Paper: That's Not What I Meant! Using Parsers to Avoid Structural Ambiguities in Generated Text

ACL ID P14-1039
Title That's Not What I Meant! Using Parsers to Avoid Structural Ambiguities in Generated Text
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We investigate whether parsers can be used for self-monitoring in surface real- ization in order to avoid egregious errors involving ?vicious? ambiguities, namely those where the intended interpretation fails to be considerably more likely than alternative ones. Using parse accuracy in a simple reranking strategy for self- monitoring, we find that with a state- of-the-art averaged perceptron realization ranking model, BLEU scores cannot be improved with any of the well-known Treebank parsers we tested, since these parsers too often make errors that human readers would be unlikely to make. How- ever, by using an SVM ranker to combine the realizer?s model score together with features from multiple parsers, including ones designed to make the ranker more ro- bust to parsing mistakes, we show ...