MAF i D : Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data (2023)

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.

## introduction while most studies have focused on text question answering (qa), where unimodal textual passages are provided as a source of evidence for an answer @xcite @xcite @xcite @xcite , realistic questions often need to refer to "heterogeneous" evidences based on both tabular and textual contents to generate an answer, motivating researchers to address table-and-text qa @xcite @xcite @xcite . among the various tasks for table-and-text qa, we address the multi-hop table-and-text qa described in hybridqa @xcite , which is a large-scale table-and-text qa dataset focusing on the multi-hop reasoning across tabular and textual contents to extract an answer. ## related works recently, many datasets such as hybridqa @xcite , ott-qa (wenhu chen, 2021), mul-timodalqa @xcite , hybridialogue @xcite , and tat-qa @xcite have been presented for table-and-text qa. various works on table-and-text qa have enhanced "pretraining" to strengthen the cross-modal matching and numerical reasoning, by learning on tables and texts jointly @xcite and exclusively on tables @xcite . to handle the long range reasoning on table-andtext qa, early works employed "efficient" transformers based on sparse attention with selective attention masks, such as the longformer @xcite in the work of @xcite and etc @xcite in the work of (wenhu chen, 2021). mate @xcite uses structure-based sparse attention that attends to either rows or columns in a given table . recently, truncation-based approaches have been employed in mitqa @xcite where the passage filter module is additionally applied such that only the filtered passages are used as textual contents of a table's row. compared to these existing approaches, which rather limitedly reduce the computational cost in the encoder part, mafid significantly lightens the encoder part by minimizing the interaction between different rows and instead fuses the encoded representations in the decoder part under the framework of fid. equipped with the low-dimensional ema, mafid only performs the "shallow" interaction across rows, thus mostly maintaining the efficiency of the interaction-less encoder. ## moving average equipped fusion-in-decoder figure 1 shows the overall neural architecture of the proposed mafid model, which combines three types of representation. here, we present the details of the mafid components. ## conclusion in this paper, we address long range-reasoning for the multi-hop table-and-text qa and propose mafid, which extends fid by equipping ema and the gated cross-attention layer for the encoder and decoder parts, respectively, to design an effective way of combining various types of encoded representations. the experimental results on hy-bridqa showed that the proposed mafid achieved state-of-the-art performances in both the development and blind test sets. in future work, we will extend mafid to open-domain table-and-text qa and explore a unified approach that integrates single-row and multi-row reasoning.