Robust Interactive Dialogue Interpretation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{rose:thesis,
  author =       "Carolyn Penstein Rose",
  title =        "Robust Interactive Dialogue Interpretation",
  school =       "Language Technologies Insititute, Carnegie Mellon
                 University",
  year =         "1997",
  note =         "Tech. Rept. CMU-LTI-97-151",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.lti.cs.cmu.edu/Research/Thesis/CarolynRose97.pdf",
  size =         "273 pages",
  abstract =     "ROSE is pronounced Rose like the wine

                 The first essential task of a natural language
                 interface is to map the users utterance onto some
                 meaning representation which can then be used for
                 further processing The three biggest challenges which
                 continue to stand in the way of accomplishing even this
                 basic task are extragrammaticality ambiguity and
                 recognition errors In this dissertation I address the
                 issue of how to handle the problem of
                 extragrammaticality efficiently where
                 extragrammaticality is defined as any deviation of an
                 input string from the coverage of a given systems
                 parsing grammar A useful analogy can be made between
                 humancomputer interaction through a natural language
                 interface and language interaction between speakers of
                 different languages with a small shared language base
                 Humans who share a very small language base are able to
                 communicate when the need arises by simplifying their
                 speech patterns and negotiating until they manage to
                 transmit their ideas to one another Hatch As the
                 speaker is speaking the listener throws his net in
                 order to catch those fragments of speech which are
                 comprehensible to him which he then attempts to fit
                 together semantically His subsequent negotiation with
                 the speaker builds upon this partial understanding

                 The approach presented here is based on this same
                 model. The ROSE approach RObustness with Structural
                 Evolution repairs extra-grammatical input in two stages
                 The first stage Repair Hypothesis Formation is
                 responsible for assembling a set of hypotheses about
                 the meaning of the ungrammatical utterance This stage
                 is itself divided into two steps Partial Parsing and
                 Combination The Partial Parsing step is similar to the
                 concept of the listener casting his net for
                 comprehensible fragments of speech Lavies GLR* parser
                 Lavie Lavie and Tomita is used to obtain an analysis of
                 islands of the speakers sentence in cases where it is
                 not possible to obtain an analysis for the entire
                 sentence In the Combination step the fragments from the
                 partial parse are assembled into a set of alternative
                 meaning representation hypotheses A genetic programming
                 approach is used to search for different ways to
                 combine the fragments in order to avoid requiring any
                 handcrafted repair rules In ROSEs second stage
                 Interaction with the user the system generates a set of
                 queries negotiating with the speaker in order to narrow
                 down to a single best meaning representation
                 hypothesis

                 The primary objective of the ROSE approach is to handle
                 the problem of extra grammaticality in an effective and
                 efficient way The most straightforward way to evaluate
                 different approaches to handling extragrammaticality is
                 by comparing them based on im provement in terms of
                 percentage of sentences handled correctly or
                 improvement of overall accuracy on a particular corpus
                 However it is misleading to compare instantiations of
                 different approaches this way since in theory many of
                 these approaches have the potential for yielding the
                 same amount of improvement given sufficient resources
                 in terms of space both static and dynamic time both
                 development time and run time and interactional effort
                 The real question is which approach can use these
                 resources most economically

                 I argue that the ROSE approach of separating the
                 Partial Parsing and Combination steps is more efficient
                 than placing the full burden of robustness on a single
                 parsing algorithm An analogous tradeoff in human-human
                 communication would be the casting and combining model
                 versus one in which the listener tries to construct a
                 complete syntac tic analysis for a sentence outside of
                 his language competence Though humans are known to make
                 a mental note of grammatical features that they are not
                 able to process correctly most of them are regarded
                 mainly as noise Hatch",
  abstract =     "Another goal of this work is to demonstrate that it is
                 more efficient to separate Repair Hypothesis Formation
                 from User Interaction rather than interleaving them In
                 other words a set of alternative ways of fitting the
                 whole set of fragments from the partial parse Global
                 Repair Hypotheses is constructed before any queries are
                 generated rather than generating a query to verify each
                 repair step Local Repair Hypotheses Besides being more
                 efficient this approach is arguably more effective When
                 humans collaborate for the purpose of understanding
                 they direct their questions towards information which
                 is necessary for accomplishing their task Clark and
                 Schaefer Clark and Wilkes-Gibbs When questions are
                 directed at clarifying the speakers meaning rather than
                 furthering the shared task speakers become agitated
                 Garfinkel By delaying the interaction until hypotheses
                 about the speakers whole meaning are formed it is
                 possible to focus the interaction on the task level
                 rather than on the language level

                 Therefore it will be shown that the ROSE approach
                 robustly extracts the meaning from the users
                 extragrammatical utterance efficiently and without
                 placing an undue burden on the user in terms of
                 interactional effort Finally because the ROSE approach
                 does not rely on any hand crafted repair rules or
                 additional knowledge sources it is a completely general
                 and portable solution",
  notes =        "Thu, 26 Feb 1998 16:03:59 EST Eventually when I get
                 around to clearing off some space in my unix account, I
                 plan to put my dissertation on the WWW, but in the mean
                 time, you can order one from CMU. You can email Debra
                 Germany at debra@cs.cmu.edu.

                 ",
}

Genetic Programming entries for Carolyn Penstein Rose

Citations