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136
A Systematic Comparison of Various Statistical Alignment Models
- COMPUTATIONAL LINGUISTICS
, 2003
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Char_align: A Program for Aligning Parallel Texts at the Character Level
- In Proceedings of the 31st Annual Conference of the Association for Computational Linguistics
, 1993
"... There have been a number of recent papers on aligning parallel texts at the sentence level, e.g., Brown et al (1991), Gale and Church (to appear), Isabelle (1992), Kay and Ro .. senschein (to appear), Simard et al (1992), WarwickArmstrong and Russell (1990). On clean inputs, such as the Canadian Han ..."
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Cited by 132 (3 self)
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There have been a number of recent papers on aligning parallel texts at the sentence level, e.g., Brown et al (1991), Gale and Church (to appear), Isabelle (1992), Kay and Ro .. senschein (to appear), Simard et al (1992), WarwickArmstrong and Russell (1990). On clean inputs, such as the Canadian Hansards, these methods have been very successful (at least 96% correct by sentence). Unfortunately, if the input is noisy (due to OCR and/or unknown markup conventions), then these methods tend to break down because the noise can make it difficult to find paragraph boundaries, let alone sentences. This paper describes a new program, char_align, that aligns texts at the character level rather than at the sentence/paragraph level, based on the cognate approach proposed by Simard et al. 1. Introduction Parallel texts have recently received considerable attention in machine translation (e.g., Brown et al, 1990), bilingual lexicography (e.g., Klavans and Tzoukermann, 1990), and terminology resea...
TERMIGHT: identifying and translating technical terminology”.
- 4th Conference on Applied Natural Language Processing,
, 1994
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Bitext Maps and Alignment via Pattern Recognition
- Computational Linguistics
, 1999
"... This article advances the state of the art ofbitext mapping by formulating the problem in terms of pattern recognition. From this point of view, the success of a bitext mapping algorithm hinges on how well it performs three tasks: signal generation, noise filtering, and search. The Smooth Injective ..."
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Cited by 105 (0 self)
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This article advances the state of the art ofbitext mapping by formulating the problem in terms of pattern recognition. From this point of view, the success of a bitext mapping algorithm hinges on how well it performs three tasks: signal generation, noise filtering, and search. The Smooth Injective Map Recognizer (SIMR) algorithm presented here integrates innovative approaches to each of these tasks. Objective evaluation has shown that SIMR's accuracy is consistently high for language pairs as diverse as French/English and Korean/English. If necessary, S IMR's bitext maps can be efficiently converted into segment alignments using the Geometric Segment Alignment (GSA) algorithm, which is also presented here. SIMR has produced bitext maps for over 200 megabytes of French-English bitexts. GSA has converted these maps into alignments. Both the maps and the alignments are available from the Linguistic Data Consortium) 1.
Automatic Evaluation and Uniform Filter Cascades for Inducing N-Best Translation Lexicons
- In Proceedings of the Third Workshop on Very Large Corpora
, 1995
"... This paper shows how to induce an N-best translation lexicon from a bilingual text corpus using statistical properties of the corpus together with four external knowledge sources. The knowledge sources are cast as filters, so that any subset of them can be cascaded in a uniform framework. A new o ..."
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Cited by 85 (20 self)
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This paper shows how to induce an N-best translation lexicon from a bilingual text corpus using statistical properties of the corpus together with four external knowledge sources. The knowledge sources are cast as filters, so that any subset of them can be cascaded in a uniform framework. A new objective evaluation measure is used to compare the quality of lexicons induced with different filter cascades. The best filter cascades improve lexicon quality by up to 137% over the plain vanilla statistical method, and approach human performance. Drastically reducing the size of the training corpus has a much smaller impact on lexicon quality when these knowledge sources are used. This makes it practical to train on small hand-built corpora for language pairs where large bilingual corpora are unavailable. Moreover, three of the four filters prove useful even when used with large training corpora.
