Md Iftekhar Tanveer


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UR-FUNNY: A Multimodal Language Dataset for Understanding Humor
Md Kamrul Hasan | Wasifur Rahman | AmirAli Bagher Zadeh | Jianyuan Zhong | Md Iftekhar Tanveer | Louis-Philippe Morency | Mohammed (Ehsan) Hoque
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Humor is a unique and creative communicative behavior often displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (visual) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it has been understudied. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research.


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SyntaViz: Visualizing Voice Queries through a Syntax-Driven Hierarchical Ontology
Md Iftekhar Tanveer | Ferhan Ture
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

This paper describes SyntaViz, a visualization interface specifically designed for analyzing natural-language queries that were created by users of a voice-enabled product. SyntaViz provides a platform for browsing the ontology of user queries from a syntax-driven perspective, providing quick access to high-impact failure points of the existing intent understanding system and evidence for data-driven decisions in the development cycle. A case study on Xfinity X1 (a voice-enabled entertainment platform from Comcast) reveals that SyntaViz helps developers identify multiple action items in a short amount of time without any special training. SyntaViz has been open-sourced for the benefit of the community.