Name
Title | ||
|---|---|---|
Sentence-level MT evaluation without reference translations: Beyond language modeling |
Abstract | ||
|---|---|---|
In this paper we investigate the possibility of evaluating MT quality and fluency at the sentence level in the absence of reference translations. We measure the correlation between automatically-generated scores and human judgments, and we evaluate the per- formance of our system when used as a classifier for identifying highly dysfluent and ill- formed sentences. We show that we can substantially improve on the correlation between language model perplexity scores and human judgment by combining these perplexity scores with class probabilities from a machine-learned classifier. The classifier uses linguis- tic features and has been trained to distinguish human translations from machine transla- tions. We show that this approach also performs well in identifying dysfluent sentences. |
Year | Venue | Keywords |
|---|---|---|
2005 | EAMT | language model,machine learning |
DocType | Citations | PageRank |
Conference | 56 | 2.96 |
References | Authors | |
19 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Gamon | 1 | 1484 | 89.50 |
| Anthony Aue | 2 | 290 | 16.87 |
| Martine Smets | 3 | 100 | 10.09 |