RTE
27 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in RTE
GLUE
RTE
Most implemented papers
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models
google-research/language
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ACL 2020
Recent models for unsupervised representation learning of text have employed a number of techniques to improve contextual word representations but have put little focus on discourse-level representations.
Design and implementation of an environment for Learning to Run a Power Network (L2RPN)
This report summarizes work performed as part of an internship at INRIA, in partial requirement for the completion of a master degree in math and informatics.
Figurative Language in Recognizing Textual Entailment
tuhinjubcse/Figurative-NLI
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Findings (ACL) 2021
We introduce a collection of recognizing textual entailment (RTE) datasets focused on figurative language.
SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations
hooman650/supcl-seq
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Findings (EMNLP) 2021
This paper introduces SupCL-Seq, which extends the supervised contrastive learning from computer vision to the optimization of sequence representations in NLP.
CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure
Multi-beam LiDAR sensors are increasingly used in robotics, particularly with autonomous cars for localization and perception tasks, both relying on the ability to build a precise map of the environment.
FLUTE: Figurative Language Understanding through Textual Explanations
tuhinjubcse/model-in-the-loop-fig-lang
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Figurative language understanding has been recently framed as a recognizing textual entailment (RTE) task (a. k. a.
Relation-Guided Few-Shot Relational Triple Extraction
congxin95/RelATE
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SIGIR 2022
To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relationrelevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities.
Content-aware Scalable Deep Compressed Sensing
To more efficiently address image compressed sensing (CS) problems, we present a novel content-aware scalable network dubbed CASNet which collectively achieves adaptive sampling rate allocation, fine granular scalability and high-quality reconstruction.
Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design
Current rapid changes in climate increase the urgency to change energy production and consumption management, to reduce carbon and other green-house gas production.
Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference
However, despite its substantial success on single sentence classification tasks where the challenge in making use of unlabeled data is to assign "good enough" pseudo-labels, for NLI tasks, the nature of unlabeled data is more complex: one of the sentences in the pair (usually the hypothesis) along with the class label are missing from the data and require human annotations, which makes SSL for NLI more challenging.
