Papers by Nabil Hossain
“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. |
Stimulating Creativity with FunLines: A Case Study of Humor Generation in Headlines (2020.acl-demos)
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| Challenge: | FunLines is an online game that allows players to generate and rate funny news headlines . it is difficult to generate data that depends on human creativity, and measuring creativity often requires more effort. |
| Approach: | They propose a game where players edit news headlines to make them funny and rate the funniness of headlines edited by others. |
| Outcome: | The proposed game outperforms other crowdsourcing approaches in generating humor datasets. |
Simple and Effective Retrieve-Edit-Rerank Text Generation (2020.acl-main)
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| Challenge: | Using retrieve-and-edit methods, text generation methods can be improved by reranking outputs from training sets and learning models to produce the final output. |
| Approach: | They propose to extend retrieve-and-edit seq2seq methods with a simple post-generation ranking approach that retrieves multiple outputs and edits each independently to produce the final output. |
| Outcome: | The proposed approach outperforms existing methods on two machine translation datasets and shows room for improvement with better candidate output selection in future work. |
“Judge me by my size (noun), do you?” YodaLib: A Demographic-Aware Humor Generation Framework (2020.coling-main)
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| Challenge: | Humor is subjective and can be interpreted in different ways by different people. |
| Approach: | They propose an automatic method for filling the blanks in Mad Libs stories . they build upon the BERT platform to predict location-biased word fillings in incomplete sentences . |
| Outcome: | The proposed framework outperforms a semi-automated approach for filling the blanks in Mad Libs stories while accounting for the demographic backgrounds of the desired audience. |