Conversations provide rich opportunities for interactive, continuous learning. When some- thing goes wrong, a system can ask for clari- fication, rewording, or otherwise redirect the interaction to achieve its goals. In this pa- per, we present an approach for using con- versational interactions of this type to induce semantic parsers. We demonstrate learning without any explicit annotation of the mean- ings of user utterances. Instead, we model meaning with latent variables, and introduce a loss function to measure how well potential meanings match the conversation. This loss drives the overall learning approach, which in- duces a weighted CCG grammar that could be used to automatically bootstrap the semantic analysis component in a complete dialog sys- tem. Experiments on DARPA Communica- tor conversational logs demonstrate effective learning, despite requiring no explicit mean- ing annotations.

(*) Learning with logical forms provided for only part of the data (clarification questions in dialogues). Loss-sensitive perceptron (Singh-Miller and Collins 2007) DARPA communicator corpus. (Walker 2002) Access to logs of conversations where system utterances annotated, user utterances not. Annotations include speech acts (Walker and Passonneau (2001)), these are not predicted for unannotated sentences. Seems like the annotated part of the dialogue (system utterances) can be seen as training set, the rest (user utterances) as test set, is there anything new here? Uses ZC05, ZC07 (template based, not unification). Further reading: Clarke et al. (2010) and Liang et al. (2011) describe approaches for learning semantic parsers from questions paired with database answers, while Goldwasser et al. (2011) presents work on unsupervised learning. Semantic analysis tasks from context-dependent database queries (Miller et al., 1996; Zettlemoyer and Collins, 2009), grounded event streams (Chen et al., 2010; Liang et al., 2009), environment interactions (Branavan et al., 2009; 2010; Vogel and Jurafsky, 2010), and even unannotated text (Poon and Domingos, 2009; 2010). Uses BIU Number Normalizer http://www.cs.biu.ac.il/˜nlp/downloads/