(***) 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

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 to semantic parsing, the task of converting text to a formal meaning representation, rely on annotated training data mapping sentences to logi- cal forms. Providing this supervision is a major bottleneck in scaling semantic parsers. This paper presents a new learn- ing paradigm aimed at alleviating the su- pervision burden. We develop two novel learning algorithms capable of predicting complex structures which only rely on a binary feedback signal based on the con- text of an external world. In addition we reformulate the semantic parsing problem to reduce the dependency of the model on syntactic patterns, thus allowing our parser to scale better using less supervision. Our results surprisingly show that without us- ing any annotated meaning representations learning with a weak feedback signal is ca- pable of producing a parser that is compet- itive with fully supervised parsers.

We present a system that learns to fol- low navigational natural language direc- tions. Where traditional models learn from linguistic annotation or word distri- butions, our approach is grounded in the world, learning by apprenticeship from routes through a map paired with English descriptions. Lacking an explicit align- ment between the text and the reference path makes it difficult to determine what portions of the language describe which aspects of the route. We learn this corre- spondence with a reinforcement learning algorithm, using the deviation of the route we follow from the intended path as a re- ward signal. We demonstrate that our sys- tem successfully grounds the meaning of spatial terms like above and south into ge- ometric properties of paths.

Extracting knowledge from unstructured text is a long-standing goal of NLP. Al- though learning approaches to many of its subtasks have been developed (e.g., pars- ing, taxonomy induction, information ex- traction), all end-to-end solutions to date require heavy supervision and/or manual engineering, limiting their scope and scal- ability. We present OntoUSP, a system that induces and populates a probabilistic on- tology using only dependency-parsed text as input. OntoUSP builds on the USP unsupervised semantic parser by jointly forming ISA and IS-PART hierarchies of lambda-form clusters. The ISA hierar- chy allows more general knowledge to be learned, and the use of smoothing for parameter estimation. We evaluate On- toUSP by using it to extract a knowledge base from biomedical abstracts and an- swer questions. OntoUSP improves on the recall of USP by 47% and greatly outperforms previous state-of-the-art ap- proaches.

(****) Sentence to logical form mapping using CCG and unification (UBL) instead of GenLex. Geo dataset, four languages, two meaning representations (funql, lambda). Start with single lex item for each sentence mapping it to LF. Introduce vertical bar | to ccg which can match / or \. (ZC07 similar?) Understand the SGD gradient possibly reading CC07. Starting with single lex item and trying splits look much less ad-hoc than ZC05,07 with Genlex and initial lexicon. Only the proper noun NPs (e.g. Texas) are in the initial lexicon. 4.1 splitting constraints interesting, can learn them from data? The split-merge process seem a bit ad-hoc, a more principled Bayesian approach may be possible. Good related work discussion in Sec 6. UBL geo880: p=.941 r=.850 f=.893 UBL-s (2pass): p=.885 r=.879 f=.882

A central problem in grounded language acqui- sition is learning the correspondences between a rich world state and a stream of text which refer- ences that world state. To deal with the high de- gree of ambiguity present in this setting, we present a generative model that simultaneously segments the text into utterances and maps each utterance to a meaning representation grounded in the world state. We show that our model generalizes across three domains of increasing difficulty—Robocup sportscasting, weather forecasts (a new domain), and NFL recaps.

Not quite sure what they are doing here. They describe everything in geoquery but give results in biomedical? They take dependency parse as given but induce paraphrases?

Machine learning offers a range of tools for training systems from data, but these methods are only as good as the underly- ing representation. This paper proposes to acquire representations for machine learn- ing by reading text written to accommo- date human learning. We propose a novel form of semantic analysis called read- ing to learn, where the goal is to obtain a high-level semantic abstract of multi- ple documents in a representation that fa- cilitates learning. We obtain this abstract through a generative model that requires no labeled data, instead leveraging repe- tition across multiple documents. The se- mantic abstract is converted into a trans- formed feature space for learning, result- ing in improved generalization on a rela- tional learning task.

Introduced the movie review dataset which can be downloaded at http://www.cs.cornell.edu/people/pabo/movie-review-data. Compares Naive Bayes, MaxEnt, and SVM with unigram / bigram feature sets. Cites Turney (2002), developed further with Pang&Lee 2004 and 2005.

Structured perceptron: yi' = argmax_y w^T g(xi,y) w = w + g(xi,yi) - g(xi,yi')