Machine Translation Using Automatically Inferred Construction-Based Correspondence and Language Models
Year: 2009
Type: conference-paper
Abstract: We discuss the problem of translation in the wider context of the problem of mean- ing in cognition and describe a structural statistical machine translation (MT) method moti- vated by philosophical, cognitive, and computational considerations. Our approach relies on a recently published algorithm capable of learning from a raw corpus a limited yet effec- tive grammar that can be used to construct probabilistic parsers and language models, and on cognitively motivated heuristics for learning construction-based translation models. A pi- lot system has been implemented and tested successfully on simple English to Hebrew and Spanish to English translation tasks.
Authors: Shimon Edelman, Zach Solan
Institutions: Cornell University, Tel Aviv University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2161761192