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Machine learning is traditionally formalized and researched as the study of learning concepts and decision functions from labeled examples, requir- ing a representation that encodes information about the domain of the decision function to be learned. We are interested in providing a way for a human teacher to interact with an automated learner us- ing natural instructions, thus allowing the teacher to communicate the relevant domain expertise to the learner without necessarily knowing anything about the internal representations used in the learn- ing process. In this paper we suggest to view the process of learning a decision function as a natural language lesson interpretation problem instead of learning from labeled examples. This interpretation of ma- chine learning is motivated by human learning pro- cesses, in which the learner is given a lesson de- scribing the target concept directly, and a few in- stances exemplifying it. We introduce a learning algorithm for the lesson interpretation problem that gets feedback from its performance on the final task, while learning jointly (1) how to interpret the lesson and (2) how to use this interpretation to do well on the final task. This approach alleviates the supervision burden of traditional machine learn- ing by focusing on supplying the learner with only human-level task expertise for learning. We evaluate our approach by applying it to the rules of the Freecell solitaire card game. We show that our learning approach can eventually use natural language instructions to learn the target concept and play the game legally. Furthermore, we show that the learned semantic interpreter also general- izes to previously unseen instructions.

(***) Build on unification based Kwiatkowski 2010. Key observation is groups of words show same syntactic/semantic tag variation. So learn the variation for the whole group, more robust to data sparsity. i.e. They have discovered that word classes exist :) This helps generalize the language-independent unification approach to unedited sentences like in atis. Mentions Clarke10, Liang11, Goldwasser11 as going from sentences to answers without LF. Mentions Branavan10, Vogel10, Liang09, Poon09, 10 as learning from interactions. Results: (ubl: Kwiatkowsky10, fubl: Kwiatkowski11) atis-exact-f1: zc07:.852 ubl:.717 fubl:.828 geo880-f1: zc05:.870 zc07:.888 ubl:.882 fubl:.886 geo250-en: wasp:.829 ubl:.826 fubl:.837 geo250-sp: wasp:.858 ubl:.824 fubl:.857 geo250-jp: wasp:.858 ubl:.831 fubl:.835 geo250-tr: wasp:.781 ubl:.746 fubl:.731

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.

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/

Current approaches for semantic parsing take a supervised approach requiring a consider- able amount of training data which is expen- sive and difficult to obtain. This supervision bottleneck is one of the major difficulties in scaling up semantic parsing. We argue that a semantic parser can be trained effectively without annotated data, and in- troduce an unsupervised learning algorithm. The algorithm takes a self training approach driven by confidence estimation. Evaluated over Geoquery, a standard dataset for this task, our system achieved 66% accuracy, com- pared to 80% of its fully supervised counter- part, demonstrating the promise of unsuper- vised approaches for this task.