Papers with in-dataset
Where are we Still Split on Tokenization? (2024.findings-eacl)
Copied to clipboard
| Challenge: | Identifying tokens is a crucial first step for many tasks in Natural Language Processing (NLP) gold tokenization is often assumed, but some work on token-level tasks is more challenging. |
| Approach: | They propose an efficient method for tokenization with subword-based language models and evaluate it on 122 languages in 20 scripts. |
| Outcome: | The proposed method performs on par with the state-of-the-art on 122 languages in 20 scripts. |
RethinkCWS: Is Chinese Word Segmentation a Solved Task? (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent years have seen remarkable success in the use of deep neural networks on Chinese word segmentation (CWS) however, the performance of CWS systems has gradually reached a plateau with the rapid development of deep networks. |
| Approach: | They propose a fine-grained evaluation for existing Chinese word segmentation systems that allows us to diagnose the strengths and weaknesses of existing models. |
| Outcome: | The proposed model can diagnose strengths and weaknesses of existing models and alleviate negative transfer problem when doing multi-criteria learning. |
CU-MAM: Coherence-Driven Unified Macro-Structures for Argument Mining (2025.acl-long)
Copied to clipboard
| Challenge: | Argument Mining (AM) involves the automatic identification of argument structure in natural language. |
| Approach: | They propose an approach that captures local and global coherence to identify argument structures by modeling macro-structure. |
| Outcome: | The proposed approach shows superior performance on heterogeneous datasets and on unseen datasets. |
Composable Cross-prompt Essay Scoring by Merging Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to cross-prompt automated essay scoring use all available sources . however, using multiple sources for continual adaptation raises privacy concerns . |
| Approach: | They propose a source-free adaptation approach that selectively merges the parameters of individual models without further access to the source datasets. |
| Outcome: | The proposed method outperforms joint-training methods on all sources while maintaining computational efficiency. |