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Found 27 papers, 10 papers with code
Neural Natural Logic Inference for Interpretable Question Answering
Many open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses.
CogBERT: Cognition-Guided Pre-trained Language Models
To fill the gap, we propose CogBERT, a framework that can induce fine-grained cognitive features from cognitive data and incorporate cognitive features into BERT by adaptively adjusting the weight of cognitive features for different NLP tasks.
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.
DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination
To relieve this issue, curriculum learning is proposed to improve model performance and generalization by ordering training samples in a meaningful (e. g., easy to hard) sequence.
Text Difficulty Study: Do machines behave the same as humans regarding text difficulty?
Given a task, human learns from easy to hard, whereas the model learns randomly.
A Graph Enhanced BERT Model for Event Prediction
To this end, we incorporate an additional structured variable into BERT to learn to predict the event connections in the training process.
e-CARE: a New Dataset for Exploring Explainable Causal Reasoning
Understanding causality has vital importance for various Natural Language Processing (NLP) applications.
AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small Molecule Inhibitor
The AlphaFold computer program predicted protein structures for the whole human genome, which has been considered as a remarkable breakthrough both in artificial intelligence (AI) application and structural biology.
Learning Event Graph Knowledge for Abductive Reasoning
Abductive reasoning aims at inferring the most plausible explanation for observed events, which would play critical roles in various NLP applications, such as reading comprehension and question answering.
ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning
ExCAR first acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning.
Guided Generation of Cause and Effect
We present a conditional text generation framework that posits sentential expressions of possible causes and effects.
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.
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.
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.
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.
Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction
The model uses a target-sensitive representation of the news abstract to weigh sentences in the news content, so as to select and combine the most informative sentences for market modeling.
Learning Sentence Representations over Tree Structures for Target-Dependent Classification
To address above issues, we propose a reinforcement learning based approach, which automatically induces target-specific sentence representations over tree structures.
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.
Knowledge-Driven Event Embedding for Stock Prediction
Representing structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications.
A General Framework for Content-enhanced Network Representation Learning
This paper investigates the problem of network embedding, which aims at learning low-dimensional vector representation of nodes in networks.
