Papers with CALL

5 papers
Bag of Tricks for In-Distribution Calibration of Pretrained Transformers (2023.findings-eacl)

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Challenge: Recent studies show that pre-trained language models (PLMs) often predict over-confidently.
Approach: They propose to use ensemble learning and data augmentation to improve confidence calibration for PLMs by combining calibration techniques with a trade-off between accuracy and classification.
Outcome: The proposed calibration method improves classification accuracy and confidence in pre-trained language models by combining several calibration techniques.
Using the LARA Little Prince to compare human and TTS audio quality (2022.lrec-1)

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Challenge: A popular idea in Computer Assisted Language Learning (CALL) is to use multimodal annotated texts to support reading.
Approach: They propose to use an open source platform to create good quality audio for L2 learning . they use four passages from LARA versions of Saint-Exupèry’s “Le petit prince” to instantiate the 2x2 cross product of dialogue, not-dialogue and humour, not humor.
Outcome: The proposed method is based on a web form and ten languages.
Towards Computational Resource Grammars for Runyankore and Rukiga (2020.lrec-1)

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Challenge: In this paper, we present computational resource grammars of Runyankore and Rukiga languages . runyankores and rukiga are under-resourced Bantu languages spoken by 6 million people .
Approach: They present computational resource grammars for Runyankore and Rukiga languages . they use a multilingual grammar formalism and a special- purpose functional programming language .
Outcome: The proposed grammars are the first attempt to create language resources for R&R . they can be used to build computer-aided language learning applications for the languages .
Revita: a Language-learning Platform at the Intersection of ITS and CALL (L18-1)

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Challenge: Existing language-learning tools do not address the fundamental requirements of language learners and teachers.
Approach: They propose a free-to-use platform for language learning beyond the beginner level . they outline the established desiderata of CALL and ITS .
Outcome: The proposed platform supports language learning beyond the beginner level.
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning (2023.emnlp-main)

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Challenge: Existing passive mental health monitoring systems use alternative features such as activity, app usage, and location via smartphones due to data privacy concerns.
Approach: They propose a mobile mental health monitoring system that utilizes continuous speech and keyboard input in a privacy-preserving way via federated learning.
Outcome: The proposed system achieves 0.15 AUROC improvement and 8.21% MAE reduction in self-reported depression, stress, anxiety, and mood from 46 participants.

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