Fast and accurate sentence alignment of bilingual corpora

By Robert C. Moore. Conference of the Association for Machine Translation in the Americas, 2002

353 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/71bdabaa757f2c6d31ac119c7f55dab03b60802f

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In Artificial Intelligence

Citations in the corpus

Improving Machine Translation Performance by Exploiting Non-Parallel Corpora (2005, neutral): “Traditional sentence alignment algorithms (Gale and Church 1991; Wu 1994; Fung and Church 1994; Melamed 1999; Moore 2002) are designed to align sentences in parallel corpora and operate on the assumption that there are no reorderings and only limited insertions and deletions between the two…”
Set-Theoretic Alignment for Comparable Corpora (2016, background): “• Two sentence-related features: source and target length ratio modeled through a Poisson distribution (Moore, 2002), and relative position of source and target sentences in the document.”
Corpora for Three South African Language Pairs in the Autshumato Project (2010, methodology): “Microsoft's bilingual sentence aligner (Moore, 2002) is used to automatically align sentences.”
Extracting Parallel Sentences from Comparable Corpora using Document Level Alignment (2010, methodology): “One is a sentence length feature taken from (Moore, 2002), which models the length ratio between the source and target sentences with a Poisson distribution.”
Analyzing Parallelism and Domain Similarities in the MAREC Patent Corpus (2012, methodology): “For alignment, we used Gargantua(9) ([8]), an unsupervised, language pair independent open source aligner, which implements the idea presented in [11]: a combination of both sentence-length-based alignment and a lexical translation model in a two-pass approach.”
Findings of the WMT 2016 Bilingual Document Alignment Shared Task (2016, methodology): “, 2005), Gargantua5 (Braune and Fraser, 2010), Bilingual Sentence Aligner (Moore, 2002) Bleualign6 (Sennrich and Volk, 2010), and Champollion7 (Ma, 2006).”
Findings of the 2009 Workshop on Statistical Machine Translation (2009, methodology): “They were sentence-aligned in batches of 10,000 document pairs, using a sentence aligner that incorporates IBM Model 1 probabilities in addition to sentence lengths (Moore, 2002).”
ParaCrawl: Web-Scale Acquisition of Parallel Corpora (2020, background): “Later work added lexical features and heuristics to speed up search, such as limiting the search space to be near the diagonal (Moore, 2002; Varga et al., 2005).”
UM-Corpus: A Large English-Chinese Parallel Corpus for Statistical Machine Translation (2014, background): “…work of Patry and Langlais (2005) in document alignment (with a precision of 99%), the works of Koehn (2005) and Gillick (2009) in sentence boundary detection (error rates on test news data are less than 0.25%), and the work of Moore (2002) in sentence alignment (it achieves 99.34% in precision).”
Hindi-to-Urdu Machine Translation through Transliteration (2010, methodology): “From these we were able to sentence-align 7000 sentence pairs using the sentence alignment algorithm given by Moore (2002).”
The IIT Bombay English-Hindi Parallel Corpus (2017, methodology): “We used the sentence alignment technique proposed by Moore (2002) to extract parallel corpora from this comparable corpus.”
Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus (2026, unknown): “Most of the sentence alignment algorithms are designed to work with unordered corpora [see, e.g., 23, 42].”