{"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/mafid-moving-average-equipped-fusion-in","title":"MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data","arxiv_id":null,"date":"2023-05-02","proceeding":"Conference 2023 5","authors":["Sung-Min Lee","Eunhwan Park","Daeryong Seo","Donghyeon Jeon","Inho Kang","Seung-Hoon Na"],"abstract":"Transformer-based models for question answering (QA) over tables and texts confront a “long” hybrid sequence over tabular and textual elements, causing long-range reasoning problems. To handle long-range reasoning, we extensively employ a fusion-in-decoder (FiD) and exponential moving average (EMA), proposing a {underline{M}oving {underline{A}verage Equipped {underline{F}usion-{underline{i}n-{underline{D}ecoder ({textbf{MAFiD}). With FiD as the backbone architecture, MAFiD combines various levels of reasoning: {textit{independent encoding} of homogeneous data and {textit{single-row} and {textit{multi-row heterogeneous reasoning}, using a {textit{gated cross attention layer} to effectively aggregate the three types of representations resulting from various reasonings. Experimental results on HybridQA indicate that MAFiD achieves state-of-the-art performance by increasing exact matching (EM) and F1 by $1.1$ and $1.7$, respectively, on the blind test set.","url_abs":"https://aclanthology.org/2023.findings-eacl.177/","url_pdf":"https://aclanthology.org/2023.findings-eacl.177.pdf","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":"mafid-moving-average-equipped-fusion-in","repo_url":"https://github.com/ZIZUN/MAFiD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-hybridqa","task":"Question Answering","dataset":"HybridQA","model":"MAFiD","rank_in_archive_order":1,"of":4,"metrics":{"ANS-EM":"65.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}