CNGL-CORE: Referential translation machines for measuring semantic similarity

We invent referential translation machines (RTMs), a computational model for identifying the translation acts between any two data sets with respect to a reference corpus selected in the same domain, which can be used for judging the semantic similarity between text. RTMs make quality and semantic similarity judgments possible by using retrieved relevant training data as interpretants for reaching shared semantics. An MTPP (machine translation performance predictor) model derives features measuring the closeness of the test sentences to the training data, the difficulty of translating them, and the presence of acts of translation involved. We view semantic similarity as paraphrasing between any two given texts. Each view is modeled by an RTM model, giving us a new perspective on the binary relationship between the two. Our prediction model is the $15$th on some tasks and $30$th overall out of $89$ submissions in total according to the official results of the Semantic Textual Similarity (STS 2013) challenge

CNGL: Grading student answers by acts of translation

We invent referential translation machines (RTMs), a computational model for identifying the translation acts between any two data sets with respect to a reference corpus selected in the same domain, which can be used for automatically grading student answers. RTMs make quality and semantic similarity judgments possible by using retrieved relevant training data as interpretants for reaching shared semantics. An MTPP (machine translation performance predictor) model derives features measuring the closeness of the test sentences to the training data, the difficulty of translating them, and the presence of acts of translation involved. We view question answering as translation from the question to the answer, from the question to the reference answer, from the answer to the reference answer, or from the question and the answer to the reference answer. Each view is modeled by an RTM model, giving us a new perspective on the ternary relationship between the question, the answer, and the reference answer. We show that all RTM models contribute and a prediction model based on all four perspectives performs the best. Our prediction model is the $2$nd best system on some tasks according to the official results of the Student Response Analysis (SRA 2013) challenge

Referential translation machines for quality estimation

We introduce referential translation machines (RTM) for quality estimation of translation outputs. RTMs are a computational model for identifying the translation acts between any two data sets with respect to a reference corpus selected in the same domain, which can be used for estimating the quality of translation outputs, judging the semantic similarity between text, and evaluating the quality of student answers. RTMs achieve top performance in automatic, accurate, and language independent prediction of sentence-level and word-level statistical machine translation (SMT) quality. RTMs remove the need to access any SMT system specific information or prior knowledge of the training data or models used when generating the translations. We develop novel techniques for solving all subtasks in the WMT13 quality estimation (QE) task (QET 2013) based on individual RTM models. Our results achieve improvements over last year’s QE task results (QET 2012), as well as our previous results, provide new features and techniques for QE, and rank 1st or 2nd in all of the subtasks

Feature decay algorithms for fast deployment of accurate statistical machine translation systems

We use feature decay algorithms (FDA) for fast deployment of accurate statistical machine translation systems taking only about half a day for each translation direction. We develop parallel FDA for solving computational scalability problems caused by the abundance of training data for SMT models and LM models and still achieve SMT performance that is on par with using all of the training data or better. Parallel FDA runs separate FDA models on randomized subsets of the training data and combines the instance selections later. Parallel FDA can also be used for selecting the LM corpus based on the training set selected by parallel FDA. The high quality of the selected training data allows us to obtain very accurate translation outputs close to the top performing SMT systems. The relevancy of the selected LM corpus can reach up to 86% reduction in the number of OOV tokens and up to 74% reduction in the perplexity. We perform SMT experiments in all language pairs in the WMT13 translation task and obtain SMT performance close to the top systems using significantly less resources for training and development

Referential translation machines for predicting translation quality

We use referential translation machines (RTM) for quality estimation of translation outputs. RTMs are a computational model for identifying the translation acts between any two data sets with respect to interpretants selected in the same domain, which are effective when making monolingual and bilingual similarity judgments. RTMs achieve top performance in automatic, accurate, and language independent prediction of sentence-level and word-level statistical machine translation (SMT) quality. RTMs remove the need to access any SMT system specific information or prior knowledge of the training data or models used when generating the translations and achieve the top performance in WMT13 quality estimation task (QET13). We improve our RTM models with the Parallel FDA5 instance selection model, with additional features for predicting the translation performance, and with improved learning models. We develop RTM models for each WMT14 QET (QET14) subtask, obtain improvements over QET13 results, and rank $1$st in all of the tasks and subtasks of QET14

RTM-DCU: referential translation machines for semantic similarity

We use referential translation machines (RTMs) for predicting the semantic similarity of text. RTMs are a computational model for identifying the translation acts between any two data sets with respect to interpretants selected in the same domain, which are effective when making monolingual and bilingual similarity judgments. RTMs judge the quality or the semantic similarity of text by using retrieved relevant training data as interpretants for reaching shared semantics. We derive features measuring the closeness of the test sentences to the training data via interpretants, the difficulty of translating them, and the presence of the acts of translation, which may ubiquitously be observed in communication. RTMs provide a language independent approach to all similarity tasks and achieve top performance when predicting monolingual cross-level semantic similarity (Task 3) and good results in semantic relatedness and entailment (Task 1) and multilingual semantic textual similarity (STS) (Task 10). RTMs remove the need to access any task or domain specific information or resource

