Papers by Tanzir Pial

2 papers
GNAT: A General Narrative Alignment Tool (2023.emnlp-main)

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Challenge: Algorithmic sequence alignment is a common operation in many NLP tasks, but it is difficult to recognize similarities between distant versions of narratives such as translations and retellings.
Approach: They propose a general approach to narrative alignment coupling the Smith-Waterman algorithm from bioinformatics with modern text similarity metrics.
Outcome: The proposed approach can be used to identify similarities between two different versions of narratives, and to define rigorous p-values on the significance of any alignment.
Analyzing Film Adaptation through Narrative Alignment (2023.emnlp-main)

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Challenge: a new study examines the book-to-film adaptation process by examining the differences between the two media . novel adaptations often require dropping sections of the source text from the movie script .
Approach: They use a Smith-Waterman local alignment algorithm to quantify text similarity between scenes and book units.
Outcome: The proposed method reveals that novel adaptations often require dropping parts of the source text from the movie script.

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