Explain to me like I am five -- Sentence Simplification Using Transformers

Sentence simplification aims at making the structure of text easier to read and understand while maintaining its original meaning. This can be helpful for people with disabilities, new language learners, or those with low literacy. Simplification often involves removing difficult words and rephrasing the sentence. Previous research have focused on tackling this task by either using external linguistic databases for simplification or by using control tokens for desired fine-tuning of sentences. However, in this paper we purely use pre-trained transformer models. We experiment with a combination of GPT-2 and BERT models, achieving the best SARI score of 46.80 on the Mechanical Turk dataset, which is significantly better than previous state-of-the-art results. The code can be found at https://github.com/amanbasu/sentence-simplification

Effective Evaluation using Logged Bandit Feedback from Multiple Loggers

Accurately evaluating new policies (e.g. ad-placement models, ranking functions, recommendation functions) is one of the key prerequisites for improving interactive systems. While the conventional approach to evaluation relies on online A/B tests, recent work has shown that counterfactual estimators can provide an inexpensive and fast alternative, since they can be applied offline using log data that was collected from a different policy fielded in the past. In this paper, we address the question of how to estimate the performance of a new target policy when we have log data from multiple historic policies. This question is of great relevance in practice, since policies get updated frequently in most online systems. We show that naively combining data from multiple logging policies can be highly suboptimal. In particular, we find that the standard Inverse Propensity Score (IPS) estimator suffers especially when logging and target policies diverge -- to a point where throwing away data improves the variance of the estimator. We therefore propose two alternative estimators which we characterize theoretically and compare experimentally. We find that the new estimators can provide substantially improved estimation accuracy.Comment: KDD 201

Financial Integration for India Stock Market, a Fractional Cointegration Approach

The Indian stock market is one of the earliest in Asia being in operation since 1875, but remained largely outside the global integration process until the late 1980s. A number of developing countries in concert with the International Finance Corporation and the World Bank took steps in the 1980s to establish and revitalize their stock markets as an effective way of mobilizing and allocation of finance. In line with the global trend, reform of the Indian stock market began with the establishment of Securities and Exchange Board of India in 1988. This paper empirically investigates the long-run equilibrium relationship and short-run dynamic linkage between the Indian stock market and the stock markets in major developed countries (United States, United Kingdom and Japan) after 1990 by examining the Granger causality relationship and the pairwise, multiple and fractional cointegrations between the Indian stock market and the stock markets from these three developed markets. We conclude that Indian stock market is integrated with mature markets and sensitive to the dynamics in these markets in a long run. In a short run, both US and Japan Granger causes the Indian stock market but not vice versa. In addition, we find that the Indian stock index and the mature stock indices form fractionally cointegrated relationship in the long run with a common fractional, nonstationary component and find that the Johansen method is the best reveal their cointegration relationship.unit root test, cointegration, Error Correction Model, Vector Autoregression Model, Johansen Multivariate Cointegration, Fractional Cointegration

Estimating Position Bias without Intrusive Interventions

Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal. While it was recently shown how counterfactual learning-to-rank (LTR) approaches \cite{Joachims/etal/17a} can provably overcome presentation bias when observation propensities are known, it remains to show how to effectively estimate these propensities. In this paper, we propose the first method for producing consistent propensity estimates without manual relevance judgments, disruptive interventions, or restrictive relevance modeling assumptions. First, we show how to harvest a specific type of intervention data from historic feedback logs of multiple different ranking functions, and show that this data is sufficient for consistent propensity estimation in the position-based model. Second, we propose a new extremum estimator that makes effective use of this data. In an empirical evaluation, we find that the new estimator provides superior propensity estimates in two real-world systems -- Arxiv Full-text Search and Google Drive Search. Beyond these two points, we find that the method is robust to a wide range of settings in simulation studies

Caustics in the sine-Gordon model from quenches in coupled 1D Bose gases

Caustics are singularities that occur naturally in optical, hydrodynamic and quantum waves, giving rise to high amplitude patterns that can be described using catastrophe theory. In this paper we study caustics in a statistical field theory setting in the form of the sine-Gordon model that describes a variety of physical systems including coupled 1D superfluids. Specifically, we use classical field simulations to study the dynamics of two ultracold 1D Bose gases (quasi-condensates) that are suddenly coupled to each other and find that the resulting non-equilibrium dynamics are dominated by caustics. Thermal noise is included by sampling the initial states from a Boltzmann distribution for phononic excitations. We find that caustics pile up over time in both the number and phase difference observables leading to a characteristic non-thermal `circus tent' shaped probability distribution at long times.Comment: 28 pages, 13 figure