Papers with F2-score
MuLVE, A Multi-Language Vocabulary Evaluation Data Set (2022.lrec-1)
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| Challenge: | Existing systems for vocabulary evaluation are based on simple rules and do not account for real-life user learning data. |
| Approach: | They propose to use real-life user vocabulary learning data to evaluate vocabulary . they use language learning data from a phase6 vocabulary trainer to generate a multilingual data set for vocabulary evaluation. |
| Outcome: | The proposed data set provides outstanding results with 95.5 accuracy and F2-score. |
Combining Psychological Theory with Language Models for Suicide Risk Detection (2023.findings-eacl)
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| Challenge: | Existing models for suicide prevention are limited in domains and are not available in low-resource languages. |
| Approach: | They propose a computational model that combines pre-trained language models with a fixed set of manually crafted suicidal cues and a two-stage fine-tuning process to detect suicide risk. |
| Outcome: | The proposed model outperforms baseline models even early on in the conversation and performs well across genders and age groups. |
Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language (2022.coling-1)
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| Challenge: | Existing domain-specific models for detecting suicide are lacking in low-resource languages. |
| Approach: | They propose a model that combines pre-trained language models with a fixed set of suicidal cues and a two-stage fine-tuning process to detect SI. |
| Outcome: | The proposed model outperforms baseline models even early on in the conversation and performs well across genders and age groups. |