@inproceedings{lee-etal-2023-mafid,
title = "{MAF}i{D}: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data",
author = "Lee, Sung-Min and
Park, Eunhwan and
Seo, Daeryong and
Jeon, Donghyeon and
Kang, Inho and
Na, Seung-Hoon",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.177",
doi = "10.18653/v1/2023.findings-eacl.177",
pages = "2337--2344",
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 Moving Average Equipped Fusion-in-Decoder (\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.",
}
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<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 Moving Average Equipped Fusion-in-Decoder (MAFiD). With FiD as the backbone architecture, MAFiD combines various levels of reasoning: independent encoding of homogeneous data and single-row and multi-row heterogeneous reasoning, using a 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.</abstract>
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%0 Conference Proceedings
%T MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data
%A Lee, Sung-Min
%A Park, Eunhwan
%A Seo, Daeryong
%A Jeon, Donghyeon
%A Kang, Inho
%A Na, Seung-Hoon
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F lee-etal-2023-mafid
%X 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 Moving Average Equipped Fusion-in-Decoder (MAFiD). With FiD as the backbone architecture, MAFiD combines various levels of reasoning: independent encoding of homogeneous data and single-row and multi-row heterogeneous reasoning, using a 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.
%R 10.18653/v1/2023.findings-eacl.177
%U https://aclanthology.org/2023.findings-eacl.177
%U https://doi.org/10.18653/v1/2023.findings-eacl.177
%P 2337-2344
Markdown (Informal)
[MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data](https://aclanthology.org/2023.findings-eacl.177) (Lee et al., Findings 2023)
ACL