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Deep Learning
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Transformers
9294 directly classified papers
Papers per year
2011: 1
2014: 2
2015: 6
2016: 17
2017: 67
2018: 156
2019: 404
2020: 769
2021: 1217
2022: 1446
2023: 1628
2024: 1574
2025: 1647
2026: 360
Papers
Incremental Transformer with Deliberation Decoder for Document Grounded Conversations
ACL 2019
Improving Long Distance Slot Carryover in Spoken Dialogue Systems
ACL 2019
Effort-Aware Neural Automatic Post-Editing
ACL 2019
What Does BERT Learn about the Structure of Language?
ACL 2019
Fact or Factitious? Contextualized Opinion Spam Detection
ACL 2019
GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling
ACL 2019
Scheduled Sampling for Transformers
ACL 2019
BAM! Born-Again Multi-Task Networks for Natural Language Understanding
ACL 2019
DFKI-NMT Submission to the WMT19 News Translation Task
ACL 2019
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives
ACL 2019
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
ACL 2019
Fully Convolutional Video Captioning with Coarse-to-Fine and Inherited Attention
AAAI 2019
A Neural Network Approach to Verb Phrase Ellipsis Resolution
AAAI 2019
A Task in a Suit and a Tie: Paraphrase Generation with Semantic Augmentation
AAAI 2019
Contextualized Non-Local Neural Networks for Sequence Learning
AAAI 2019
Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation
AAAI 2019
Tied Transformers: Neural Machine Translation with Shared Encoder and Decoder
AAAI 2019
A Tensorized Transformer for Language Modeling
NIPS 2019
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
NIPS 2019
Levenshtein Transformer
NIPS 2019
Are Sixteen Heads Really Better than One?
NIPS 2019
Visualizing and Measuring the Geometry of BERT
NIPS 2019
Geometry-Aware Neural Rendering
NIPS 2019
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
NIPS 2019
DppNet: Approximating Determinantal Point Processes with Deep Networks
NIPS 2019
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