Papers with average F-score
Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines (D19-1)
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| Challenge: | Using dialog agents, we can collect family history data from in-person consultations and crowdsource it to a genetic counselor. |
| Approach: | They propose to use natural language interactions annotated with medical family histories to collect information from a genetic counselor and crowdsourcing. |
| Outcome: | The proposed system averages 0.87 on complex sentences on the targeted relations. |
Combating the Curse of Multilinguality in Cross-Lingual WSD by Aligning Sparse Contextualized Word Representations (2022.naacl-main)
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| Challenge: | Existing approaches to handle knowledge acquisition bottlenecks in multilingual training are limited due to the curse of multilinguality. |
| Approach: | They propose to use large pre-trained monolingual language models in cross lingual zero-shot word sense disambiguation coupled with a contextualized mapping mechanism. |
| Outcome: | The proposed model improves the average F-score by nearly 6.5 points over 17 target languages. |
Using English Baits to Catch Serbian Multi-Word Terminology (L18-1)
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| Challenge: | a new method for bilingual terminology extraction is proposed for a source language and a target language. |
| Approach: | They propose to use a bilingual terminology extraction approach for a source language and a target language to extract the terminology for sri lanka. |
| Outcome: | The proposed method extracts terminology for a source language and a target language from it. |