Search Results for author:
Found 15 papers, 4 papers with code
Improving Cross-Task Generalization with Step-by-Step Instructions
Instruction tuning has been shown to be able to improve cross-task generalization of language models.
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks
Causal chain reasoning (CCR) is an essential ability for many decision-making AI systems, which requires the model to build reliable causal chains by connecting causal pairs.
Exact Recovery of Community Detection in dependent Gaussian Mixture Models
We study the community detection problem on a Gaussian mixture model, in which (1) vertices are divided into $k\geq 2$ distinct communities that are not necessarily equally-sized; (2) the Gaussian perturbations for different entries in the observation matrix are not necessarily independent or identically distributed.
CausalBERT: Injecting Causal Knowledge Into Pre-trained Models with Minimal Supervision
Recent work has shown success in incorporating pre-trained models like BERT to improve NLP systems.
Guided Generation of Cause and Effect
We present a conditional text generation framework that posits sentential expressions of possible causes and effects.
HIT-SCIR at SemEval-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection
In this task, we dedicate to detecting causation, especially counterfactuals from texts.
On the coercivity condition in the learning of interacting particle systems
In the learning of systems of interacting particles or agents, coercivity condition ensures identifiability of the interaction functions, providing the foundation of learning by nonparametric regression.
Exact Recovery of Community Detection in k-Community Gaussian Mixture Model
We study the community detection problem on a Gaussian mixture model, in which vertices are divided into $k\geq 2$ distinct communities.
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder
Understanding event and event-centered commonsense reasoning are crucial for natural language processing (NLP).
Event Representation Learning Enhanced with External Commonsense Knowledge
Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction.
ELG: An Event Logic Graph
The evolution and development of events have their own basic principles, which make events happen sequentially.
Learning to Rank for Plausible Plausibility
Researchers illustrate improvements in contextual encoding strategies via resultant performance on a battery of shared Natural Language Understanding (NLU) tasks.
Story Ending Prediction by Transferable BERT
In this study, we investigate a transferable BERT (TransBERT) training framework, which can transfer not only general language knowledge from large-scale unlabeled data but also specific kinds of knowledge from various semantically related supervised tasks, for a target task.
Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training
In this paper, we propose using adversarial training augmented Seq2Seq model to generate reasonable and diversified story endings given a story context.
Constructing Narrative Event Evolutionary Graph for Script Event Prediction
Script event prediction requires a model to predict the subsequent event given an existing event context.
