This part of the book is concerned with the inter-related issues of how
BP and signals intelligence were made over the course of WW2. The
focus is primarily upon various forms of organizational structuring,
broadly conceived, and the emphasis on ‘making’ indicates that, in line
with the general approach outlined in the introduction to the book, I
will seek to explore some of the processes of ‘organizing’ which lie
beneath the production of ‘organization’.
The task of aligning corresponding phrases across two related sentences is an important component of approaches for natural language problems such as textual inference, paraphrase detection and text-to-text generation. In this work, we examine a state-of-the-art structured prediction model for the alignment task which uses a phrase-based representation and is forced to decode alignments using an approximate search approach.
Minimum Error Rate Training (MERT) and Minimum Bayes-Risk (MBR) decoding are used in most current state-of-theart Statistical Machine Translation (SMT) systems. The algorithms were originally developed to work with N -best lists of translations, and recently extended to lattices that encode many more hypotheses than typical N -best lists. We here extend lattice-based MERT and MBR algorithms to work with hypergraphs that encode a vast number of translations produced by MT systems based on Synchronous Context Free Grammars.
In this work we present two extensions to the well-known dynamic programming beam search in phrase-based statistical machine translation (SMT), aiming at increased efﬁciency of decoding by minimizing the number of language model computations and hypothesis expansions.
In the late 1970s a new academic discipline was born: Translation Studies. We could not read literature in translation, it was argued, without asking ourselves if linguistics and cultural phenomena really were ‘translatable’ and exploring in some depth the concept of ‘equivalence’. When Susan Bassnett’s Translation Studies appeared in the New Accents series, it quickly became the one introduction every student and interested reader had to own.
The Executive Director reports directly to the Board of Directors of DIT Foundation.
The Executive Director is responsible for the strategic leadership of the DIT Foundation to ensure its
future relevance, credibility, and viability. He/she is responsible for establishing organisational
objectives and priorities and for reviewing and evaluating the progress and work for attainment of
objectives and performance goals.
The Executive Director will work in close co-operation with the Chair of DIT Foundation, the
President of DIT and the DIT Senior Leadership Team.
Having dealt with in-depth analysis of SS#7, GSM and GPRS networks I started to monitor
UTRAN interfaces approximately four years ago. Monitoring interfaces means decoding
the data captured on the links and analysing how the different data segments and messages
are related to each other. In general I wanted to trace all messages belonging to a single
call to prove if the network elements and protocol entities involved worked fine or if there
had been failures or if any kind of suspicious events had influenced the normal call
proceeding or the call’s quality of service.
This chapter provides pointers to help you to prepare for the CCIE Routing
and Switching written exam, including how to choose proper answers, how
to decode ambiguity, how to work within the Cisco testing framework, how
to decide what you need to memorize, and what to expect before, during,
and after the exam. After presenting exam pointers, this chapter supplies a
100-question sample test devised to quiz you on subject matter related to
Cisco CCIE Routing and Switching written exam 350-001. You’ll find the
answers to the sample test in Chapter 12....
Another class of linear codes, known as
We studied the structure of the encoder
and different ways for representing it.What are the state diagram and trellis
representation of the code?
How the decoding is performed for
What is a Maximum likelihood decoder?
What are the soft decisions and hard
How does the Viterbi algorithm work?
How decoding is performed for
What is a Maximum likelihood decoder?
What are soft decisions and hard
How does the Viterbi algorithm work?The demodulator makes a firm or hard decision
whether one or zero was transmitted and provides
no other information for the decoder such as how
reliable the decision is.
The large combined search space of joint word segmentation and Part-of-Speech (POS) tagging makes efﬁcient decoding very hard. As a result, effective high order features representing rich contexts are inconvenient to use. In this work, we propose a novel stacked subword model for this task, concerning both efﬁciency and effectiveness.
This paper extends the training and tuning regime for phrase-based statistical machine translation to obtain ﬂuent translations into morphologically complex languages (we build an English to Finnish translation system). Our methods use unsupervised morphology induction. Unlike previous work we focus on morphologically productive phrase pairs – our decoder can combine morphemes across phrase boundaries. Morphemes in the target language may not have a corresponding morpheme or word in the source language.
Multi-source statistical machine translation is the process of generating a single translation from multiple inputs. Previous work has focused primarily on selecting from potential outputs of separate translation systems, and solely on multi-parallel corpora and test sets. We demonstrate how multi-source translation can be adapted for multiple monolingual inputs.
We present a new open source toolkit for phrase-based and syntax-based machine translation. The toolkit supports several state-of-the-art models developed in statistical machine translation, including the phrase-based model, the hierachical phrase-based model, and various syntaxbased models. The key innovation provided by the toolkit is that the decoder can work with various grammars and offers different choices of decoding algrithms, such as phrase-based decoding, decoding as parsing/tree-parsing and forest-based decoding. ...
Word lattice decoding has proven useful in spoken language translation; we argue that it provides a compelling model for translation of text genres, as well. We show that prior work in translating lattices using ﬁnite state techniques can be naturally extended to more expressive synchronous context-free grammarbased models. Additionally, we resolve a signiﬁcant complication that non-linear word lattice inputs introduce in reordering models. Our experiments evaluating the approach demonstrate substantial gains for ChineseEnglish and Arabic-English translation. ...
Despite much recent progress on accurate semantic role labeling, previous work has largely used independent classiﬁers, possibly combined with separate label sequence models via Viterbi decoding. This stands in stark contrast to the linguistic observation that a core argument frame is a joint structure, with strong dependencies between arguments. We show how to build a joint model of argument frames, incorporating novel features that model these interactions into discriminative loglinear models. ...