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Found 6 papers, 5 papers with code
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension
Recent machine reading comprehension datasets such as ReClor and LogiQA require performing logical reasoning over text.
TSQA: Tabular Scenario Based Question Answering
To solve the task, we extend state-of-the-art MRC methods with TTGen, a novel table-to-text generator.
TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching
Although neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data.
SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge Bases
This dedicated coarse-grained formalism with a BERT-based parsing algorithm helps to improve the accuracy of the downstream fine-grained semantic parsing.
Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation
In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while altering the content.
Reading Comprehension with Graph-based Temporal-Casual Reasoning
Complex questions in reading comprehension tasks require integrating information from multiple sentences.
