{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multimodal-automl-on-structured-tables-with","title":"Multimodal AutoML on Structured Tables with Text Fields","arxiv_id":null,"date":"2021-05-20","proceeding":"ICML Workshop AutoML 2021 7","authors":["Xingjian Shi","Jonas Mueller","Nick Erickson","Mu Li","Alex Smola"],"abstract":"We  design  automated  supervised  learning  systems  for  data  tables  that  not  only  contain numeric/categorical columns,  but  text  fields  as  well.  Here  we  assemble  15  multimodal data tables that each contain some text fields and stem from a real business application. Over this benchmark, we evaluate numerous multimodal AutoML strategies, including a standard two-stage approach where NLP is used to featurize the text such that AutoML for tabular data can then be applied. We propose various practically superior strategies based on multimodal adaptations of Transformer networks and stack ensembling of these networks with classical tabular models.  Beyond performing the best in our benchmark, our proposed (fully automated) methodology manages to rank 1st place (against human data scientists) when fit to the raw tabular/text data in two MachineHack prediction competitions  and 2nd place (out of 2380 teams) in Kaggle’s Mercari Price Suggestion Challenge.","url_abs":"https://openreview.net/forum?id=OHAIVOOl7Vl","url_pdf":"https://openreview.net/pdf?id=OHAIVOOl7Vl","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multimodal-automl-on-structured-tables-with","repo_url":"https://github.com/sxjscience/automl_multimodal_benchmark","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"automl","task_name":"AutoML"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}