Papers by Jonathan Francis
A Free/Open-Source Morphological Analyser and Generator for Sakha (2022.lrec-1)
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| Challenge: | a morphological transducer for Sakha is being developed for use in downstream tasks . the marginalised language is subject to increasing economic and cultural peril due to climate change . |
| Approach: | They describe the development of a morphological analyser and generator for Sakha . the transducer has coverage of solidly above 90%, and high precision . it is already being used in downstream tasks such as linguistic maintenance . |
| Outcome: | The proposed morphological analyser has coverage of 90% and high precision . it is already being used in computer assisted language learning applications . |
Coalescing Global and Local Information for Procedural Text Understanding (2022.coling-1)
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| Challenge: | Existing models for procedural text understanding have low precision or low recall . et al., 2012, pp. 106-106. |
| Approach: | They propose a model that builds entity- and timestep-aware input representations . they extend the model with additional output layers and integrate it into a story reasoning framework . |
| Outcome: | The proposed model achieves state-of-the-art on a popular procedural text understanding dataset and on 'story reasoning benchmark' it integrates the model with additional output layers and improves on the previous models. |
Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models (2021.emnlp-main)
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| Challenge: | Recent work proposes lightweight updates to improve commonsense reasoning models . fine-tuning can cause models to overfit to task-specific data and forget knowledge gained during training . |
| Approach: | They propose to use lightweight models to update pre-trained language models to learn commonsense background knowledge. |
| Outcome: | The proposed models learn from commonsense reasoning datasets, but they are overfitted and limited generalized. |
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering (D19-60)
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| Challenge: | Recent approaches on non-extractive commonsense QA show increased performance . attention-based injection seems to be preferable for knowledge integration . |
| Approach: | They propose to use attention-based injection to integrate knowledge into commonsense QA models. |
| Outcome: | The proposed methods show that attention-based injection is preferable for knowledge integration, and that the degree of domain overlap plays a crucial role in determining model success. |