Ambiguity Resolution in the DMTRANS PLUS

Ambiguity Resolution in the DMTRANS PLUS Hiroaki Kitano, Hideto Tomabechi, and Lori Levin Abstract We present a cost-based (or energy-based) model of dis- ambiguation. When a sentence is ambiguous, a parse with the least cost is chosen from among multiple hypotheses. Each hypothesis is assigned a cost which is added when: (1) a new instance is created to satisfy reference success, (2) links between instances are created or removed to sat- isfy constraints on concept sequences, and (3) a concept node with insufficient priming is used for further process- ing. This method of ambiguity resolution is implemented in DMT~NS PLUS, which is a second generation bi-direetional English/Japanese machine translation system based on a mas- sively parallel spreading activation paradigm developed at the Center for Machine Translation at Carnegie Mellon Uni- versity. Center for Machine Translation Carnegie Mellon University Pittsburgh, PA 15213 U.S.A. access (DMA) paradigm of natural language process- ing. Under the DMA paradigm, the mental state of the hearer is modelled by a massively parallel network representing memory. Parsing is performed by pass- ing markers in the memory network. In our model, the meaning of a sentence is viewed as modifications made to the memory network. The meaning of a sen- tence in our model is definable as the difference in the memory network before and after understanding the sentence. 2 Limitations of Current Methods of Ambiguity Resolution 1 Introduction One of the central issues in natural language under- standing research is ambiguity resolution. Since many sentences are ambiguous out of context, techniques for ambiguity resolution have been an important topic in natural language understanding. In this paper, we de- scribe a model of ambiguity resolution implemented in DMTRANS PLUS, which is a next generation ma- chine translation system based on a massively parallel comuputational paradigm. In our model, ambiguities are resolved by evaluating the cost of each hypothe- sis; the hypothesis with the least cost will be selected. Costs are assigned when (1) a new instance is ere- ated to satisfy reference success, (2) links between in- stances are created or removed to satisfy constraints on concept sequences, and (3) a concept node with insufficient priming is used for further processing. The underlying philosophy of the model is to view parsing as a dynamic physical process in which one trajectory is taken from among many other possible paths. Thus our notion of the cost of the hypothesis is a representation of the workload required to take the path representing the hypothesis. One other impor- tant idea is that our model employs the direct memory *E-mail address is hiroaki@a.nl.cs.cmu.edu. Also with NEC Corporation. Traditional syntactic parsers have been using attach- ment preferences and local syntactic and semantic con- straints for resolving lexical and structural ambiguities. ([17], [28], [2], [7], [26], [11], [5]) However, these methods cannot select one interpretation from several plausible interpretations because they do not incorpo- rate the discourse context of the sentences being parsed ([81, [4]). Connectionist-type approaches as seen in [18], [25], and [8] essentially …

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