Transfer and architecture : views from chart parsing

The objective of this report is to describe the embedding of a transfer module within an alternative architectural approach for machine translation of spontaneous spoken language. The approach is cognitively oriented, i.e. it adapts some of the assumed properties of human language comprehension and production. The aspects to be modeled will include incrementality and robustness with respect to disturbances caused by the environment and performance phenomena of speech. Interaction between software modules is used to reduce ambiguity. The transfer stage of a translation system clearly has to obey these requirements to be an integral part of such a system. This paper outlines the kind of demands to be placed on the transfer module. Relations between the basic formalisms representing linguistic knowledge on the one hand and transfer on the other hand are demonstrated as well as the consequences for algorithms and data structures

What's in a word graph evaluation and enhancement of word lattices

During the last few years, word graphs have been gaining increasing interest within the speech community as the primary interface between speech recognizers and language processing modules. Both development and evaluation of graph-producing speech decoders require generally accepted measures of word graph quality. While the notion of recognition accuracy can easily be extended to word graphs, a meaningful measure of word graph size has not yet surfaced. We argue, that the number of derivation steps a theoretical parser would need to process all unique sub-paths in a graph could provide a measure that is both application oriented enough to be meaningful and general enough to allow a useful comparison of word recognizers across different applications. This paper discusses various measures that are used, or could be used, to measure word graph quality. Using real-life data (word graphs evaluated in the 1996 Verbmobil acoustic evaluation), it is demonstrated how different measures can affect evaluation results. Finally, some algorithms are presented that can significantly improve word graph quality with respect to specific quality measures

Chart-based Incremental Transfer in Machine Translation

The transfer stage of a machine translation system for spontaneously spoken lan-guage in any case has to work incrementally and time-synchronously to be acceptable within natural dialogue settings. To achieve some of the necessary properties, we start from data structures and algorithms as known from chart parsing. Techniques used in this framework for analysis can be applied to the transfer stage in an MT-system as well. The adaptations and modifications will be explained. Head switching, a pro-totypical transfer problem, is used as an example to demonstrate how the proposed schema operates and in which way transfer may benefit from working with a chart. An experimental system has been implemented which is able to process the examples listed in this paper

Parallel Parsing: Different Distribution Schemata for Charts

Introduction We are going to present results from two experiments designed for parallel parsing within the chart paradigm. Parallel processing gains more relevance as applications become increasingly complex and nets of workstations as well as dedicated parallel computers are widely available. A chart-based parser is well suited for approaches to parallelism due to the identification of almost independent data objects that a chart is made of. A parallelization based on tasks of an agenda is only suitable for shared memory systems with a tight coupling, the choice of individual edges as autonomous agents may result in too many processes. But both nodes of a chart and rules of a grammar may provide sufficient possibilities for parallelization in a loosely coupled framework. Context-free parsing in a net of workstations These latter two aspects --- node-based and grammar-based approaches --- of a data-driven parallelization of chart parsing have been pursued in

Layered Charts for Speech Translation

Abstract. We are going to present an architecture for natural language processing sys-tems especially designed for spontaneous speech. We introduce the notion of a layered chart for information separation and information hiding. Complex distributed systems can be built using this approach which do not rely on a central data structure or control model. We describe a typed feature formalism with appropriateness suitable for such sys-tems by providing a compact and relocatable storage schema for feature structures. We set out an architecture for spontaneous speech translation that relies on partial parsing to minimize complex operations at an early syntactic stage, but rather carries out ut-terance integration after building up small scale syntactic objects. We adopt the notion of a variable depth of analysis by providing a module specialized to the translation of idioms and show how an global scoring schema for sub-paths in graphs can be used to block further operation on idioms already accounted for.

Communication in large distributed AI Systems for Natural Language Processing

We are going to describe the design and itnl)lemcntation of a conmmniealion system lbr large AI projects, pable of supporting various so2ware componeuts in a heterogeneous hard ware and programming-langnagc environment

Perspectives for Incremental MT with Charts

Obviously, human speech comprehension works in an incremental way: Unbiased introspection as well as evidence from psycholinguistics strongly suggest that we start to recognize a word before it has been completely uttered; even more, we may interrupt our conversation partner at that point if we disagree with what we think (s)he will be saying. Thus, incrementality is not restricted to any particular level of human speech comprehension, but is a general principle that is valid for all levels of processing --- thus making simultaneous interpretation possible. From a more technical point of view, incrementality would offer several advantages when constructing a machine interpretation system. The enormous processing amount that is necessary to cope with e.g. input consisting of continuous and spontaneous speech can partially be reduced by means of incremental processing. There have been several investigations on the use of incrementality for natural language understanding at various levels..

ICE INTARC Communication Environment Users Guide and Reference Manual Version 1.4

This version of the Technical Document covers version 1.4 Patch level 0 of ICE. Previous versions are no longer supported. Changes to the previous published version of the manual are indicated by margin bars.ICE is a communication mechanism for general AI projects. It was developed at the University of Hamburg to serve as the main cooperation means between modules in an architectural study for an advanced natural language interpreting system --- Verbmobil. Besides being used for our local work, ICE has been chosen to be the communication environment for the Demonstrator and the research prototype of the Verbmobil project. The Implementation is grounded on PVM (Parallel Virtual Machine), which is a system for communication between many processes in a heterogeneous network. On top of that we implemented an interface layer for several programming languages. It is modeled after a channel abstraction known from programming languages for parallel computers (e.g. Occam). The goal of the imple..