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Found 9 papers, 1 papers with code
Relation Extraction with Explanation
Recent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences.
Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation
We present a new local entity disambiguation system.
Saliency Learning: Teaching the Model Where to Pay Attention
Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances.
Attentional Multi-Reading Sarcasm Detection
We also study the behavior of the proposed model to provide explanations for the model's decisions.
Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference
Deep learning models have achieved remarkable success in natural language inference (NLI) tasks.
Joint Neural Entity Disambiguation with Output Space Search
In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS).
Dependent Gated Reading for Cloze-Style Question Answering
We present a novel deep learning architecture to address the cloze-style question answering task.
Event Nugget Detection with Forward-Backward Recurrent Neural Networks
Traditional event detection methods heavily rely on manually engineered rich features.
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference
Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference.
Ranked #16 on
Natural Language Inference
on SNLI
