Summary of Multimodal Contextualized Semantic Parsing From Speech, by Jordan Voas et al.
Multimodal Contextualized Semantic Parsing from Speech
by Jordan Voas, Raymond Mooney, David Harwath
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce Semantic Parsing in Contextual Environments (SPICE), a task that enhances artificial agents’ contextual awareness by integrating multimodal inputs with prior contexts. This goes beyond traditional semantic parsing by offering a structured framework for dynamically updating an agent’s knowledge with new information, mirroring human communication complexity. The VG-SPICE dataset is developed to challenge agents with visual scene graph construction from spoken conversational exchanges, highlighting the importance of speech and visual data integration. A model called Audio-Vision Dialogue Scene Parser (AViD-SP) is also presented for use on VG-SPICE. These innovations aim to improve multimodal information processing and integration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial agents are getting better at understanding what we say, but they still struggle with context. Imagine having a conversation with a friend while cooking dinner – the agent would need to understand not just your words, but also the fact that you’re holding a spatula and standing in front of the stove. This paper introduces a new task called SPICE that helps agents do just that. It’s like a game where agents have to use what they already know to figure out what someone is saying, based on visual clues like pictures or videos. The researchers created a special dataset to test this idea and developed a model to work with it. The goal is to make agents better at understanding our language and actions in different situations. |
Keywords
» Artificial intelligence » Semantic parsing
