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 of multimodality. We propose a multimodal annotation exchange format, based on the annotation graph formalism, which is supported by import and export routines in the respective tools.

It is well known that Machine Translation (MT) has not approached the quality of human translations. It has also been noted that MT research has largely ignored the work of professionals and researchers in the field of translation, and that MT might benefit from collaboration with this field. In this paper, I look at a specialized type of translation, Simultaneous Interpretation (SI), in the light of possible applications to MT. I survey the research and practice of SI, and note that explanatory analyses of SI do not yet exist. However, descriptive analyses do, arrived at through anecdotal, empirical, and model-based methods. These descriptive analyses include “techniques” humans use for interpreting, and I suggest possible ways MT might use these techniques. I conclude by noting further questions which must be answered before we can fully understand SI, and how it might help MT.

A not-translated word (NTW) is a token which a machine translation (MT) system is unable to translate, leaving it untranslated in the output. The number of not-translated words in a document is used as one measure in the evaluation of MT systems. Many MT developers agree that in order to reduce the number of NTWs in their systems, designers must increase the size or coverage of the lexicon to include these untranslated tokens, so that the system can handle them in future processing. While we accept this method for enhancing MT capabilities, in assessing the nature of NTWs in real-world documents, we found surprising results. Our study looked at the NTW output from two commercially available MT systems (Systran and Globalink) and found that lexical coverage played a relatively small role in the words marked as not translated. In fact, 45% of the tokens in the list failed to translate for reasons other than that they were valid source language words not included in the MT lexicon. For instance, e-mail addresses, words already in the target language and acronyms were marked as not-translated words. This paper presents our analysis of NTWs and uses these results to argue that in addition to lexicon enhancement, MT systems could benefit from more sophisticated pre- and postprocessing of real-world documents in order to weed out such NTWs.