Papers with HybridQA
HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data (2020.findings-emnlp)
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| Challenge: | Existing question answering datasets focus on dealing with homogeneous information, but using homogenous information alone might lead to coverage problems. |
| Approach: | They propose a large-scale question-answering dataset that requires reasoning on heterogeneous information. |
| Outcome: | The proposed model can achieve an EM score of 40% while the existing model is far behind human performance. |
S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering (2023.acl-short)
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| Challenge: | Existing question answering systems use a retriever-reader framework to answer multi-hop questions . existing models lack retrieval, selector, and reasoner capabilities . |
| Approach: | They propose a three-stage text tableQA framework which comprises of retriever, selector, and reasoner. |
| Outcome: | The proposed framework outperforms baseline methods in the few-shot setting and ranks first on the HybridQA leaderboard. |
MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data (2023.findings-eacl)
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| Challenge: | Experimental results show that Transformer-based questions have a "long" hybrid sequence over tabular and textual elements, causing long-range reasoning problems. |
| Approach: | They propose a moving average-equipped fusion-in-decoder to handle long-range reasoning problems . they use FiD and EMA to combine different levels of reasoning . |
| Outcome: | Experimental results show that the proposed model increases exact matching and F1 by 1.1 and 1.7 on the blind test set. |
TACR: A Table Alignment-based Cell Selection Method for HybridQA (2023.findings-acl)
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| Challenge: | Hybrid Question-Answering datasets lack a robust reasoning model for text-based QA. |
| Approach: | They propose a table-question-alignment-based cell-selection and reasoning model for hybrid text and table QA. |
| Outcome: | The proposed model outperforms baselines on HybridQA and WikiTableQuestions datasets on cell selection and argumentation. |
Multi-Row, Multi-Span Distant Supervision For Table+Text Question Answering (2023.acl-long)
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Vishwajeet Kumar, Yash Gupta, Saneem Chemmengath, Jaydeep Sen, Soumen Chakrabarti, Samarth Bharadwaj, Feifei Pan
| Challenge: | Existing question answering systems for tables and linked text are relatively unexplored. |
| Approach: | They propose a transformer-based question answering system that copes with distant supervision along both axes of the question and answer. |
| Outcome: | The proposed system beats baselines for HybridQA and OTT-QA with best EM and F1 scores on a held out test set. |
Unsupervised Multi-hop Question Answering by Question Generation (2021.naacl-main)
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| Challenge: | Existing training data for multi-hop question answering (QA) is time-consuming and resource-intensive. |
| Approach: | They propose an unsupervised framework that generates human-like multi-hop training data from homogeneous and heterogeneously data sources. |
| Outcome: | The proposed framework achieves 61% and 83% of the supervised learning performance for the HybridQA and HotpotQA datasets. |
DRAMA: Dynamic Multi-Granularity Graph Estimate Retrieval over Tabular and Textual Question Answering (2024.lrec-main)
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| Challenge: | TableTextQA tasks require tabular and textual data, gaining increasing attention . however, row-based approaches suffer from limitations such as lack of interaction between rows . |
| Approach: | They propose a method that incorporates an interaction mechanism among multiple rows . Empirical results demonstrate that the proposed method is effective . |
| Outcome: | Empirical results show that the proposed model is effective on tabFact and HybridQA datasets. |
Exploring Hybrid Question Answering via Program-based Prompting (2024.acl-long)
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| Challenge: | Existing approaches to question answering over heterogeneous data are limited due to large scale of information and organic coupling of heterogenous data. |
| Approach: | They propose a program-based prompting framework for hybrid question answering tasks . it integrates various functions to perform hybrid information-seeking over data . |
| Outcome: | The proposed framework surpasses baseline systems and achieves the best performance under the fewshot settings. |
MATE: Multi-view Attention for Table Transformer Efficiency (2021.emnlp-main)
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| Challenge: | Tables are ubiquitous on the web, and are rich in information. |
| Approach: | They propose a sparse-attention Transformer architecture for modeling documents that contain large tables. |
| Outcome: | The proposed architecture scales linearly with respect to speed and memory, and can handle documents containing more than 8000 tokens with current accelerators. |
SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA (2023.emnlp-main)
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| Challenge: | In-Context Learning with Large Language Models (LLMs) has shown great performance on reasoning tasks. |
| Approach: | They propose a method for selecting a set of exemplars that is representative and diverse. |
| Outcome: | The proposed method outperforms existing methods on FinQA and TAT-QA on hybrid questions. |