This paper presents a method for learning a semantic parser from ambiguous supervision. Training data consists of natural language sentences annotated with multiple potential mean...
We investigate whether erroneous examples in the domain of fractions can help students learn from common errors of other students presented in a computer-based system. Presenting t...
Dimitra Tsovaltzi, Erica Melis, Bruce M. McLaren, ...
We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states ...
This paper proposes a two-phase example-based machine translation methodology which develops translation templates from examples and then translates using template matching. This ...