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Found 8 papers, 4 papers with code
Analytical Reasoning of Text
In this paper, we study the challenge of analytical reasoning of text and collect a new dataset consisting of questions from the Law School Admission Test from 1991 to 2016.
Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video
Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our case, the video content and its transcript.
Modeling Semantic Composition with Syntactic Hypergraph for Video Question Answering
A key challenge in video question answering is how to realize the cross-modal semantic alignment between textual concepts and corresponding visual objects.
AR-LSAT: Investigating Analytical Reasoning of Text
Analytical reasoning is an essential and challenging task that requires a system to analyze a scenario involving a set of particular circumstances and perform reasoning over it to make conclusions.
Syntax-Enhanced Pre-trained Model
We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa.
Neural Deepfake Detection with Factual Structure of Text
To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text.
Reasoning Over Semantic-Level Graph for Fact Checking
We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy.
Ranked #2 on
Fact Verification
on FEVER
A Deep Neural Information Fusion Architecture for Textual Network Embeddings
Textual network embeddings aim to learn a low-dimensional representation for every node in the network so that both the structural and textual information from the networks can be well preserved in the representations.
