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Found 7 papers, 2 papers with code
Using Causality for Enhanced Prediction of Web Traffic Time Series
To leverage these relationships for improved web service traffic prediction, we propose an effective neural network module, CCMPlus, designed to extract causal relationship features across services.
A Generic Method for Fine-grained Category Discovery in Natural Language Texts
To address these shortcomings, we introduce a method that successfully detects fine-grained clusters of semantically similar texts guided by a novel objective function.
Graph-to-Text Generation with Dynamic Structure Pruning
Most graph-to-text works are built on the encoder-decoder framework with cross-attention mechanism.
Learning Explicit User Interest Boundary for Recommendation
The core objective of modelling recommender systems from implicit feedback is to maximize the positive sample score $s_p$ and minimize the negative sample score $s_n$, which can usually be summarized into two paradigms: the pointwise and the pairwise.
Improving Encoder by Auxiliary Supervision Tasks for Table-to-Text Generation
However, it is hard for a vanilla encoder to capture these.
Ranked #1 on
Table-to-Text Generation
on RotoWire
Learning Better Representation for Tables by Self-Supervised Tasks
Secondly, the target texts in training dataset may contain redundant information or facts do not exist in the input tables.
FC2RN: A Fully Convolutional Corner Refinement Network for Accurate Multi-Oriented Scene Text Detection
Accurate detection of multi-oriented text with large variations of scales, orientations, and aspect ratios is of great significance.
