Papers with pre-ordering approach
Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to generative commonsense reasoning hypothesize that pre-trained models lack sufficient parametric knowledge for this task. |
| Approach: | They propose to use order-agnostic input to elaborately manipulate the order of the given concepts before generation to evaluate their commonsense knowledge. |
| Outcome: | The proposed approach outperforms more sophisticated models with a lot of external data and resources in the task of generating a logical sentence from a set of concepts. |
Exploiting Pre-Ordering for Neural Machine Translation (L18-1)
Copied to clipboard
| Challenge: | Existing studies have shown that Neural Machine Translation suffers from the problems that some source words are mistakenly translated for multiple times . |
| Approach: | They propose a pre-ordering approach to solve the under-translation problem by pre-ordnanced source sentences and position embedding to enhance monotone translation. |
| Outcome: | The proposed method significantly improves translation quality by 2.43 BLEU points on Chinese-to-English translation. |