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Found 10 papers, 0 papers with code
Modeling Content Importance for Summarization with Pre-trained Language Models
Previous work is mostly based on statistical methods that estimate word-level salience, which does not consider semantics and larger context when quantifying importance.
Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI
Recently, language models (LMs) have achieved significant performance on many NLU tasks, which has spurred widespread interest for their possible applications in the scientific and social area.
Exploring Logically Dependent Multi-task Learning with Causal Inference
In the field of causal inference, GS in our model is essentially a counterfactual reasoning process, trying to estimate the causal effect between tasks and utilize it to improve MTL.
End-to-End Conversational Search for Online Shopping with Utterance Transfer
Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers.
A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation
In this paper, we propose a semantically consistent and syntactically variational encoder-decoder framework, which uses adversarial learning to ensure the syntactic latent variable be semantic-free.
MCapsNet: Capsule Network for Text with Multi-Task Learning
Multi-task learning has an ability to share the knowledge among related tasks and implicitly increase the training data.
Learning What to Share: Leaky Multi-Task Network for Text Classification
Neural network based multi-task learning has achieved great success on many NLP problems, which focuses on sharing knowledge among tasks by linking some layers to enhance the performance.
Gated Multi-Task Network for Text Classification
This success can be largely attributed to the feature sharing by fusing some layers among tasks.
Multi-Task Label Embedding for Text Classification
Multi-task learning in text classification leverages implicit correlations among related tasks to extract common features and yield performance gains.
A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning
Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains.
