We describe an architecture that allows to combine statistical machine translation (SMT) with rule-based machine translation (RBMT) in a multi-engine setup. We use a variant of standard SMT technology to align translations from one or more RBMT systems with the source text. We incorporate phrases extracted from these alignments into the phrase table of the SMT system and use the open-source dec...

This article shows how the automatic disambiguation of discourse connectives can improve Statistical Machine Translation (SMT) from English to French. Connectives are firstly disambiguated in terms of the discourse relation they signal between segments. Several classifiers trained using syntactic and semantic features reach stateof-the-art performance, with F1 scores of 0.6 to 0.8 over thirteen...

The SC project arose out of the recognition that the Symbolic Computation and Satisfiability Checking communities mutually benefit from the sharing of results and techniques. An SMT solver can profit from the inclusion of computer algebra techniques in its theory solver, while a computer algebra system can profit from dispatching SAT or SMT queries which arise as sub-problems during computation...

We build a grammatical error correction (GEC) system primarily based on the state-of-the-art statistical machine translation (SMT) approach, using task-specific features and tuning, and further enhance it with the modeling power of neural network joint models. The SMT-based system is weak in generalizing beyond patterns seen during training and lacks granularity below the word level. To address...

The Resource-Constrained Project Scheduling Problem (RCPSP) and some of its extensions have been widely studied. Many approaches have been considered to solve this problem: constraint programming (CP), Boolean satisfiability (SAT), mixed integer linear programming (MILP), branch and bound algorithms (BB) and others. In this paper, we present a new approach for solving this problem: satisfiabili...

We describe an architecture that allows to combine statistical machine translation (SMT) with rule-based machine translation (RBMT) in a multi-engine setup. We use a variant of standard SMT technology to align translations from one or more RBMT systems with the source text. We incorporate phrases extracted from these alignments into the phrase table of the SMT system and use the open-source dec...

This paper proposes an unsupervised word segmentation algorithm that identifies word boundaries in continuous source language text in order to improve the translation quality of statistical machine translation (SMT) approaches. The method can be applied to any language pair where the source language is unsegmented and the target language segmentation is known. First, an iterative bootstrap meth...

SMT solvers have been used successfully as reasoning engines for automated verification and other applications based on automated reasoning. Current techniques for dealing with quantified formulas in SMT are generally incomplete, forcing SMT solvers to report “unknown” when they fail to prove the unsatisfiability of a formula with quantifiers. This inability to return counter-models limits thei...

To automatically detect SMT (surface mount technology) electronic modules, algorithms are investigated for geometry and shape features, module matching and detection in PCB (printed-circuit board) components. First, geometry and shape features of SMT electronic modules are presented. Then matching technology and process for SMT electronic modules are studied through optimization searching algor...

Current Statistical Machine Translation (SMT) systems translate texts sentence by sentence without considering any cross-sentential context. Assuming independence between sentences makes it difficult to take certain translation decisions when the necessary information cannot be determined locally. We argue for the necessity to include crosssentence dependencies in SMT. As a case in point, we st...