Name
Abstract | ||
|---|---|---|
Machine translation (MT) between natural languages is an infamously difficult problem in Natural Language Processing that is still very much being researched. This research study explores the efficacy of developing an adaptive translator using Lexical Functional Grammars. The main research objective is building a machine translator generator for multilingual communication, i.e. developing a system whose inputs are linguistic descriptions of a desired source and target language and whose output is a program that translates between the two natural languages. A bidirectional machine translator between English and Hungarian, developed as a proof-of-concept case study, is discussed. The benefits and drawbacks of this approach as generalized to MT systems are also discussed, along with possible areas of future work. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/WETICE.2017.53 | 2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE) |
Keywords | Field | DocType |
adaptive computing,lexical functional grammars,machine translation,natural language processing | Rule-based machine translation,Pragmatics,Programming language,Computer science,Adaptive system,Machine translation,Grammar,Natural language,Natural language processing,Artificial intelligence,Adaptive computing | Conference |
ISBN | Citations | PageRank |
978-1-5386-1760-1 | 0 | 0.34 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryan Lane | 1 | 0 | 0.34 |
| Ajay Bansal | 2 | 320 | 27.21 |