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NAACL - 2016
Total Papers:- 228
Total Papers accross all years:- 6159
Total Citations :- 2666
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Improved Neural Network-based Multi-label Classification with Better Initialization Leveraging Label Co-occurrence
Gakuto Kurata |
Bing Xiang |
Bowen Zhou |
Improving event prediction by representing script participants
Simon Ahrendt |
Vera Demberg |
Vision and Feature Norms: Improving automatic feature norm learning through cross-modal maps
Luana Bulat |
Douwe Kiela |
Stephen Clark |
Towards Unsupervised and Language-independent Compound Splitting using Inflectional Morphological Transformations
Patrick Ziering |
Lonneke van der Plas |
Assessing Relative Sentence Complexity using an Incremental CCG Parser
Bharat Ram Ambati |
Siva Reddy |
Mark Steedman |
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents
Rui Zhang |
Honglak Lee |
Dragomir R. Radev |
Achieving Accurate Conclusions in Evaluation of Automatic Machine Translation Metrics
Yvette Graham |
Qun Liu |
Grounded Semantic Role Labeling
Shaohua Yang |
Qiaozi Gao |
Changsong Liu |
Caiming Xiong |
Song-Chun Zhu |
Joyce Y. Chai |
Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning
Janarthanan Rajendran |
Mitesh M. Khapra |
Sarath Chandar |
Balaraman Ravindran |
Stating the Obvious: Extracting Visual Common Sense Knowledge
Mark Yatskar |
Vicente Ordonez |
Ali Farhadi |
Top-down Tree Long Short-Term Memory Networks
Xingxing Zhang |
Liang Lu |
Mirella Lapata |
The Sensitivity of Topic Coherence Evaluation to Topic Cardinality
Jey Han Lau |
Timothy Baldwin |
Probabilistic Models for Learning a Semantic Parser Lexicon
Jayant Krishnamurthy |
Discriminative Reranking for Grammatical Error Correction with Statistical Machine Translation
Tomoya Mizumoto |
Yuji Matsumoto |
K-Embeddings: Learning Conceptual Embeddings for Words using Context-Embeddings: Learning Conceptual Embeddings for Words using Context
Thuy Vu |
D. Stott Parker |
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