In this paper, we discuss a natural language interface to a database of structured textual descriptions in the form of annotations of video objects. The interface maps the natural language query input on to the annotation structures. The language processing is done in three phases of expectations and implications from the input word, disambiguation of noun implications and slot-filling of prepositional expectations, and finally, disambiguation of verbal expectations. The system has been tested with different types of user inputs, including ill-formed sentences, and studied for erroneous inputs and for different types of portability issues.

System evaluation has mattered since research on automatic language and information processing began. However, the (D)ARPA conferences have raised the stakes substantially in requiring and delivering systematic evaluations and in sustaining these through long term programmes; and it has been claimed that this has both significantly raised task performance, as defined by appropriate effectiveness measures, and promoted relevant engineering development. These controlled laboratory evaluations have made very strong assumptions about the task context. The paper examines these assumptions for six task areas, considers their impact on evaluation and performance results, and argues that for current tasks of interest, e.g. summarising, it is now essential to play down the present narrowly-defined performance measures in order to address the task context, and specifically the role of the human participant in the task, so that new measures, of larger value, can be developed and applied.

We present a data-to-speech system called D2S, which can be used for the creation of data-to-speech systems in different languages and domains. The most important characteristic of a data-to-speech system is that it combines language and speech generation: language generation is used to produce a natural language text expressing the system's input data, and speech generation is used to make this text audible. In D2S, this combination is exploited by using linguistic information available in the language generation module for the computation of prosody. This allows us to achieve a better prosodic output quality than can be achieved in a plain text-to-speech system. For language generation in D2S, the use of syntactically enriched templates is guided by knowledge of the discourse context, while for speech generation pre-recorded phrases are combined in a prosodically sophisticated manner. This combination of techniques makes it possible to create linguistically sound but efficient systems with a high quality language and speech output.