Parallel text alignment is a key procedure in the automated translation area. A large number of aligners have been presented along the years, but these require that the target resources have been pre-prepared for alignment (either manually or automatically). It is rather normal to encounter mixed language documents, that is, documents where the same information is written in many languages (Ex: manuals of electronic devices, touristic information, PhD thesis with dual language abstracts, etc). In this article we present MLT-prealigner: a tool aimed at helping those that need to process mixed texts in order to feed alignment tools and other related language systems
With the advent of end-to-end deep learning approaches in machine translation, interest in word alig...
We present an algorithm for bilingual word alignment that extends previous work by treating multi-wo...
This paper describes the work achieved in the #rst half of a 4-year cooperative research project #AR...
Parallel text alignment is a key procedure in the automated translation area. A large number of alig...
Abstract: Classical methods for parallel text alignment consider one specific level (e.g. sentences)...
While alignment of texts on the sentential level is often seen as being too coarse, and word alignme...
This paper describes the opportunities that arise from automatic word alignment for bilingual concor...
Abstract. Parallel text alignment is a special type of pattern recognition task aimed to discover th...
Abstract. An adaptable statistical or hybrid MT system relies heav-ily on the quality of word-level ...
International audienceThis paper describes a new alignment method that extracts high quality multi-w...
While alignment of texts on the sentential level is often seen as being too coarse, and word align-m...
This paper focuses on investigation of the parallel corpora role as a linguistic recourse. The appli...
After outlining a short and select history of (the usefulness of) parallel texts and alignment, this...
Parallel corpora are usually a collection of documents which are translations of each other. To be u...
Alignment of words and multiword units plays an important role in many natural language processing a...
With the advent of end-to-end deep learning approaches in machine translation, interest in word alig...
We present an algorithm for bilingual word alignment that extends previous work by treating multi-wo...
This paper describes the work achieved in the #rst half of a 4-year cooperative research project #AR...
Parallel text alignment is a key procedure in the automated translation area. A large number of alig...
Abstract: Classical methods for parallel text alignment consider one specific level (e.g. sentences)...
While alignment of texts on the sentential level is often seen as being too coarse, and word alignme...
This paper describes the opportunities that arise from automatic word alignment for bilingual concor...
Abstract. Parallel text alignment is a special type of pattern recognition task aimed to discover th...
Abstract. An adaptable statistical or hybrid MT system relies heav-ily on the quality of word-level ...
International audienceThis paper describes a new alignment method that extracts high quality multi-w...
While alignment of texts on the sentential level is often seen as being too coarse, and word align-m...
This paper focuses on investigation of the parallel corpora role as a linguistic recourse. The appli...
After outlining a short and select history of (the usefulness of) parallel texts and alignment, this...
Parallel corpora are usually a collection of documents which are translations of each other. To be u...
Alignment of words and multiword units plays an important role in many natural language processing a...
With the advent of end-to-end deep learning approaches in machine translation, interest in word alig...
We present an algorithm for bilingual word alignment that extends previous work by treating multi-wo...
This paper describes the work achieved in the #rst half of a 4-year cooperative research project #AR...