The ability to understand natural-language instructions is crit- ical to building intelligent agents that interact with humans. We present a system that learns to transform natural-language navigation instructions into executable formal plans. Given no prior linguistic knowledge, the system learns by simply observing how humans follow navigation instructions. The system is evaluated in three complex virtual indoor environ- ments with numerous objects and landmarks. A previously collected realistic corpus of complex English navigation in- structions for these environments is used for training and test- ing data. By using a learned lexicon to refine inferred plans and a supervised learner to induce a semantic parser, the sys- tem is able to automatically learn to correctly interpret a rea- sonable fraction of the complex instructions in this corpus.

This paper presents a method for learning a semantic parser from ambiguous supervision. Training data consists of nat- ural language sentences annotated with multiple potential meaning representations, only one of which is correct. Such ambiguous supervision models the type of supervision that can be more naturally available to language-learning systems. Given such weak supervision, our approach produces a se- mantic parser that maps sentences into meaning represen- tations. An existing semantic parsing learning system that can only learn from unambiguous supervision is augmented to handle ambiguous supervision. Experimental results show that the resulting system is able to cope up with ambiguities and learn accurate semantic parsers.