Semantic parsing is the task of converting natural language utterances into machine interpretable meaning representations which can be executed against a real-world environment such as a database. Scaling semantic parsing to arbitrary domains faces two interrelated challenges: obtaining broad coverage training data effectively and cheaply; and developing a model that generalizes to compositional utterances and complex intentions. We address these challenges with a framework which allows to elicit training data from a domain ontology and bootstrap a neural parser which recursively builds derivations of logical forms. In our framework meaning representations are described by sequences of natural language templates, where each template corresponds to a decomposed fragment of the underlying meaning representation. Although artificial, templates can be understood and paraphrased by humans to create natural utterances, resulting in parallel triples of utterances, meaning representations, and their decompositions. These allow us to train a neural semantic parser which learns to compose rules in deriving meaning representations. We crowdsource training data on six domains, covering both single-turn utterances which exhibit rich compositionality, and sequential utterances where a complex task is procedurally performed in steps. We then develop neural semantic parsers which perform such compositional tasks. In general, our approach allows to deploy neural semantic parsers quickly and cheaply from a given domain ontology.
Until early 2000, semantic parsing research mainly focused on restricted domains. Besides , commonly used datasets are for coaching advice to soccer agents @cite_41 , and for air travel information service @cite_46 . At that time, statistical approaches for parsing domains-specific context-free grammars have been largely explored. For example, kate2006using propose , which induces context-free grammar rules that generate meaning representations, and uses kernel SVM to score derivations. ge2005statistical propose , which employs an integrated statistical parser to produce a semantically augmented parse tree. Each non-terminal node in the tree has both a syntactic and a semantic label, from which the final meaning representation can be derived. The system proposed by wong2007learning learns synchronous context free grammars that generate utterances and meaning representations. Parsing is achieved by finding the most probable derivation that leads to the utterance and recovering the meaning representation with synchronous rules. lu2008generative proposes a generative model for utterances and meaning representations. Similar to ge2005statistical , they define hybrid trees whose nodes include both words and meaning representation tokens. Training is performed with the EM algorithm. The model, especially the generative process, was extend by kim2010generative to learn from ambiguous supervisions.
@cite_41 @cite_46
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RoboCup Challenge offers a set of challenges for intelligent agent researchers using a friendly competition in a dynamic, real-time, multiagent domain. While RoboCup in general envisions longer range challenges over the next few decades, RoboCup Challenge presents three specific challenges for the next two years: (i) learning of individual agents and teams; (ii) multi-agent team planning and plan-execution in service of teamwork; and (iii) opponent modeling. RoboCup Challenge provides a novel opportunity for machine learning, planning, and multi-agent researchers it not only supplies a concrete domain to evaluate their techniques, but also challenges researchers to evolve these techniques to face key constraints fundamental to this domain: real-time, uncertainty, and teamwork. Progress can be measured and encouraged via standards for comparison and evaluation. Though qualitative assessments can be useful in initial stages, quantifiable measures of systems under the same conditions are essential for comparing results and assessing claims. This paper will address the emerging standards for evaluation of spoken language systems.