Papers with data quality misleads model learning
Leveraging Entailment Judgements in Cross-Lingual Summarisation (2024.findings-acl)
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| Challenge: | Synthetically created cross-lingual summarisation datasets are prone to include document-summary pairs where the reference summary is unfaithful to the corresponding document. |
| Approach: | They propose to use off-the-shelf cross-lingual Natural Language Inference to evaluate faithfulness of reference and model generated summaries and use unlikelihood loss to teach a model about unfaithful summary sequences. |
| Outcome: | The proposed approach evaluates faithfulness of reference and model generated summaries and uses unlikelihood loss to teach a model about unfaithful summary sequences. |