Papers by Khalid Alnajjar

5 papers
Finnish Dialect Identification: The Effect of Audio and Text (2021.emnlp-main)

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Challenge: Finnish is a language with multiple dialects that differ in accent, morphological forms and lexical choice.
Approach: They propose an approach to automatically detect the dialect of a speaker based on a transcript and transcript with audio recording in a dataset consisting of 23 different dialects.
Outcome: The proposed method achieves 57% accuracy, compared to 85% accuracy for text and audio.
Dialect Text Normalization to Normative Standard Finnish (D19-55)

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Challenge: a new method for normalizing dialect transcripts is proposed for normative Finnish . dialectal Finnish is the common way of communication for people online in finnish .
Approach: They propose a method for normalizing dialectal Finnish into the normative standard Finnish.
Outcome: The proposed method lowers the initial word error rate of the corpus from 52.89 to 5.73 . it can be used as one processing step with many types of spoken language materials.
Generating Modern Poetry Automatically in Finnish (D19-1)

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Challenge: a novel approach to generate poetry for the morphologically rich Finnish language is presented . the method is evaluated and described within the paradigm of computational creativity .
Approach: They propose a method for generating poetry automatically for the morphologically rich Finnish language using a genetic algorithm.
Outcome: The proposed method improves the state-of-the-art of previous Finnish poetry generators by introducing a higher degree of freedom in terms of structural creativity.
Ve’rdd. Narrowing the Gap between Paper Dictionaries, Low-Resource NLP and Community Involvement (2020.coling-demos)

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Challenge: Existing tools for Skolt Sami are limited due to its pluricentric nature and limited resources.
Approach: They propose to integrate community activities into a finite-state language description of a seriously endangered minority language, Skolt Sami.
Outcome: The proposed system integrates with existing tools and infrastructures for Uralic language masking the technical complexities behind a user-friendly UI.
When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and Its Intensity (2022.coling-1)

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Challenge: Existing methods to generate humor using multimodal data are needed to study the role of humor in human social function.
Approach: They propose a model that automatically detects humor in the Friends TV show using multimodal data and use prerecorded laughter as annotation as it marks humor.
Outcome: The proposed model detects humor 78% of the time and how long the audience’s laughter reaction should last with a mean absolute error of 600 milliseconds.

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