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Found 11 papers, 2 papers with code
Commonsense Knowledge in Word Associations and ConceptNet
Humans use countless basic, shared facts about the world to efficiently navigate in their environment.
BLCU-NLP at COIN-Shared Task1: Stagewise Fine-tuning BERT for Commonsense Inference in Everyday Narrations
This paper describes our system for COIN Shared Task 1: Commonsense Inference in Everyday Narrations.
BLCU\_NLP at SemEval-2019 Task 7: An Inference Chain-based GPT Model for Rumour Evaluation
Researchers have been paying increasing attention to rumour evaluation due to the rapid spread of unsubstantiated rumours on social media platforms, including SemEval 2019 task 7.
BLCU\_NLP at SemEval-2019 Task 8: A Contextual Knowledge-enhanced GPT Model for Fact Checking
For contextual knowledge enhancement, we extend the training set of subtask A, use several features to improve the results of our system and adapt the input formats to be more suitable for this task.
From Plots to Endings: A Reinforced Pointer Generator for Story Ending Generation
We introduce a new task named Story Ending Generation (SEG), whic-h aims at generating a coherent story ending from a sequence of story plot.
Multi-Perspective Fusion Network for Commonsense Reading Comprehension
From the experimental results, we can conclude that the difference fusion is comparable with union fusion, and the similarity fusion needs to be activated by the union fusion.
DEMN: Distilled-Exposition Enhanced Matching Network for Story Comprehension
We divide a complete story into three narrative segments: an \textit{exposition}, a \textit{climax}, and an \textit{ending}.
Multi-turn Inference Matching Network for Natural Language Inference
The inference of each turn is performed on the current matching feature and the memory.
BLCU\_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention
To comprehend an argument and fill the gap between claims and reasons, it is vital to find the implicit supporting warrants behind.
Semantic Frame Labeling with Target-based Neural Model
This paper explores the automatic learning of distributed representations of the target{'}s context for semantic frame labeling with target-based neural model.
