170,945 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
Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance
We introduce a new measure of distance between languages based on word
embedding, called word embedding language divergence (WELD). WELD is defined as
divergence between unified similarity distribution of words between languages.
Using such a measure, we perform language comparison for fifty natural
languages and twelve genetic languages. Our natural language dataset is a
collection of sentence-aligned parallel corpora from bible translations for
fifty languages spanning a variety of language families. Although we use
parallel corpora, which guarantees having the same content in all languages,
interestingly in many cases languages within the same family cluster together.
In addition to natural languages, we perform language comparison for the coding
regions in the genomes of 12 different organisms (4 plants, 6 animals, and two
human subjects). Our result confirms a significant high-level difference in the
genetic language model of humans/animals versus plants. The proposed method is
a step toward defining a quantitative measure of similarity between languages,
with applications in languages classification, genre identification, dialect
identification, and evaluation of translations
Semantic Tagging with Deep Residual Networks
We propose a novel semantic tagging task, sem-tagging, tailored for the
purpose of multilingual semantic parsing, and present the first tagger using
deep residual networks (ResNets). Our tagger uses both word and character
representations and includes a novel residual bypass architecture. We evaluate
the tagset both intrinsically on the new task of semantic tagging, as well as
on Part-of-Speech (POS) tagging. Our system, consisting of a ResNet and an
auxiliary loss function predicting our semantic tags, significantly outperforms
prior results on English Universal Dependencies POS tagging (95.71% accuracy on
UD v1.2 and 95.67% accuracy on UD v1.3).Comment: COLING 2016, camera ready versio
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