Papers by Lauri Karttunen
Recursive Routing Networks: Learning to Compose Modules for Language Understanding (N19-1)
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Ignacio Cases, Clemens Rosenbaum, Matthew Riemer, Atticus Geiger, Tim Klinger, Alex Tamkin, Olivia Li, Sandhini Agarwal, Joshua D. Greene, Dan Jurafsky, Christopher Potts, Lauri Karttunen
| Challenge: | Recursive Routing Networks are modular, adaptable models that learn effectively in diverse environments. |
| Approach: | They propose to apply Recursive Routing Networks (RRNs) to natural language understanding by integrating them into existing architectures and recurrent network hidden layers. |
| Outcome: | The proposed model optimizes the parameters of the functions and the meta-learner decision-making component for routing inputs through those functions. |
Posing Fair Generalization Tasks for Natural Language Inference (D19-1)
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| Challenge: | Existing evaluation methods for deep learning semantics rely on naturalistic corpora, but they often fail to support the kind of generalization we are asking for. |
| Approach: | They define and motivate a formal notion of fairness for evaluations of deep learning models for semantics . they then apply it to natural language inference by constructing challenging but provably fair artificial datasets based on the results . |
| Outcome: | The proposed evaluations show that standard neural models fail to generalize in the required ways and even these models do not solve the task perfectly. |