A Program for Aligning Sentences in Bilingual Corpora
Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" does not allow redistributing the text itself. Original — aclanthology.org/J93-1004.pdf
Summary
Gale and Church tackle sentence alignment: deciding which sentences in a text correspond to which sentences in its translation. This is a prerequisite for statistical machine translation and bilingual lexicography. Earlier systems were mostly unpublished, slow, or poorly evaluated. The authors present a program, align, that uses no lexical information. It relies only on the observation that sentence lengths, counted in characters, are strongly correlated across translations.
Each candidate pairing is scored probabilistically from the normalized length difference and its variance, including 1-1, 1-2, 2-1 and 2-2 matches. Dynamic programming then selects the most likely overall alignment. Paragraphs are aligned first, then sentences within each paragraph.
On a trilingual UBS corpus of banking reports in English, French and German, about 4% of alignments were wrong. Keeping only the highest-scoring alignments yields a much cleaner subset. The authors also aligned roughly 90 million words of Canadian Hansards for release through ACL/DCI. The method's speed, simplicity and openness made large parallel corpora practical to use.
Key points
- The evaluation data were 15 UBS economic reports, with 14,680 words, 725 sentences and 188 paragraphs on the English side. Across English-French and English-German, the overall error rate was 4.2% on 1316 alignments, with English-French producing more errors.
- Restricting output to the best-scoring 80% of alignments cut the error rate to 0.7%.
- Counting length in characters worked better than counting words. Word-based lengths raised the error rate from 4.2% to 6.5%. Paragraph lengths and their translations correlated at 0.991.
- The same language-independent parameters were used for both language pairs. Fitting pair-specific values left the total error count unchanged.
- The aligned Canadian Hansards (about 90M words) were released via ACL/DCI, and the paper includes C code for the program's core. Later extensions added hard and soft delimiters, and the authors suggest lexical cues as a future fallback.
Cited by
- Adaptive Sentence Boundary Disambiguation
- ParaCrawl: Web-Scale Acquisition of Parallel Corpora
- Extracting Paraphrases from a Parallel Corpus
- Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging
- Accurate Methods for the Statistics of Surprise and Coincidence
- Generating Phrasal and Sentential Paraphrases: A Survey of Data-Driven Methods
- Improving Machine Translation Performance by Exploiting Non-Parallel Corpora
- Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora
Cite
@article{gale1993program,
title = {{A Program for Aligning Sentences in Bilingual Corpora}},
author = {Gale, William A. and Church, Kenneth W.},
journal = {Computational Linguistics},
year = {1993},
url = {https://aclanthology.org/J93-1004/}
}References
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