From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood

Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself. Consequently, we must search the space of programs for those that output the correct result, while not being misled by spurious programs: incorrect programs that coincidentally output the correct result. We connect two common learning paradigms, reinforcement learning (RL) and maximum marginal likelihood (MML), and then present a new learning algorithm that combines the strengths of both. The new algorithm guards against spurious programs by combining the systematic search traditionally employed in MML with the randomized exploration of RL, and by updating parameters such that probability is spread more evenly across consistent programs. We apply our learning algorithm to a new neural semantic parser and show significant gains over existing state-of-the-art results on a recent context-dependent semantic parsing task.Comment: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (2017

The QCD/SM Working Group: Summary Report

This Report documents the results obtained by the Working Group on Quantum ChromoDynamics and the Standard Model for the Workshop ``Physics at TeV Colliders'', Les Houches, France, 21 May - 1 June 2001. The account of uncertainties in Parton Distribution Functions is reviewed. Progresses in the description of multiparton final states at Next-to-Leading Order and the extension of calculations for precision QCD observables beyond this order are summarized. Various issues concerning the relevance of resummation for observables at TeV colliders is examined. Improvements to algorithms of jet reconstruction are discussed and predictions for diphoton and photon pi-zero production at the LHC are made for kinematic variables of interest regarding searches for a Higgs boson decaying into two photons. Finally, several improvements implemented in Monte-Carlo event generators are documented

Tree-Structured Grid Model of Line and Polarization Variability from Massive Binaries

We have developed a 3-D Monte Carlo radiative transfer model which computes line and continuum polarization variability for a binary system with an optically thick non-axisymmetric envelope. This allows us to investigate the complex (phase-locked) line and continuum polarization variability features displayed by many massive binaries: W-R+O, O+O, etc. An 8-way tree data structure constructed via a ``cell-splitting'' method allows for high precision with efficient use of computer resources. The model is not restricted to binary systems; it can easily be adapted to a system with an arbitrary density distribution and large density gradients. As an application to a real system, the phase dependent Stokes parameters (I, Q, U) and the phase dependent He I (5876) profiles of the massive binary system V444 Cyg (WN5+O6 III-V) are computed.Comment: 11 pages, 14 figures, accepted by Astronomy & Astrophysic