Summary of Meant: Multimodal Encoder For Antecedent Information, by Benjamin Iyoya Irving et al.
MEANT: Multimodal Encoder for Antecedent Information
by Benjamin Iyoya Irving, Annika Marie Schoene
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper introduces a new multimodal model, called MEANT, designed to process temporal data that consists of multiple information types. Specifically, it focuses on stock market data, combining price, tweets, and graphical information. The authors create a new dataset, TempStock, which includes over a million tweets from S&P 500 companies. They find that MEANT improves performance by over 15% compared to existing baselines and that textual information has a significant impact on the time-dependent task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about using different types of data together, like prices, tweets, and pictures, to make predictions about the stock market. It creates a new way to process this kind of data, called MEANT, and tests it with a huge dataset that includes over a million tweets from big companies. The results show that using all these different types of information together can help predict the stock market better than just using one type of information. |
