Towards a Noise-Tolerant, Representation-Independent Mechanism for Argument Interpretation

Towards a Noise-Tolerant, Representation-Independent Mechanism for Argument Interpretation � Ingrid Zukerman and Sarah George School of Computer Science and Software Engineering Monash University Clayton, VICTORIA 3800, AUSTRALIA Abstract We describe a mechanism for the interpretation of arguments, which can cope with noisy conditions in terms of wording, beliefs and argument struc- ture. This is achieved through the application of the Minimum Message Length Principle to evalu- ate candidate interpretations. Our system receives as input a quasi-Natural Language argument, where propositions are presented in English, and gener- ates an interpretation of the argument in the form of a Bayesian network (BN). Performance was eval- uated by distorting the system’s arguments (gener- ated from a BN) and feeding them to the system for interpretation. In 75% of the cases, the interpreta- tions produced by the system matched precisely or almost-precisely the representation of the original arguments. 1 Introduction In this paper, we focus on the interpretation of argu- mentative discourse, which is composed of impli- cations. We present a mechanism for the interpreta- tion of NL arguments which is based on the applica- tion of the Minimum Message Length (MML) Prin- ciple for the evaluation of candidate interpretations (Wallace and Boulton, 1968). The MML princi- ple provides a uniform and incremental framework for combining the uncertainty arising from different stages of the interpretation process. This enables our mechanism to cope with noisy input in terms of wording, beliefs and argument structure, and to fac- tor out the elements of an interpretation which rely on a particular knowledge representation. So far, our mechanism has been tested on one knowledge representation – Bayesian Networks (BNs) (Pearl, 1988); logic-based representations will be tested in the future. Figure 1(a) shows a simple argument, and Figure 1(d) shows a subset � This research was supported in part by Australian Research Council grant A49927212. of a BN which contains the preferred interpretation of this argument (the nodes corresponding to the original argument are shaded). In this example, the argument is obtained through a web interface (the uncertainty value of the consequent is entered us- ing a drop-down menu). As seen in this example, the input argument differs structurally from the sys- tem’s interpretation. In addition, the belief value for the consequent differs from that in the domain BN, and the wording of the statements differs from the canonical wording of the BN nodes. Still, the sys- tem found a reasonable interpretation in the context of its domain model. The results obtained in this informal trial are val- idated by our automated evaluation. This evalua- tion, which assesses baseline performance, consists of passing distorted versions of the system’s argu- ments back to the system for interpretation. In 75% of the cases, the interpretations produced by the sys- tem matched the original arguments (in BN form) precisely or almost-precisely. In the next section, we review related research. We then describe the application of the MML cri- terion to the evaluation of interpretations. In Sec- tion 4, we outline the argument …

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