Joint Training for Neural Machine Translation Models with Monolingual Data

Monolingual data have been demonstrated to be helpful in improving translation quality of both statistical machine translation (SMT) systems and neural machine translation (NMT) systems, especially in resource-poor or domain adaptation tasks where parallel data are not rich enough. In this paper, we propose a novel approach to better leveraging monolingual data for neural machine translation by jointly learning source-to-target and target-to-source NMT models for a language pair with a joint EM optimization method. The training process starts with two initial NMT models pre-trained on parallel data for each direction, and these two models are iteratively updated by incrementally decreasing translation losses on training data. In each iteration step, both NMT models are first used to translate monolingual data from one language to the other, forming pseudo-training data of the other NMT model. Then two new NMT models are learnt from parallel data together with the pseudo training data. Both NMT models are expected to be improved and better pseudo-training data can be generated in next step. Experiment results on Chinese-English and English-German translation tasks show that our approach can simultaneously improve translation quality of source-to-target and target-to-source models, significantly outperforming strong baseline systems which are enhanced with monolingual data for model training including back-translation.Comment: Accepted by AAAI 201

Extension of Risk-Based Measure of Time-Varying Prognostic Discrimination for Survival Models

The Cox proportional hazards (PH) model and time dependent PH model are the most popular survival models in survival analysis. The hazard discrimination summary HDS(t) proposed by Liang and Heagerty [2017] is used to evaluate the mean hazard difference between cases and controls at time t. Liang and Heagerty [2017] evaluated the discrimination performance under the PH model and time dependent PH model with right censoring. In this thesis, first, we further investigate their method via comprehensive simulations including 1) We extend the simulation in Liang and Heagerty [2017] under the PH model by adding more scenarios such as different distributions, censoring proportions under the PH model; and 2) similarly, more situations were added to time dependent PH model such as different time dependent functions. Second, we develop an estimation method of HDS(t) for the PH model with interval censored data. Third, we apply the proposed method to HIV data from Health Sciences South Carolina (HSSC)

Financial liberalisation and international market interdependence: evidence from China’s stock market in the post-WTO accession period

This paper studies China’s stock market with respect to financial liberalization and international market interdependence after its accession to the WTO in 2001. Using the multi-factor R-squared measure, we derive a normalized index to measure the impact of financial liberalization policies on stock market interdependence between China and the world. Some of China’s financial liberalization measures, such as QFII and exchange rate reform, are found to have played an important role in increasing market interdependence. After the US credit crunch in 2007 and the world financial crisis in the following years, some anomalies were observed as China’s stock market was more interdependent of the global market than the US stock market in some specific periods. These anomalies may have been related to the former’s overreaction and economic overheating

Efficient Estimation of General Treatment Effects using Neural Networks with A Diverging Number of Confounders

The estimation of causal effects is a primary goal of behavioral, social, economic and biomedical sciences. Under the unconfounded treatment assignment condition, adjustment for confounders requires estimating the nuisance functions relating outcome and/or treatment to confounders. The conventional approaches rely on either a parametric or a nonparametric modeling strategy to approximate the nuisance functions. Parametric methods can introduce serious bias into casual effect estimation due to possible mis-specification, while nonparametric estimation suffers from the "curse of dimensionality". This paper proposes a new unified approach for efficient estimation of treatment effects using feedforward artificial neural networks when the number of covariates is allowed to increase with the sample size. We consider a general optimization framework that includes the average, quantile and asymmetric least squares treatment effects as special cases. Under this unified setup, we develop a generalized optimization estimator for the treatment effect with the nuisance function estimated by neural networks. We further establish the consistency and asymptotic normality of the proposed estimator and show that it attains the semiparametric efficiency bound. The proposed methods are illustrated via simulation studies and a real data application

On the sixth power mean values of a generalized two-term exponential sums

This paper examines the evaluations of sixth power mean values of a generalized two-term exponential sums. In the case $p\equiv 3\bmod 4$, we try to establish two precise formulas by applying the properties of character sums and the number of the solutions of relevant congruence equations modulo an odd prime $p$