Papers with PCM
Better Quality Estimation for Low Resource Corpus Mining (2022.findings-acl)
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| Challenge: | State-of-the-art Quality Estimation models lack robustness to out-of domain examples. |
| Approach: | They propose a method that uses multitask training, data augmentation and contrastive learning to achieve better and more robust QE performance. |
| Outcome: | The proposed method improves QE performance significantly in the MLQE challenge and the robustness of QE models when tested in the Parallel Corpus Mining setup. |
Coupling Local Context and Global Semantic Prototypes via a Hierarchical Architecture for Rhetorical Roles Labeling (2026.eacl-long)
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Anas Belfathi, Nicolas Hernandez, Monceaux Laura, Warren Bonnard, Mary Catherine Lavissière, Christine Jacquin, Richard Dufour
| Challenge: | Hierarchical models capture local dependencies but lack global, corpus-level representations. |
| Approach: | They propose two prototype-based methods that integrate local context with global representations to address this limitation. |
| Outcome: | The proposed methods integrate local context with global representations. |
Measuring Context-Word Biases in Lexical Semantic Datasets (2022.emnlp-main)
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| Challenge: | Existing pretrained contextualized models have been used to evaluate word-in-context representations in many lexical semantic tasks. |
| Approach: | They propose to quantify the degree of context or word biases in existing datasets by probing masked input. |
| Outcome: | The proposed model performs better when both word and context are available than with masked input. |