Summary of Domain-independent Automatic Generation Of Descriptive Texts For Time-series Data, by Kota Dohi et al.
Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data
by Kota Dohi, Aoi Ito, Harsh Purohit, Tomoya Nishida, Takashi Endo, Yohei Kawaguchi
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 The proposed method generates domain-independent descriptive texts from time-series data, addressing the scarcity of annotated data. The approach involves two methods: forward and backward. A novel backward approach creates the Temporal Automated Captions for Observations (TACO) dataset. Experimental results show that a contrastive learning model trained on TACO can generate descriptive texts in new domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps with generating descriptions of time-series data, which is important because there’s not much labeled data available. The researchers came up with two ways to create pairs of time-series data and descriptions: going forward or backward. They created a special dataset called TACO using the backward approach. The results show that a certain type of AI model can learn from this dataset and generate descriptions for new kinds of time-series data. |
Keywords
» Artificial intelligence » Time series
