Summary of Towards Causal Relationship in Indefinite Data: Baseline Model and New Datasets, by Hang Chen et al.
Towards Causal Relationship in Indefinite Data: Baseline Model and New Datasets
by Hang Chen, Xinyu Yang, Keqing Du
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research proposes a novel approach to integrating deep learning and causal discovery in indefinite data, characterized by multi-structure data and multi-value representations. The authors highlight the challenges posed by such data forms, including gaps in datasets and methods. To address these challenges, they release two high-quality datasets, Causalogue and Causaction, containing text dialogue samples and video action samples with causal annotations, respectively. They also propose a probabilistic framework as a baseline method to overcome the limitations of existing approaches. The proposed highlights include establishing causation conditions using independence of noise terms, treating causal strength as a latent variable, and estimating the effects of latent confounders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about finding patterns in dialogue and video data that are connected to each other. Right now, there’s no good way to do this because the data is complex and comes in different forms. To fix this, the authors created two new datasets with labeled examples of dialogue and video actions, and a new method called probabilistic framework. This method can handle the complexity of the data by using noise terms, treating causal strength as hidden information, and estimating the effects of unknown factors. The results show that this approach can identify patterns in the data better than existing methods. |
Keywords
* Artificial intelligence * Deep learning
