Discussion Details
| Title: | Reply to disc. posting 16.1156 |
| Submitter: | Peter Hallman |
| Description: | In issue 16.1156, Richard Sproat and Shalom Lappin challenge the
Minimalist community to ''to produce, by May of 2008, a working P&P [Principals and Parameters Framework] parser that can be trained in a supervised fashion on a standard treebank, such as the Penn Treebank, and perform in a range comparable to state-of-the-art statistical parsers,'' between 80% and 90% accuracy under certain conditions. The goals of the P&P approach to language acquisition are dramatically different from those of statistical approaches, which makes a comparison in terms of accuracy alone uninformative. The P&P framework seeks to connect typological universals to the mechanism of language learning, in effect explaining those universals as properties of the initial state of the trainable parser. A statistical parser can, within physical limitations, recognize and learn any statistically significant pattern, not merely those patterns that occur in human languages. The P&P approach finds this disadvantageous, because the P&P framework seeks to answer the question ''What is a possible human language (type)?'' The P&P parser that Sproat and Lappin envision would answer this question; comparable statistical parsers do not. A successful P&P parser would not only acquire the target language accurately, it would behave like a language learner in its acquisition timeline and would fail to acquire languages that violate language universals. It would have to display these properties in order to successfully learn the target language, because these properties ought to be inherent in the parameters underlying the system. So the P&P parser that Sproat and Lappin envision would accomplish much more than comparable statistical parsers, which makes the proposed accuracy metric a poor yardstick for comparison, and furthermore, I suspect, it makes the three-year timeline unrealistic, especially since there is no reason to believe that the discovery of parameters and implicational relations among them is finished at the present time and ready to form the basis of a trainable parser. Nonetheless, I hope someone takes up the challenge (it's not my field), since the attempt can only benefit the P&P framework. Perhaps there should be a prize. Peter Hallman Department of Linguistics McGill University Montreal, Quebec, Canada |
| Date Posted: | 20-Apr-2005 |
| Linguistic Field(s): |
Computational Linguistics
Discipline of Linguistics |
| LL Issue: | 16.1251 |
| Posted: | 20-Apr-2005 |
