are looking for methods to detect humorous content in text . |
| Approach: | They analyze a corpus of English-Hindi code-mixed tweets annotated with humorous(H) tags. |
| Outcome: | The proposed method detects humor in code-mixed tweets in English-Hindi. |
MUCH: A Multimodal Corpus Construction for Conversational Humor Recognition Based on Chinese Sitcom (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing multimodal corpora for conversational humor are coarse-grained and insufficient to support the conversational comprehension task. |
| Approach: | They constructed a multimodal humor corpus based on a Chinese sitcom and used both unimodal and multimodal methods to test the corpus. |
| Outcome: | The proposed method outperforms unimodal and multimodal methods in the evaluation of a Chinese sitcom for conversational humor recognition. |
Corpora and Baselines for Humour Recognition in Portuguese (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing work on the recognition of verbal humour in Portuguese has not been done . humor recognition is a sign of fluency in a language, and is not yet widely used in other languages. |
| Approach: | They propose to create three corpora covering two styles of humour and four sources of non-humorous text that are used for testing computational models. |
| Outcome: | The proposed models can be used to train and test models in Portuguese, and may be used as baselines for future projects. |
Large Dataset and Language Model Fun-Tuning for Humor Recognition (P19-1)
Copied to clipboard
| Challenge: | Humor recognition datasets contain only English texts and focus on puns. |
| Approach: | They collected a dataset of jokes and funny dialogues in Russian and complemented them carefully with unfunny texts with similar lexical properties. |
| Outcome: | The proposed method is based on the universal language model finetuning and has an F1 score of 0.91 on a test set. |
Development and Validation of a Corpus for Machine Humor Comprehension (2020.lrec-1)
Copied to clipboard
| Challenge: | a Chinese humor corpus was labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. |
| Approach: | They develop a Chinese humor corpus with 3,365 jokes labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. |
| Outcome: | The proposed corpus contains 3,365 jokes from over 40 sources. |
Construction of English-French Multimodal Affective Conversational Corpus from TV Dramas (L18-1)
Copied to clipboard
| Challenge: | Existing technologies for speech recognition and speech synthesis focus on non-verbal content and paralinguistic information. |
| Approach: | They propose to construct a multimodal affective conversational corpus based on TV dramas . their data contain parallel English-French languages in lexical, acoustic, and facial features . |
| Outcome: | The proposed corpus can be used to assess speech recognition, speech recognition and synthesis, linguistic, and paralinguistic speech-to-speech translation and multimodal dialog systems. |