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Methodology
← Learning Types
Deep Learning
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Learning Types
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Representation Learning
4516 directly classified papers
Papers per year
2006: 7
2007: 2
2008: 7
2009: 1
2010: 5
2011: 9
2012: 20
2013: 37
2014: 51
2015: 47
2016: 86
2017: 197
2018: 322
2019: 499
2020: 569
2021: 521
2022: 608
2023: 552
2024: 535
2025: 432
2026: 9
Papers
Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data
EMNLP 2018
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
EMNLP 2018
Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity
EMNLP 2018
Exploiting Deep Representations for Neural Machine Translation
EMNLP 2018
Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures
EMNLP 2018
NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation
EMNLP 2018
Spherical Latent Spaces for Stable Variational Autoencoders
EMNLP 2018
Multi-Task Label Embedding for Text Classification
EMNLP 2018
Dual Fixed-Size Ordinally Forgetting Encoding (FOFE) for Competitive Neural Language Models
EMNLP 2018
The Importance of Being Recurrent for Modeling Hierarchical Structure
EMNLP 2018
Learning Gender-Neutral Word Embeddings
EMNLP 2018
Similarity-Based Reconstruction Loss for Meaning Representation
EMNLP 2018
Grammar Induction with Neural Language Models: An Unusual Replication
EMNLP 2018
An Interactive Web-Interface for Visualizing the Inner Workings of the Question Answering LSTM
EMNLP 2018
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing
EMNLP 2018
OpenKE: An Open Toolkit for Knowledge Embedding
EMNLP 2018
Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks
EMNLP 2018
An Operation Sequence Model for Explainable Neural Machine Translation
EMNLP 2018
What do RNN Language Models Learn about Filler–Gap Dependencies?
EMNLP 2018
Do Language Models Understand Anything? On the Ability of LSTMs to Understand Negative Polarity Items
EMNLP 2018
Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models
EMNLP 2018
Explicitly modeling case improves neural dependency parsing
EMNLP 2018
Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Syntactic Task Analysis
EMNLP 2018
Representation of Word Meaning in the Intermediate Projection Layer of a Neural Language Model
EMNLP 2018
Debugging Sequence-to-Sequence Models with Seq2Seq-Vis
EMNLP 2018
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