Papers by Jeff Mitchell
Jack the Reader – A Machine Reading Framework (P18-4)
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Dirk Weissenborn, Pasquale Minervini, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bošnjak, Jeff Mitchell, Thomas Demeester, Tim Dettmers, Pontus Stenetorp, Sebastian Riedel
| Challenge: | Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions. |
| Approach: | They propose a framework for Machine Reading that allows for quick prototyping by component reuse and evaluation of new models on existing datasets. |
| Outcome: | The proposed framework supports question answering, natural language inference and link prediction tasks. |
Priorless Recurrent Networks Learn Curiously (2020.coling-main)
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| Challenge: | a recent study shows domain-general recurrent neural networks reproduce human language behaviours . a lack of a unified concept of number agreement between these processes is a limitation of the model . |
| Approach: | They propose to use domain-general recurrent neural networks without explicit linguistic inductive biases to reproduce human language behaviours. |
| Outcome: | The proposed model can learn number agreement within unnatural sentences, the authors show . they show that the model has an effective understanding of singular versus plural for individual sentences . |
Behavior Analysis of NLI Models: Uncovering the Influence of Three Factors on Robustness (N18-1)
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| Challenge: | Currently, state-of-the-art models achieve impressive test set performance in the form of accuracy scores. |
| Approach: | They examine the models' robustness to semantically-valid alterations to the input data by identifying three factors and comparing their impact on three SNLI models. |
| Outcome: | The proposed models show that they can generalise to new in-domain instances while also showing that they suffer from insensitivity to small but semantically significant alterations. |