Bitext Alignment
By Jörg Tiedemann. 2011
97 citations recorded by the source corpus.
This book provides an overview of various techniques for the alignment of bitexts. It describes general concepts and strategies that can be applied to map corresponding parts in parallel documents on various levels of granularity. Bitexts are valuable linguistic resources for many different research fields and practical applications. The most predominant application is machine translation, in particular, statistical machine translation. However, there are various other threads that can be followed which may be supported by the rich linguistic knowledge implicitly stored in parallel resources. Bitexts have been explored in lexicography, word sense disambiguation, terminology extraction, computer-aided language learning and translation studies to name just a few. The book covers the essential tasks that have to be carried out when building parallel corpora starting from the collection of translated documents up to sub-sentential alignments. In particular, it describes various approaches to document alignment, sentence alignment, word alignment and tree structure alignment. It also includes a list of resources and a comprehensive review of the literature on alignment techniques. Table of Contents: Introduction / Basic Concepts and Terminology / Building Parallel Corpora / Sentence Alignment / Word Alignment / Phrase and Tree Alignment / Concluding Remarks
Subfields: Artificial Intelligence, Information Systems, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jimmy J. Lin
- Philipp Koehn
- Rodrigo Nogueira
- Kenneth Heafield
- Kyunghyun Cho
- Andrew Yates
- Felix Stahlberg
- Barry Haddow
In Information Systems
- Rodrigo Nogueira
- Andrew Yates
- Jimmy J. Lin
- Udeepa Meepegama
- Brian Thompson
- Raphael Lenain
- Hieu D. Hoang
- Kenneth Heafield
In Natural Language Processing
- Philipp Koehn
- Barry Haddow
- Kenneth Heafield
- Nadir Durrani
- Jimmy J. Lin
- Ondrej Bojar
- Rico Sennrich
- Felix Stahlberg
Citations in the corpus
- Pretrained Transformers for Text Ranking: BERT and Beyond (2020, background): “An entirely different example comes from machine translation: In modern systems, such as those built by Facebook and Google referenced in the introduction, translation models are learned from a parallel corpus (also called bitext), comprised of pairs of sentences in two languages that are translatio”
- ParaCrawl: Web-Scale Acquisition of Parallel Corpora (2020, background): “(Tiedemann, 2011) describes some of the challenges in greater detail.”
- Generative Pretraining for Paraphrase Evaluation (2021, methodology): “This is 160 similar to the statistical method of bitext align161 ment (Tiedemann, 2011).”