MEANT: Multimodal Encoder for Antecedent Information

Anonymous

Abstract: The stock market provides a rich well of information that can be split across modalities, which makes it an ideal candidate for multimodal evaluation. Multimodal data plays an increasingly important role in the development of machine learning and has shown to positively impact performance. But information can do more than exist across modes--- it can exist across time. How should we attend to temporal data that consists of multiple information types? This work introduces (i) the MEANT model, a Multimodal Encoder for Antecedent information and (ii) a new dataset called TempStock. TempStock consists of price, Tweets, and graphical data with over a million Tweets from all of the companies in the S\&P 500 Index. We find that MEANT improves performance on existing baselines by over 15\%, and that the textual information affects performance far more than the visual information on our time-dependent task from our ablation study.

Paper Type: long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models, Data resources

Languages Studied: English