Papers with supervised and unsupervised methods
Weakly-supervised Deep Cognate Detection Framework for Low-Resourced Languages Using Morphological Knowledge of Closely-Related Languages (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to cognate detection focus on orthographic, phonetic or contextual models, which under-perform for most under-resourced languages. |
| Approach: | They propose a language-agnostic weakly-supervised deep cognate detection framework for under-resourced languages using morphological knowledge from closely related languages. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on cognate detection datasets across languages and can be extended to a wide range of languages from any language family. |
Keyphrase Generation: A Text Summarization Struggle (N19-1)
Copied to clipboard
| Challenge: | Existing methods for keyphrase generation are unable to produce valuable terms that do not appear in the text. |
| Approach: | They propose to consider the keyphrase string as an abstractive summary of the title and the abstract. |
| Outcome: | The proposed method can generate better keyphrases than the existing methods or the unsupervised ones. |
MetaKP: On-Demand Keyphrase Generation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing keyphrase prediction methods only output a single set of keyphrases per document . however, existing methods fail to cater to diverse needs of users and downstream applications . |
| Approach: | They propose a method that requires keyphrases that conform to specific high-level goals or intents to generate on-demand keyphrase generation. |
| Outcome: | The proposed method surpasses the performance of a fully fine-tuned BART-base model in 0.548 SemF1 . it can be used in epidemic event detection from social media. |
Unsupervised Open-domain Keyphrase Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models that generate keyphrases without human-labeled data are lacking in this area. |
| Approach: | They propose a model that consists of two modules that can be built in an unsupervised fashion and can perform consistently across domains. |
| Outcome: | The proposed model performs consistently across domains and narrows the gap between supervised and unsupervised models down to about 16%. |