Papers with recall score
Alligators All Around: Mitigating Lexical Confusion in Low-resource Machine Translation (2025.naacl-short)
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| Challenge: | Current machine translation systems for low-resource languages have a particular failure mode: they tend to confuse words within a domain. |
| Approach: | They propose a recall-based metric to measure the failure mode of machine translation systems for low-resource languages. |
| Outcome: | The proposed model outperforms a lexicon-based translator in 122 low-resource languages. |
A Training-free and Reference-free Summarization Evaluation Metric via Centrality-weighted Relevance and Self-referenced Redundancy (2021.acl-long)
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| Challenge: | Existing evaluation metrics for text summarization systems are expensive and time-consuming. |
| Approach: | They propose a training-free and reference-free summarization evaluation metric that incorporates a centrality-weighted relevance score and a self-referenced redundancy score. |
| Outcome: | The proposed evaluation metric outperforms existing methods on multi-document and single-document summarization evaluation. |
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing (N19-1)
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| Challenge: | Existing entity typing systems exploit type hierarchy provided by KB schema to model label correlations. |
| Approach: | They propose a graph layer that encodes global label co-occurrence statistics and word-level similarities. |
| Outcome: | The proposed model achieves a 15.3% relative F1 improvement on a large dataset with over 10,000 free-form types. |