NICE: Native language Interpretation and Communication Environment Lori Levin, Jaime Carbonell, Alon Lavie, Ralf Brown Carnegie Mellon University

Low Cost MT for Minor Languages Speakers of electronically underrepresented languages can participate in the information age. Policy makers can access ideas, viewpoints, and information from the developing world. MT for unforseen translation needs: e.g., humanitarian aid. Documentation and preservation of endangered languages.

NICE Languages Spanish and two or three indigenous languages of Latin America. Part of a larger program of Western Hemisphere collaboration. Planning joint meetings of indigenous people, language technologies people, government and funding agencies.

Our Approach to Low Cost MT Apply new machine learning techniques that require –less development data –shorter development time –less specialized human expertise

Our Approach to Low Cost MT Use multi-engine architecture to –flexibly make use of whatever resources are available data: Generalized EBMT human consultants: automatic elicitation human experts: knowledge-based MT –Tap the strengths of different approaches to MT.

Grammar Acquisition Tool

A Noun Phrase Learning Instance and Transfer Rule Learning Instance: English: the big boy Hebrew: ha-yeled ha-gadol Acquired Transfer Rule: Hebrew: NP: N ADJ English: NP: the ADJ N where: (Hebrew:N English: N) (Hebrew:ADJ English:ADJ) (Hebrew:N has ((def +))) (Hebrew:ADJ has ((def +)))

Version Space Abstraction Lattice

Scientific Challenges of NICE Automatic linguistic elicitation from native speakers: –bottom up: from small phrases to larger constructions –interrogation intermingled with confirmation of induced grammatical rules

Scientific Challenges of NICE Transfer rule induction: –version space learning: hone in on the right level of generalization –take advantage of expected constituents, e.g., ART-N but not V-ART –only local optimality is guaranteed –converges faster: no worst case exponential problem

iKBMT: Instructable Knowledge Based MT

Grammar Acquisition 2

A Sentence Learning Instance and Transfer Rule Learning Instance: English: I saw the big boy Hebrew: Ra’iti et ha-yeled ha-gadol Acquired Transfer Rule: Hebrew: S: V et NP English: S: “ I ” NP V NP where: (Hebrew:V English: V) (Hebrew:NP English: NP) (Hebrew:V has ((agreement 1sg))) (Hebrew:NP has ((def +)))