This paper presents the results of a joint effort of a group of multimodality researchers and tool developers to improve the interoperability between several tools used for the annotation and analysis of multimodality. Each of the tools has specific strengths so that a variety of different tools, working on the same data, can be desirable for project work. However this usually requires tedious conversion between formats. We propose a common exchange format for multimodal annotation, based on the annotation graph (AG) formalism, which is supported by import and export routines in the respective tools. In the current version of this format the common denominator information can be reliably exchanged between the tools, and additional information can be stored in a standardized way.
This paper presents a multi-neuro tagger that uses variable lengths of contexts and weighted inputs (with information gains) for part of speech tagging. Computer experiments show that it has a correct rate of over 94% for tagging ambiguous words when a small Thai corpus with 22,311 ambiguous words is used for training. This result is better than any of the results obtained using the single-neuro taggers with fixed but different lengths of contexts, which indicates that the multi-neuro tagger can dynamically find a suitable length of contexts in tagging.
This paper details a software architecture for discourse processing in spoken dialogue systems, where the three component tasks of discourse processing are (1) Dialogue Management, (2) Context Tracking, and (3) Pragmatic Adaptation. We define these three component tasks and describe their roles in a complex, near-future scenario in which multiple humans interact with each other and with computers in multiple, simultaneous dialogue exchanges. This paper reports on the software modules that accomplish the three component tasks of discourse processing, and an architecture for the interaction among these modules and with other modules of the spoken dialogue system. A motivation of this work is reusable discourse processing software for integration with non-discourse modules in spoken dialogue systems. We document the use of this architecture and its components in several prototypes, and also discuss its potential application to spoken dialogue systems defined in the near-future scenario.
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