MultiComp 2018 - First Workshop on Computational Modeling of Human Multimodal Language

Computational analysis of human multimodal language is an emerging research area in Natural Language Processing (NLP). It expands the horizons of NLP to study language used in face to face communication and in online multimedia. This form of language contains modalities of language (in terms of spoken text), visual (in terms of gestures and facial expressions) and acoustic (in terms of changes in the voice tone). At its core, this research area is focused on modeling the three modalities and their complex interactions. The first workshop on Computational Modeling of Human Multimodal Language aims to facilitate the growth of this new research direction in NLP community. A grand challenge on multimodal sentiment analysis and emotion recognition on the recently introduced CMU Multimodal Opinion Sentiment and Emotion Intensity dataset accompanies this workshop. The workshop will be held in conjunction with the 56th Annual Meeting of the Association for Computational Linguistics 2018.
Communicating using multimodal language (verbal and nonverbal) shares a significant portion of our communication including face-to-face communication, video chatting, and social multimedia opinion sharing. Hence, it’s computational analysis is centric to NLP research. The challenges of modeling human multimodal language can be split into two major categories: 1) studying each modality individually and modeling each in a manner that can be linked to other modalities (also known as intramodal dynamics) 2) linking the modalities by modeling the interactions between them (also known as intermodal dynamics). Common forms of these interactions include complementary or correlated information across modes. Intrinsic to each modality, modeling human multimodal language is complex due to factors such as idiosyncrasy in communicative styles, non-trivial alignment between modalities and unreliable or contradictory information across modalities. Therefore computational analysis becomes a challenging research area.
The focus of this workshop is on joint analysis of language (spoken text), vision (gestures and expressions) and acoustic (paralingustic) modalities. We seek the following types of submissions:
Grand challenge papers: Papers summarizing the research effort with the CMU-MOSEI shared task on multimodal sentiment analysis and emotion recognition. Grand challenge papers are 8 pages, including references.
Full and short papers: These papers are presenting substantial, original and unpublished research on human multimodal language. Full papers are up to 8 pages including references and short papers are 4 pages + 1 page for references.
Topics of interest for full and short papers include:
• Multimodal sentiment analysis
• Multimodal emotion recognition
• Multimodal affective computing
• Multimodal speaker traits recognition
• Dyadic multimodal interactions
• Multimodal dialogue modeling
• Cognitive modeling and multimodal interaction
• Statistical analysis of human multimodal language