Summary of the paper
| Title | Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation |
|---|---|
| Authors | Jonathan Clark, Robert Frederking and Lori Levin |
| Abstract | Data Selection has emerged as a common issue in language technologies. We define Data Selection as the choosing of a subset of training data that is most effective for a given task. This paper describes deductive feature detection, one component of a data selection system for machine translation. Feature detection determines whether features such as tense, number, and person are expressed in a language. The database of the The World Atlas of Language Structures provides a gold standard against which to evaluate feature detection. The discovered features can be used as input to a Navigator, which uses active learning to determine which piece of language data is the most important to acquire next. |
| Language | Multiple languages |
| Topics | Corpus (creation, annotation, etc.), Machine Translation, SpeechToSpeech Translation, Typological databases |
Full paper |
Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation |
Slides |
Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation |
| Bibtex | @InProceedings{CLARK08.308, author = {Jonathan Clark, Robert Frederking and Lori Levin}, title = {Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation}, booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)}, year = {2008}, month = {may}, date = {28-30}, address = {Marrakech, Morocco}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-4-0}, note = {http://www.lrec-conf.org/proceedings/lrec2008/}, language = {english} } |