Papers with downstream natural language understanding
Investigating the Role and Impact of Disfluency on Summarization (2023.emnlp-industry)
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| Challenge: | Existing studies have focused on disfluency detection and removal, with limited studies into its impact on downstream tasks. |
| Approach: | They propose to incorporate disfluency in summarization models to reduce the impact of replacement disfluencies on natural language processing tasks. |
| Outcome: | The proposed model improves on both public and real-life datasets and shows that it can handle disfluent data with up to 6.99-point degradation in Rouge-L score and replacement disfluencies have the highest negative impact. |
Pretraining Language Models Using Translationese (2024.emnlp-main)
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| Challenge: | a recent study shows that large language models perform well in low-resource languages . a vast majority of languages don't have comparable data as compared to English . |
| Approach: | They propose to use Translationese as synthetic data for pre-training language models for low-resource languages. |
| Outcome: | The proposed method reduces performance of LMs trained on clean data in Indian languages . the proposed model performs better in English than in other languages, but is not comparable to English. |