Papers with count-based methods
Train, Sort, Explain: Learning to Diagnose Translation Models (N19-4)
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| Challenge: | Evaluating translation models is a trade-off between effort and detail. |
| Approach: | They propose to use a neural text classifier to automatically expose systematic differences between human and machine translations to human experts. |
| Outcome: | The proposed method exposes systematic differences between human and machine translations to human experts. |
Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese (2020.acl-main)
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| Challenge: | a method using neural language models (LMs) for analyzing the word order of language is currently lacking. |
| Approach: | They propose a method using neural language models to analyze the word order in Japanese . they test whether there is a parallel between LMs and human word order preference . |
| Outcome: | The proposed method is validated by comparing it with other linguistic studies. |