Abstract
In this paper we investigate the automatic generation and evaluation of sentential paraphrases. We describe a method
for generating sentential paraphrases by
using a large aligned monolingual corpus of news headlines acquired automatically from Google News and a standard Phrase-Based Machine Translation
(PBMT) framework. The output of this
system is compared to a word substitution baseline. Human judges prefer the
PBMT paraphrasing system over the word
substitution system. We demonstrate that
BLEU correlates well with human judgements provided that the generated paraphrased sentence is sufficiently different
from the source sentence.
for generating sentential paraphrases by
using a large aligned monolingual corpus of news headlines acquired automatically from Google News and a standard Phrase-Based Machine Translation
(PBMT) framework. The output of this
system is compared to a word substitution baseline. Human judges prefer the
PBMT paraphrasing system over the word
substitution system. We demonstrate that
BLEU correlates well with human judgements provided that the generated paraphrased sentence is sufficiently different
from the source sentence.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 6th International Natural Language Generation Conference |
| Number of pages | 5 |
| Publication status | Published - 2010 |