Discussion Details
| Title: | Re: A Challenge to the Minimalist Community |
| Submitter: | Richard Sproat |
| Description: | We thank the people who have responded to our challenge posted in
16.1156, both in private and on the List. A number of the responses (mostly those offered in private) have been supportive. Others have raised issues with our challenge. In the interests of brevity, we will respond to the main objections rather than to individual comments: 1. It is too early to expect P&P to provide a theory that can be implemented as part of a large-scale parsing system that learns from data. RESPONSE: This was our "Objection 3", which we characterized as a "remarkable dodge". Need we say more? 2. The challenge is the wrong challenge, either because: A. We rely on the Penn Treebank as our gold standard, whereas there is no reason to accept the validity of the Penn Treebank structures; they are not even theoretically interesting. B. Providing valid structures for sentences is not the only goal or even the most reasonable goal of syntactic theory: a syntactic theory should also provide grammaticality judgments for sentences; a syntactic theory should explain cross-linguistic variation. C. Statistical approaches have it too easy since they are trained on data that is similar in genre to the test data. RESPONSE: If you do not like the Penn Treebank, you are free to use any other reasonable corpus, and to provide your own annotations and representations. The task remains the same. Show that a P&P acquisition system can do at least as well as statistical approaches. Regarding B, we remind readers that humans do assign structure to sentences, that assigning structure to sentences is surely a part of what syntax is about, that humans acquire this knowledge as part of language acquisition, and that P&P claims to provide an explanation of how this is achieved. So we are at a loss to understand why inducing a large-scale working parser from sample data is not a valid test of P&P. The claim that statistical approaches have it "too easy" will have some content when it is accompanied by an implemented P&P device that matches the performance of machine learning systems. If such a device cannot be constructed, it suggests not that statistical systems have it too easy (the same conditions have always been on offer to those interested in developing a large coverage P&P parser), but that the P&P framework is not computationally viable as a model for language acquisition. 3. The challenge could certainly in principle be met by P&P. RESPONSE: "In principle" doesn't count here. Only "in fact" has any credibility. 4. The challenge is already being met. RESPONSE: Oh really, where? We look forward to seeing convincing evidence of this. 5. Computational linguistics is about engineering rather than science. It may be useful for us scientists to be more aware of what is going on in engineering, and similarly the engineers could gain some insights from us scientists. RESPONSE: It is true that computational linguistics often has engineering applications and that these applications often motivate computational linguists to address certain problems. But let's not confuse the issue. Many computational linguists, the two present authors included, are fully trained linguists who happen to be interested in how computational methods can yield insights on language. If this is not science, we do not know what is. 6. Machine learning cannot produce constraints that rule out ungrammatical sentences. Where the P&P seeks to characterize the set of possible natural languages, ML just learns syntactic patterns exhibited in a particular corpus. RESPONSE: Machine learning has achieved induction of robust grammars that can, in fact, be turned into classifiers able to distinguish between acceptable and ill formed structures over large linguistic domains. The fact that after more than half a century of sustained research the P&P enterprise and its antecedents have failed to produce a single broad coverage computational system for grammar learning suggests that its notion of Universal Grammar encoded in a language faculty may well be misconceived. The increasing success of unsupervised ML techniques in grammar acquisition lends at least initial plausability to the proposal that general learning and induction mechanisms, together with minimal assumptions concerning basic linguistic categories and rule hypothesis search spaces are sufficient to account for much (perhaps all) of the language acquisition task. 7. You should have offered a monetary prize as a financial incentive for meeting the challenge. RESPONSE: We don't see why we need to pay people extra for demonstrating the viability of a "research program" which has dominated much of the field for decades, but has yet to produce anything approaching the results that its rivals have achieved efficiently in a relatively short period of time. Finally since our challenge has actually stimulated relatively little discussion from the P&P community, we suspect the following may also be one response: 8. Ignore the challenge because it's irrelevant to the theory and therefore not interesting. RESPONSE: This is the "answer" we had most anticipated. It does not bode well for a field when serious scientific issues are dismissed and dealt with through silence. Richard Sproat Shalom Lappin |
| Date Posted: | 05-May-2005 |
| Linguistic Field(s): |
Computational Linguistics
Syntax Discipline of Linguistics |
| LL Issue: | 16.1439 |
| Posted: | 05-May-2005 |
