| Challenge: | Existing question answering systems rely on raw text and structured knowledge graphs. |
| Approach: | They build an end-to-end system to answer multiple choice questions with semi-structured tables as its knowledge. |
| Outcome: | The proposed system improves on the state-of-the-art question answering system with tabMCQ dataset. |
Similar Papers
Joint Models for Answer Verification in Question Answering Systems (2021.acl-long)
Copied to clipboard
| Challenge: | Using a joint approach, we found that the model is more efficient than those developed in machine reading (MR) work. |
| Approach: | They propose a joint model for selecting correct answer sentences among the top k provided by answer sentence selection modules. |
| Outcome: | The proposed model improves on WikiQA, TREC-QA, and a real-world dataset. |
A Graph Representation of Semi-structured Data for Web Question Answering (2020.coling-main)
Copied to clipboard
| Challenge: | Existing studies treat semi-structured data as flat documents with pieces of text . semi-structural data is more effective to represent rich relational information . question answering is an important feature in most search engines . |
| Approach: | They propose a graph representation of Web tables and lists based on categorization of components and their relations . they also develop reasoning techniques on the graph model for the question answering task . |
| Outcome: | The proposed graph improves F1 score by 3.90 points over the state-of-the-art baselines on real datasets. |
CNN for Text-Based Multiple Choice Question Answering (P18-2)
Copied to clipboard
| Challenge: | Existing models for text-based multiple choice question answering are based on a text. |
| Approach: | They propose a Convolutional Neural Network (CNN) model for text-based multiple choice question answering where questions are based on a particular article. |
| Outcome: | The proposed model outperforms several baseline models on the SciQ and TQA datasets. |
Question Answering with Long Multiple-Span Answers (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing QA systems for question answering are limited by the availability of annotated datasets. |
| Approach: | They propose a dataset for question-answering that extracts information from multiple parts of text . they propose QA-based multi-span neural architecture that captures relevance among multiple answer spans . |
| Outcome: | The proposed model outperforms state-of-the-art QA models in this multi-span QA setting. |
Simple and Effective Semi-Supervised Question Answering (N18-2)
Copied to clipboard
| Challenge: | Existing deep learning systems for extractive Question Answering are limited and expensive to construct. |
| Approach: | They propose a semi-supervised QA system where end user specifies a set of documents and only a few labelled examples. |
| Outcome: | The proposed system achieves 50% F1 score on SQuAD and TriviaQA with very little labeled data. |
Question-Answer Sentence Graph for Joint Modeling Answer Selection (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing approaches to automate Question Answering (QA) are graph-based and can target large text databases. |
| Approach: | They propose graph-based approaches for Answer Sentence Selection (AS2) . they train and integrate state-of-the-art (SOTA) models for computing scores . |
| Outcome: | The proposed approach outperforms baseline models on academic benchmarks and a real-world dataset on unseen queries. |
Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation (2026.acl-long)
Copied to clipboard
| Challenge: | Table Question Answering (TQA) aims to answer natural language questions using tabular data. |
| Approach: | They propose a systematic overview of TQA research using large language models and summarize available benchmarks based on task features. |
| Outcome: | The proposed framework provides a comprehensive overview of the current state of the art in the field of Table Question Answering. |
FeTaQA: Free-form Table Question Answering (2022.tacl-1)
Copied to clipboard
Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Hailey Schoelkopf, Riley Kong, Xiangru Tang, Mutethia Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev, Dragomir Radev
| Challenge: | Existing table-based question answering datasets lack advanced information-based questions that require reasoning and integration of information pieces retrieved from structured knowledge sources. |
| Approach: | They propose a dataset with 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs that can be used to generate an answer. |
| Outcome: | The proposed dataset has 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs. |
API-Assisted Code Generation for Question Answering on Varied Table Structures (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to table question answering have been limited to specific table structures. |
| Approach: | They propose a unified TableQA framework that uses Python as a querying language and few-shot prompting to translate NL questions into Python programs. |
| Outcome: | The proposed framework provides a unified representation for structured tables as multi-index Pandas data frames and uses Python as a powerful querying language to translate NL questions into Python programs. |
SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Text-to-SQL parsing and end-to end question answering have yet to be compared and their synergy remains unexplored. |
| Approach: | They propose a Synergistic Table-based Question Answering approach that integrates different models via answer selection. |
| Outcome: | The proposed approach improves on multiple benchmarks and on large scale datasets. |