Discussion Details
| Title: | Re: 16.1156, A Challenge to the Minimalist Community |
| Submitter: | Chung-chieh Shan |
| Description: | In response to Richard Sproat and Shalom Lappin's challenge (16.1156),
Peter Hallman (16.1251) draws a contrast between the Principles and Parameters (P&P) approach and statistical approaches to parsing. 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 framework seeks to answer the question (Q) 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. He suspects that it would be "unrealistic" for a P&P parser to reach accuracy comparable to current statistical parsers in three years, for two reasons. First, as the paragraph above concludes, a P&P parser would accomplish more than current statistical parsers. Second, current P&P theory may not be "ready to form the basis of a trainable parser". I am more optimistic for P&P. To me, these same two reasons indicate Sproat and Lappin's challenge to be realistic rather than unrealistic. First, a statistical parser is only hindered when it recognizes patterns that do not occur in human languages. The larger the space of hypotheses to explore, the less effective machine learning can be. Conversely, many advances in statistical parsing (going back as far as probabilistic regular and context-free grammars) are made precisely by better delineating "those patterns that occur in human languages", such as locality and hierarchy. In other words, a statistical parser embodies an (approximate) answer to the question Q, just as a P&P parser or theory does. A better answer should give rise to a better parser. Second, the attention that the P&P approach pays to language acquisition corresponds directly to payoffs in parsing performance. For example, a parser whose design addresses the poverty of the stimulus should require less training data, less supervision, or both. Such a parser would be able to learn from the Penn Treebank better, take advantage of vast amounts of unlabeled corpora, or both. In sum, a parser that better "connect[s] typological universals to the mechanism of language learning" will fare better in accuracy, all other things being equal. That one linguistic theory may be more "ready" than another for implementation reflects on not just the focus of different communities (as Martha McGinnis points out, 16.1251), but also the theories themselves. Trying to answer the question Q is no excuse for poor parsing. All other things being equal, poor (or unknown) parsing performance indicates failure at (resp. disinterest in) answering Q. |
| Date Posted: | 22-Apr-2005 |
| Linguistic Field(s): |
Computational Linguistics
Discipline of Linguistics |
| LL Issue: | 16.1288 |
| Posted: | 22-Apr-2005 |
