3,664 research outputs found
Improving the translation environment for professional translators
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side.
This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project
Highlighting matched and mismatched segments in translation memory output through sub-Âtree alignment
In recent years, it is becoming more and more clear that the
localisation industry does not have the necessary manpower to satisfy the increasing demand for high-quality translation. This has fuelled the search new and existing technologies that would increase translator throughput. As Translation Memory (TM) systems are the most commonly employed tool by translators, a number of enhancements are
available to assist them in their job. One such enhancement would be to show the translator which parts of the sentence
that needs to be translated match which parts of the fuzzy
match suggested by the TM. For this information to be used,
however, the translators have to carry it over to the TM
translation themselves. In this paper, we present a novel methodology that can automatically detect and highlight
the segments that need to be modified in a TM-Âsuggested
translation. We base it on state-Âof-the-art sub-Âtree align-
ment technology (Zhechev,2010) that can produce aligned
phrase-Âbased-Âtree pairs from unannotated data. Our system
operates in a three-Âstep process. First, the fuzzy match
selected by the TM and its translation are aligned. This
lets us know which segments of the source-Âlanguage sentence
correspond to which segments in its translation. In the
second step, the fuzzy match is aligned to the input sentence that is currently being translated. This tells us
which parts of the input sentence are available in the fuzzy
match and which still need to be translated. In the third
step, the fuzzy match is used as an intermediary, through
which the alignments between the input sentence and the TM
translation are established. In this way, we can detect with
precision the segments in the suggested translation that the
translator needs to edit and highlight them appropriately to
set them apart from the segments that are already good translations for parts of the input sentence. Additionally,
we can show the alignmentsâas detected by our systemâbetween
the input and the translation, which will make it even easier for the translator to post-edit the TM suggestion. This alignment information can additionally be used to pre-
translate the mismatched segments, further reducing the post-Âediting load
Neural Mechanisms for Information Compression by Multiple Alignment, Unification and Search
This article describes how an abstract framework for perception and cognition may be realised in terms of neural mechanisms and neural processing.
This framework â called information compression by multiple alignment, unification and search (ICMAUS) â has been developed in previous research as a generalized model of any system for processing information, either natural or
artificial. It has a range of applications including the analysis and production of natural language, unsupervised inductive learning, recognition of objects and patterns, probabilistic reasoning, and others. The proposals in this article may be seen as an extension and development of
Hebbâs (1949) concept of a âcell assemblyâ.
The article describes how the concept of âpatternâ in the ICMAUS framework may be mapped onto a version of the cell
assembly concept and the way in which neural mechanisms may achieve the effect of âmultiple alignmentâ in the ICMAUS framework.
By contrast with the Hebbian concept of a cell assembly, it is proposed here that any one neuron can belong in one assembly and only one assembly. A key feature of present proposals, which is not part of the Hebbian concept, is that any cell assembly may contain âreferencesâ or âcodesâ that serve to identify one or more other cell assemblies. This mechanism allows information to be stored in a compressed form, it provides a robust mechanism by which assemblies may be connected to form hierarchies and other kinds of structure, it means that assemblies can express
abstract concepts, and it provides solutions to some of the other problems associated with cell assemblies.
Drawing on insights derived from the ICMAUS framework, the article also describes how learning may be achieved with neural mechanisms. This concept of learning is significantly different from the Hebbian concept and appears to provide a better account of what we know about human learning
Towards Example-Based NMT with Multi-Levenshtein Transformers
Retrieval-Augmented Machine Translation (RAMT) is attracting growing
attention. This is because RAMT not only improves translation metrics, but is
also assumed to implement some form of domain adaptation. In this contribution,
we study another salient trait of RAMT, its ability to make translation
decisions more transparent by allowing users to go back to examples that
contributed to these decisions.
For this, we propose a novel architecture aiming to increase this
transparency. This model adapts a retrieval-augmented version of the
Levenshtein Transformer and makes it amenable to simultaneously edit multiple
fuzzy matches found in memory. We discuss how to perform training and inference
in this model, based on multi-way alignment algorithms and imitation learning.
Our experiments show that editing several examples positively impacts
translation scores, notably increasing the number of target spans that are
copied from existing instances.Comment: 17 pages, EMNLP 2023 submissio
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