72 citations · 226 across the 16 of their papers we have counts for
5 papers · 2 filters
Look-up and Adapt: A One-shot Semantic Parser
Zhichu Lu, Forough Arabshahi, Igor Labutov +1
Computing devices have recently become capable of interacting with their end users via natural language. However, they can only operate within a limited "supported" domain of disco…
Understanding language-elicited EEG data by predicting it from a fine-tuned language model
Dan Schwartz, Tom Mitchell
Electroencephalography (EEG) recordings of brain activity taken while participants read or listen to language are widely used within the cognitive neuroscience and psycholinguistic…
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.…