Session summary

ABSTRACT

Machine Translation (MT) technology has progressed significantly since the days of the ALPAC* report. In particular, multiple paradigms are being investigated ranging from statistical methods to full knowledge-based interlingual MT systems. Much of the recent work is based on advances in natural language processing since ALPAC in the 1960s, including:• Semantic analysis to resolve lexical and syntactic ambiguities during parsing, and thus reduce translation errors very significantly.• Unification grammars allowing syntactic and semantic constraints to be checked in a unified manner while parsing, and permitting reversible grammars---i. e., the same grammars to be used for generation as well as for analysis.• Advanced parsing methodologies, including augmented-LR compilation where knowledge sources (syntactic grammars, lexicons, and semantic ontologies) can be defined and maintained separately but are jointly compiled to apply simultaneously at run time, both in parsing and in generation.• Natural language generation, focusing on how to structure fluent target-language output, an activity not truly investigated in the pre-ALPAC days.• Automated corpus analysis tools, statistical and other means of extracting useful information from large bi- or multi-lingual corpora, including collocations, transfers, and contextual cues for disambiguation.• MRDs → MTDs, use of electronic machine-readable dictionaries (MRDs) to partially automate the creation of machine-tractable dictionaries (MTDs) in processable internal form for parsers and generators, permitting principled scaling up in MT configurations.

• Applied computing

  • Arts and humanities

• Computing methodologies

  • Artificial intelligence

• Hardware

  • Power and energy

    • Power estimation and optimization