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Learning Context-Dependent Mappings from Sentences to Logical Form Luke S. Zettlemoyer and Michael Collins MIT SAIL Cambridge, MA 02139 LSD, Collins sail.mit.com Abstract We consider the problem of
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The first version of our method is trained on sentences in the context-free language (CFL), which is a well-known set of syntactic rules for LSL, and is widely used in artificial intelligence. The second approach is trained directly on LSL, and is a significant improvement compared to the first version of our algorithm due to the more complete representation at each stage of the process. In an empirical study, the first version of our algorithm outperformed more conventional ML methods on a variety of benchmarks. We describe various practical improvements to our method, and provide experiments to demonstrate their effectiveness. The paper also includes a detailed introduction to the machine learning literature and an extensive tutorial that compares the approaches. Topics Mutation-based model-building and learning from experience Representation Learning and contextual models for understanding discourse Intelligibility-based semantics Learning from context Information diffusion and contextual learning from sentence representations The paper is organized as follows. Section 2: Introduction and goals defines the context-dependent learning paradigm from which we derive the two methods and introduces the motivation for our work. Section 3 proposes the context-dependent learning model, an extension of the classical linear models. Finally, we describe the training and performance on the training data. Section 4 discusses the implementation of the models and the experimental validation. We start by describing the process that led to the construction of a context-dependent LSL model and discuss the practical performance of the model. We present the results from a validation experiment conducted with data drawn from a set of natural conversations. The model we build achieves a top-performing performance on the data, and demonstrates the generalizability of our method to a new domain. Section 5 presents the implementation of our learning framework, with an overall overview of the major modules and data-processing steps. The framework is built upon the core machine learning methods for understanding, representing, and manipulating language, and on the standard language model for parsing, that are taught in computer science courses for high-schoolers. We describe the data-processing procedure for data analysis and the implementation of our model. These steps highlight the importance of implementing these standard ML methods with a context-dependent framework. Figure 1 displays the input to our method and shows a sequence of sentences. Figure 1: Sequence of sentences generated with LSD at high context model loss. Figure credit: LSD. Conclusion & Perspectives LSD is a useful tool for exploring contextual modeling, and this work presents a framework that exploits its strengths.

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