Papers by Khalid Alnajjar
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. |