Finding terminology translations from non-parallel corpora
- In Proceedings of the 5th Annual Workshop on Very Large Corpora
, 1997
"... We present a statistical word feature, the Word Relation Matrix, which can be used to find translated pairs of words and terms from non-parallel corpora, across language groups. Online dictionary entries are used as seed words to generate Word Relation Matrices for the unknown words according to cor ..."
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Cited by 82 (5 self)
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We present a statistical word feature, the Word Relation Matrix, which can be used to find translated pairs of words and terms from non-parallel corpora, across language groups. Online dictionary entries are used as seed words to generate Word Relation Matrices for the unknown words according to correlation measures. Word Relation Matrices are then mapped across the corpora to find translation pairs. Translation accuracies are around 30% when only the top candidate is counted. Nevertheless, top 20 candidate output give a 50.9% average increase in accuracy on human translator performance.
Robust Bilingual Word Alignment for Machine Aided Translation
- In Proceedings of the Workshop on Very Large Corpora
, 1993
"... We have developed a new program called word_align for aligning parallel text, text such as the Canadian Hansards that are available in two or more languages. The program takes the output of char_align (Church, 1993), a robust alternative to sentence-based alignment pro- grams, and applies word-level ..."
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Cited by 77 (2 self)
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We have developed a new program called word_align for aligning parallel text, text such as the Canadian Hansards that are available in two or more languages. The program takes the output of char_align (Church, 1993), a robust alternative to sentence-based alignment pro- grams, and applies word-level constraints us- ing a version of Brown et al.'s Model 2 (Brown et al., 1993), modified and extended to deal with robustness issues. Word_align was tested on a subset of Canadian Itansards supplied by Simard (Simard et al., 1992). The combination of word_align plus char_align reduces the variance (average square error) by a factor of 5 over char_align alone. More importantly, because word_align and char_align were designed to work robustly on texts that are smaller and more noisy than the 1tansards, it has been pos- sible to successfully deploy the programs at AT&T Language Line Services, a commercial translation service, to help them with difficult terminology.
Multipath Translation Lexicon Induction via Bridge Languages
- In Proceedings of NAACL 2001
, 2001
"... This paper presents a method for inducing translation lexicons based on transduction models of cognate pairs via bridge languages. Bilingual lexicons within languages families are induced using probabilistic string edit distance models. Translation lexicons for arbitrary distant language pairs are t ..."
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Cited by 73 (1 self)
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This paper presents a method for inducing translation lexicons based on transduction models of cognate pairs via bridge languages. Bilingual lexicons within languages families are induced using probabilistic string edit distance models. Translation lexicons for arbitrary distant language pairs are then generated by a combination of these intra-family translation models and one or more cross-family online dictionaries. Up to 95% exact match accuracy is achieved on the target vocabulary (30-68% of inter-family test pairs). Thus substantial portions of translation lexicons can be generated accurately for languages where no bilingual dictionary or parallel corpora may exist.
A Geometric Approach to Mapping Bitext Correspondence
, 1996
"... NLP work is to construct a detailed map of the correspondence between a text and its translation. Several auto- matic methods for this task have been proposed in recent years. Yet even the best of these methods can err by several typeset pages. The Smooth Injective Map Recognizer (SIMR) is a new bit ..."
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Cited by 63 (13 self)
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NLP work is to construct a detailed map of the correspondence between a text and its translation. Several auto- matic methods for this task have been proposed in recent years. Yet even the best of these methods can err by several typeset pages. The Smooth Injective Map Recognizer (SIMR) is a new bitext mapping algorithm. SIMR's errors are smaller than those of the previous front-runner by more than a factor of 4. Its robustness has en- abled new commercial-quality applications. The greedy nature of the algorithm makes it independent of memory resources. Unlike other bitext mapping algorithms, SIMR allows crossing correspondences to account for word order differences. Its output can be converted quickly and easily into a sen- tence alignment. SIMR's output has been used to align more than 200 megabytes of the Canadian Hansards for publication by the Linguistic Data Consortium.
