Papers with token-level prediction tasks
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding (2022.naacl-main)
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| Challenge: | Existing studies on weak supervision for NLU focus on a specific task or simulate weak supervision signals from ground-truth labels. |
| Approach: | They propose a benchmark to advocate and facilitate research on weak supervision for NLU . they use document-level and token-level prediction tasks as examples . |
| Outcome: | The proposed benchmark advocates and facilitates research on weak supervision for NLU tasks. |
SLICER: Sliced Fine-Tuning for Low-Resource Cross-Lingual Transfer for Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Large multilingual models fail to successfully transfer to low-resource languages for zero-shot cross-lingual transfer . sliced fine-tuning for named entity recognition (SLICER) forces stronger token contextualization in the Transformer. |
| Approach: | They propose a simple yet highly effective approach for improving zero-shot cross-lingual transfer for named entity recognition to low-resource languages. |
| Outcome: | The proposed approach reduces decontextualization of token representations and classifiers . it yields consistent transfer gains for low-resource languages, the authors show . |