AI-Accelerated Product Development
FeTaQA: A new dataset for training QA systems for tabular data
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Enables readers to understand the advancements in the field of QA systems for tabular data. Also, compares the different datasets available for the same.
Potential Use Cases
Question Answering system for tables
Who is This For ?
People interested in knowing how to train models for performing QA over tabules.
Click on each of the following
to see details.
1. Intro to Automated Question Answering
Introduction to QA systems?
2. QA over text
What is QA over text?
How to use BERT for QA systems?
3. QA over semi structured data
How does QA over table generally work?
How does TaPaS work?
4. A survey on Semantic Parsing techniques
What is Semantic parsing task?
How is semantic task applicable to QA over tabular data?
5. Issues with Semantic Parsing using logical formalism
What is label bias problem?
6. Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs
What are weak supervision signals that are used in semantic parsers?
What are the problems with denotations ?
7. FeTaQA : A Free form Table Question Answering dataset
What is the difference between Short form QA and Free form QA systems?
What are the datasets available for training models for QA systems ?
What are the data collection and annotation strategies used ?
What are the different modelling strategies used to test the performance on FeTaQA dataset?