Apart from MT, another important type of sentence transduction is semantic parsing, in the sense of mapping sentences in some domain to logical forms usable for question answering. (Note that semantic role labeling, discussed above, can also be viewed as a step towards semantic parsing.) Several studies in this relatively recent area have employed supervised learning, based on training corpora annotated with LFs (e.g., Mooney 2007; Zettlemoyer &amp; Collins 2007) or perhaps syntactic trees along with LFs (e.g., Ge and Mooney 2009). Typical domains have been QA about geography (where LFs are database queries), about Robocup soccer, or about travel reservations. Even unsupervised learning has been shown to be possible in restricted domains, such as QA based on medical abstracts (Poon and Domingos 2009) or the travel reservation domain (Poon 2013). Ideas used in this work include forming synonym clusters of nominal terms and verbal relations much as in Lin and Pantel's DIRT system, with creation of logical names (reflecting their word origins) for these concepts and relations; and learning (via Markov logic, a generalization of Markov networks) to annotate the nodes of dependency parse trees with database entities, types, and relations on the basis of a travel reservation dialogue corpus (where the data needed for the travel agent's answers are known to lie in the database). Whether such methods can be generalized to less restricted domains and forms of language remains to be seen. The recent creation of a general corpus annotated with an “abstract meaning representation”, AMR, is likely to foster progress in that direction (Banarescu et al. 2013).