mlsanityData to 14 Oct 2026

!Qu'e maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baseline

arXiv 2105.05542 ↗First posted 12 May 2021cs.CLcited by 8

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Abstract (arXiv)

We construct the first ever multimodal sarcasm dataset for Spanish. The audiovisual dataset consists of sarcasm annotated text that is aligned with video and audio. The dataset represents two varieties of Spanish, a Latin American variety and a Peninsular Spanish variety, which ensures a wider dialectal coverage for this global language. We present several models for sarcasm detection that will serve as baselines in the future research. Our results show that results with text only (89%) are worse than when combining text with audio (91.9%). Finally, the best results are obtained when combining all the modalities: text, audio and video (93.1%).

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 10 of its 37 references that are in the map. First posted on arXiv 12 May 2021.

Builds on

10 of 37 references.

References · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018Deep contextualized word representationscited by 2,290
  3. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,026
  4. 2017OpenNMT: Open-source Toolkit for Neural Machine Translationcited by 251
  5. 2019Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)cited by 174
  6. 2020R3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledgecited by 32
  7. 2018Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenationcited by 21
  8. 2017Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translationcited by 18
  9. 2019Let's FACE it. Finnish Poetry Generation with Aesthetics and Framingcited by 11
  10. 2019Modelling the Socialization of Creative Agents in a Master-Apprentice Setting: The Case of Movie Title Punscited by 3

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Citing papers per year: 2022: 2; 2023: 0; 2024: 2; 2025: 4.

8 papers.