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Transformers

10,397 papers

Papers per year

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Papers

Improving Relation Extraction with Knowledge-attention IJCNLP 2019
Neural data-to-text generation: A comparison between pipeline and end-to-end architectures IJCNLP 2019
Syntax-Enhanced Self-Attention-Based Semantic Role Labeling IJCNLP 2019
Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention IJCNLP 2019
Dynamic Past and Future for Neural Machine Translation IJCNLP 2019
Look-up and Adapt: A One-shot Semantic Parser IJCNLP 2019
Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons IJCNLP 2019
Recurrent Positional Embedding for Neural Machine Translation IJCNLP 2019
Self-Attention with Structural Position Representations IJCNLP 2019
Movie Plot Analysis via Turning Point Identification IJCNLP 2019
Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information IJCNLP 2019
Answering questions by learning to rank - Learning to rank by answering questions IJCNLP 2019
Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension IJCNLP 2019
Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching IJCNLP 2019
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing - A Tale of Two Parsers Revisited IJCNLP 2019
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs IJCNLP 2019
Attribute-aware Sequence Network for Review Summarization IJCNLP 2019
Contrastive Attention Mechanism for Abstractive Sentence Summarization IJCNLP 2019
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge IJCNLP 2019
Broad-Coverage Semantic Parsing as Transduction IJCNLP 2019
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks IJCNLP 2019
Denoising based Sequence-to-Sequence Pre-training for Text Generation IJCNLP 2019
Hierarchically-Refined Label Attention Network for Sequence Labeling IJCNLP 2019
Visualizing and Understanding the Effectiveness of BERT IJCNLP 2019
Transformer Dissection: An Unified Understanding for Transformer’s Attention via the Lens of Kernel IJCNLP 2019