TACR : A Table Alignment-based Cell Selection Method for H ybrid QA (2023)

Abstract: Hybrid Question-Answering (HQA), which targets reasoning over tables and passages linked from table cells, has witnessed significant research in recent years. A common challenge in HQA and other passage-table QA datasets is that it is generally unrealistic to iterate over all table rows, columns, and linked passages to retrieve evidence. Such a challenge made it difficult for previous studies to show their reasoning ability in retrieving answers. To bridge this gap, we propose a novel Table-alignment-based Cell-selection and Reasoning model (TACR) for hybrid text and table QA, evaluated on the HybridQA and WikiTableQuestions datasets. In evidence retrieval, we design a table-question-alignment enhanced cell-selection method to retrieve fine-grained evidence. In answer reasoning, we incorporate a QA module that treats the row containing selected cells as context. Experimental results over the HybridQA and WikiTableQuestions (WTQ) datasets show that TACR achieves state-of-the-art results on cell selection and outperforms fine-grained evidence retrieval baselines on HybridQA, while achieving competitive performance on WTQ. We also conducted a detailed analysis to demonstrate that being able to align questions to tables in the cell-selection stage can result in important gains from experiments of over 90% table row and column selection accuracy, meanwhile also improving output explainability.

## introduction text-based question-answering datasets derive answers based on reasoning over given passages @xcite @xcite , while table-based qa datasets collect tables from sources such as wikitables @xcite @xcite . however, datasets combining textual passages and tables, like hybridqa (chen * indicates equal contribution. et al., 2020b), ott-qa @xcite , and tat-qa @xcite are more realistic benchmarks. as the answer to a given question may come from either table cells or linked passages, current hybrid qa models usually consist of two components, a retriever to learn evidence and a reasoner to leverage the evidence to derive answers. such models retrieve evidence from different granularities, coarse-grained (e.g., row or column) or fine-grained (e.g., cell), and directly use a spanbased reading comprehension model to reason the answer. @xcite , for example, chooses coarse-grained regions as evidence, e.g., a table row. @xcite and @xcite , however, focus on fine-grained units, table cells and linked passages. to preserve the advantages and eliminate the disadvantages of differentgranularity evidence, @xcite propose muger, 2 which performs multi-granularity evidence retrieval and answer reasoning. @xcite conducts extensive experiments to prove that a coarse-grained retriever contributes less than a fine-grained retriever. moreover, fine-grained methods, although giving an exact position of candidate cells, fail to illustrate why the selected cells are chosen, while our method is based on row and column selection probabilities. we thus further extend the fine-grained method by aligning questions with tables, letting our approach know which parts of questions are accounted for by which modalities. intuitively, multi-hop questions in the text-table qa task usually contain two pieces of information from different modalities, tables and passages. moreover, tables and passages are connected with evidence contained in tabular data. our method implicitly decomposes the questions for different modalities to locate evidence and improve cell-selection accuracy. as illustrated in figure 1 , an example from the hybridqa dataset shows how humans work on figure 1 : example from the hybridqa dataset. the top sentence is the original question, and words in different colors show different parts of questions required for reasoning in different modalities. the two headers in blue-dashed boxes are column names aligned with the given question. tacr first uses a method based on table-questionalignment to align the original question with table columns to help obtain golden table cells and then retrieve the final answer based on linked passages. multi-hop and multi-modal qa tasks. the original question "what is the middle name of the player with the second most national football league career rushing yards ?" can be divided into two parts, "what is the middle name of" and "the player with the second most national football league career rushing yards?" for passages and tables, respectively. such sub-questions are connected with the evidence of a cell ( "walter payton"). for humans, we first locate who was the player in the second rank, which requires information from two columns: "rank" and "player". after locating the cell, we can finally determine walter payton's middle name from the passage. such reasoning process inspired us to develop tacr, a table-alignmentbased cell-selection and reasoning model, which incorporates a fine-grained evidence-retrieval module that utilizes table-question-alignment to learn which parts of the question are used for retrieving evidence from different modalities and reasoning towards answers. to explicitly and correctly show the reasoning process in the text-table qa task, in the evidence retrieval stage, tacr first selects the golden cells and avoids redundant information in multi-granularity evidence that would lower the performance of the answer-reasoning module. the table-cellselection module of tacr is designed to navigate the fine-grained evidence for the reader by fusing well-learned information from the table-questionalignment module. compared with current finegrained retrievers, the table-question-alignment module of tacr can help our model learn which parts of questions are used for reasoning in which modality, and which parts of tables contain candidate cells. together with the alignment module, tacr preserves both high golden cell-selection accuracy and shows competitive performance on the hybridqa and wikitablequestions (wtq) datasets, while providing improved explainability. our contributions are as follows: (1) tacr is the first model able to explicitly show its reasoning process in the passage-table qa task; (2) we jointly train the cell-selection and table-question alignment modules to improve golden cell selection performance and preserve the qa reader's performance; and (3) we conduct extensive experiments on the hybridqa and wtq datasets to demonstrate the effectiveness of tacr. ## framework as described in the previous section, both coarseand fine-grained approaches fail to provide a reasoning process showing which parts of multi-hop questions map to which modality and evidence. here we describe the details of tacr and its three main components: (1) data augmentation for training the table-question alignment module; (2) a multi-task learning module for table-question alignment and table-cell-selection; and (3) a text-based multi-hop qa module for retrieving answers. figure 2 shows the overall architecture of tacr. ## conclusion this paper presents tacr, a