28 citations · 76 across the 7 of their papers we have counts for
7 papers
Relating Simple Sentence Representations in Deep Neural Networks and the Brain
Sharmistha Jat, Hao Tang, Partha Talukdar +1
What is the relationship between sentence representations learned by deep recurrent models against those encoded by the brain? Is there any correspondence between hidden layers of…
Competence-based Curriculum Learning for Neural Machine Translation
Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig +2
Current state-of-the-art NMT systems use large neural networks that are not only slow to train, but also often require many heuristics and optimization tricks, such as specialized…
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
Bishan Yang, Tom Mitchell
This paper focuses on how to take advantage of external knowledge bases (KBs) to improve recurrent neural networks for machine reading. Traditional methods that exploit knowledge f…
Learning to Learn Semantic Parsers from Natural Language Supervision
Igor Labutov, Bishan Yang, Tom Mitchell
As humans, we often rely on language to learn language. For example, when corrected in a conversation, we may learn from that correction, over time improving our language fluency.…
Machine Reading with Background Knowledge
Ndapandula Nakashole, Tom M. Mitchell
Intelligent systems capable of automatically understanding natural language text are important for many artificial intelligence applications including mobile phone voice assistants…
Joint Extraction of Events and Entities within a Document Context
Bishan Yang, Tom Mitchell
Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon. The interpretation of events and entities is…