Definition of interfaces

The aim of this report is to define the interfaces for the tools used in the MT development and evaluation scenarios as included in the QTLaunchPad (QTLP) infrastructure. Specification of the interfaces is important for the interaction and interoperability of the tools in the developed QTLP infrastructure. In addressing this aim, the report provides: 1. Descriptions of the common aspects of the tools and their standardized data formats; 2. Descriptions of the interfaces for the tools for interoperability. where the tools are categorized into preparation, development, and evaluation categories including the human interfaces for quality assessment with multidimensional quality metrics. Interface specifications allow a modular tool infrastructure, flexibly selecting among alternative implementations, enabling realistic expectations to be made at different sections of the QTLP information flow pipeline, and supporting the QTLP infrastructure. D3.2.1 allows the emergence of the QTLP infrastructure and helps the identification and acquisition of existing tools (D4.4.1), the integration of identified language processing tools (D3.3.1), their implementation (D3.4.1), and their testing (D3.5.1). QTLP infrastructure will facilitate the organization and running of the quality translation shared task (D5.2.1). We also provide human interfaces for translation quality assessment with the multidimensional quality metrics (D1.1.1). D3.2.1 is a living document until M12, which is when the identification and acquisition of existing tools (D4.4.1) and the implementation of identified language processing tools (D3.4.1) are due

Multi-task and Multi-view Learning for Predicting Adverse Drug Reactions

Adverse drug reactions (ADRs) present a major concern for drug safety and are a major obstacle in modern drug development. They account for about one-third of all late-stage drug failures, and approximately 4% of all new chemical entities are withdrawn from the market due to severe ADRs. Although off-target drug interactions are considered to be the major causes of ADRs, the adverse reaction profile of a drug depends on a wide range of factors such as specific features of drug chemical structures, its ADME/PK properties, interactions with proteins, the metabolic machinery of the cellular environment, and the presence of other diseases and drugs. Hence computational modeling for ADRs prediction is highly complex and challenging. We propose a set of statistical learning models for effective ADRs prediction systematically from multiple perspectives. We first discuss available data sources for protein-chemical interactions and adverse drug reactions, and how the data can be represented for effective modeling. We also employ biological network analysis approaches for deeper understanding of the chemical biological mechanisms underlying various ADRs. In addition, since protein-chemical interactions are an important component for ADRs prediction, identifying these interactions is a crucial step in both modern drug discovery and ADRs prediction. The performance of common supervised learning methods for predicting protein-chemical interactions have been largely limited by insufficient availability of binding data for many proteins. We propose two multi-task learning (MTL) algorithms for jointly predicting active compounds of multiple proteins, and our methods outperform existing states of the art significantly. All these related data, methods, and preliminary results are helpful for understanding the underlying mechanisms of ADRs and further studies. ADRs data are complex and noisy, and in many cases we do not fully understand the molecular mechanisms of ADRs. Due to the noisy and heterogeneous data set available for some ADRs, we propose a sparse multi-view learning (MVL) algorithm for predicting a specific ADR - drug-induced QT prolongation, a major life-threatening adverse drug effect. It is crucial to predict the QT prolongation effect as early as possible in drug development. MVL algorithms work very well when complex data from diverse domains are involved and only limited labeled examples are available. Unlike existing MVL methods that use L2-norm co-regularization to obtain a smooth objective function, we propose an L1-norm co-regularized MVL algorithm for predicting QT prolongation, reformulate the objective function, and obtain its gradient in the analytic form. We optimize the decision functions on all views simultaneously and achieve 3-4 fold higher computational speedup, comparing to previous L2-norm co-regularized MVL methods that alternately optimizes one view with the other views fixed until convergence. L1-norm co-regularization enforces sparsity in the learned mapping functions and hence the results are expected to be more interpretable. The proposed MVL method can only predict one ADR at a time. It would be advantageous to predict multiple ADRs jointly, especially when these ADRs are highly related. Advanced modeling techniques should be investigated to better utilize ADR data for more effective ADRs prediction. We study the quantitative relationship among drug structures, drug-protein interaction profiles, and drug ADRs. We formalize the modeling problem as a multi-view (drug structure data and drug-protein interaction profile data) multi-task (one drug may cause multiple ADRs and each ADR is a task) classification problem. We apply the co-regularized MVL on each ADR and use regularized MTL to increase the total sample size and improve model performance. Experimental studies on the ADR data set demonstrate the effectiveness of our MVMT algorithm. Cluster analysis and significant feature identification using the results of our models reveal interesting hidden insight. In summary, we use computational methods such as biological network analysis, multi-task learning, multi-view learning, and inductive multi-view multi-task learning to systematically investigate the modeling of various ADRs, and construct highly accurate models for ADRs prediction. We also have significant contribution on proposing novel supervised and semi-supervised learning algorithms, which can be applied to many other real-world applications

Findings of the 2011 Workshop on Statistical Machine Translation

This paper presents the results of the WMT11 shared tasks, which included a translation task, a system combination task, and a task for machine translation evaluation metrics. We conducted a large-scale manual evaluation of 148 machine translation systems and 41 system combination entries. We used the ranking of these systems to measure how strongly automatic metrics correlate with human judgments of translation quality for 21 evaluation metrics. This year featured a Haitian Creole to English task translating SMS messages sent to an emergency response service in the aftermath of the Haitian earthquake. We also conducted a pilot 'tunable metrics' task to test whether optimizing a fixed system to different metrics would result in perceptibly different translation quality

Systems definition study for shuttle demonstration flights of large space structures, Volume 2: Technical Report

The development of large space structure (LSS) technology is discussed, with emphasis on space fabricated structures which are automatically manufactured in space from sheet-strip materials and assembled on-orbit. It is concluded that an LSS flight demonstration using an Automated Beam Builder and the orbiter as a construction base, could be performed in the 1983-1984 time period. The estimated cost is $24 million exclusive of shuttle launch costs. During the mission, a simple space platform could be constructed in-orbit to accommodate user requirements associated with earth viewing and materials exposure experiments needs