Search Results for author:
Found 7 papers, 1 papers with code
Impact of representation matching with neural machine translation
Most neural machine translation models are implemented as a conditional language model framework composed of encoder and decoder models.
Learning from Matured Dumb Teacher for Fine Generalization
The flexibility of decision boundaries in neural networks that are unguided by training data is a well-known problem typically resolved with generalization methods.
Choose Your Own Question: Encouraging Self-Personalization in Learning Path Construction
Finally, Rocket provides students with fine-grained information on their learning path, giving them an avenue to assess their own skills and track their learning progress.
JBNU at MRP 2019: Multi-level Biaffine Attention for Semantic Dependency Parsing
We propose a unified parsing model using biaffine attention (Dozat and Manning, 2017), consisting of 1) a BERT-BiLSTM encoder and 2) a biaffine attention decoder.
Concept Equalization to Guide Correct Training of Neural Machine Translation
Neural machine translation decoders are usually conditional language models to sequentially generate words for target sentences.
Improving a Multi-Source Neural Machine Translation Model with Corpus Extension for Low-Resource Languages
We found that the corpus extension could also improve the performance of multi-source neural machine translation.
