We introduce a stochastic grammatical channel model for machine translation, that synthesizes several desirable characteristics of both statistical and grammatical machine translation. As with the pure statistical translation model described by Wu (1996) (in which a bracketing transduction grammar models the channel), alternative hypotheses compete probabilistically, exhaustive search of the tr...

This paper presents a hybrid approach to the enhancement of English to Arabic statistical machine translation quality. Machine Translation has been defined as the process that utilizes computer software to translate text from one natural language to another. Arabic, as a morphologically rich language, is a highly flexional language, in that the same root can lead to various forms according to i...

We consider the problem of building probabilistic models that jointly model short natural language utterances and source code snippets. The aim is to bring together recent work on statistical modelling of source code and work on bimodal models of images and natural language. The resulting models are useful for a variety of tasks that involve natural language and source code. We demonstrate thei...

R is a very powerful framework for statistical modeling. Thus, it is of high importance to integrate R with state-of-theart tools in NLP. In this paper, we present the functionality and architecture of such an integration by means of TextImager. We use the OpenCPU API to integrate R based on our own R-Server. This allows for communicating with R-packages and combining them with TextImager’s NLP...

A robot’s ability to understand or ground natural language instructions is fundamentally tied to its knowledge about the surrounding world. We present an approach to grounding natural language utterances in the context of factual information gathered through natural-language interactions and past visual observations. A probabilistic model estimates, from a natural language utterance, the object...

Abstract In the field of machine translation, significant progress has been made by using statistical methods. In this paper we suggest a statistical machine translation system for Sign Language and written language, especially for the language pair German Sign Language (DGS) and German. After introducing the system’s architecture, statistical machine translation in general and notation systems...

Our research is on developing artificial intelligence-based approaches to help lay audiences to understand medical and other scientific arguments. Currently, we are developing a natural language generation system that will synthesize patient-tailored genetic counseling documents. In this position paper, we focus on two forms of internal representation to be used by the system: a qualitative cau...

The $64,000 question in computational linguistics these days is: “What should I read to learn about statistical natural language processing?” I have been asked this question over and over, and each time I have given basically the same reply: there is no text that addresses this topic directly, and the best one can do is find a good probability-theory textbook and a good information-theory textb...

We introduce a stochastic grammatical channel model for machine translation, that synthesizes several desirable characteristics of both statistical and grammatical machine translation. As with the pure statistical translation model described by Wu (1996) (in which a bracketing transduction grammar models the channel), alternative hypotheses compete probabilistically, exhaustive search of the tr...

We report on findings of exploiting large data sets for translation modeling, language modeling and tuning for the development of competitive machine translation systems for eight language pairs.