Papers by Michael Gamon
“President Vows to Cut <Taxes> Hair”: Dataset and Analysis of Creative Text Editing for Humorous Headlines (N19-1)
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| Challenge: | Existing datasets address specific humor templates, such as funny one-liners and filling in Mad Libs R. |
| Approach: | They introduce a dataset for research in computational humor that uses crowdsourced editing techniques to create funny headlines. |
| Outcome: | The new dataset supports classic theories of humor, including incongruity, superiority, setup/punchline. |
MS-LaTTE: A Dataset of Where and When To-do Tasks are Completed (2022.lrec-1)
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| Challenge: | Tasks are a fundamental unit of work in the daily lives of people, who are increasingly using digital means to keep track of, organize, triage, and act on them. |
| Approach: | They compile and release a large-scale dataset that captures location and time for tasks and a BERT-fine-tuned model that predicts task co-occurrence. |
| Outcome: | The proposed framework captures location and time, and predicts task co-occurrence with a BERT fine-tuned model outperforming baselines. |
One Document, Many Revisions: A Dataset for Classification and Description of Edit Intents (2022.lrec-1)
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| Challenge: | Existing methods to understand revisions have failed to provide a deeper understanding of the nature of these edits. |
| Approach: | They propose to use a Wikipedia revision history dataset to train a classifier that achieves a 90% accuracy in identifying edit intent and a distantly-supervised model that generates . |
| Outcome: | The proposed model achieves 90% accuracy in identifying edit intent and a best score of 28 ROUGE. |
Modeling the Relationship between User Comments and Edits in Document Revision (D19-1)
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| Challenge: | Managing collaborative documents can be difficult due to the profusion of edits and comments that multiple authors make during a document’s evolution. |
| Approach: | They propose a hierarchical multi-layer deep neural network to model the relationship between edits and comments by encoding specific edit actions such as additions and deletions while accounting for document context. |
| Outcome: | The proposed model outperforms baselines in a number of evaluation settings and achieves a precision@1 of 71.0% and precision@3 of 94.4% for Comment Ranking while achieving 74.4% accuracy on Edit Anchoring. |
LITE: Intent-based Task Representation Learning Using Weak Supervision (2022.naacl-main)
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| Challenge: | To-do texts are often short and under-specified, which poses a challenge for current text representation models. |
| Approach: | They propose a neural multi-task learning framework that extracts representations of English to-do tasks with a multi-head attention mechanism on top of a pre-trained text encoder. |
| Outcome: | The proposed model outperforms baseline models on four downstream tasks and achieves error reduction of 38.7%. |