Abstract not found

We introduce a polynomial-time algorithm for statistical machine translation. This algorithm can be used in place of the expensive, slow best-first search strategies in current statistical translation architectures.

We present a novel method for discovering parallel sentences in comparable, non-parallel corpora. We train a maximum entropy classifier that, given a pair of sentences, can reliably determine whether or not they are translations of each other. Using this approach, we extract parallel data from large Chinese, Arabic, and English non-parallel newspaper corpora. We evaluate the quality of the extracted data by showing that it improves the performance of a state-of-the-art statistical machine translation system. We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a large non-parallel corpus. Thus, our method can be applied with great benefit to language pairs for which only scarce resources are available. 1.

We present a pattern matching method for compiling a bilingual lexicon of nouns and proper nouns from unaligned, noisy parallel texts of Asian/IndcEuropean language pairs. Tagging information of one guage is used. Word frequency and position information for high and low frequency words are represented in two different vector forms for pattern matching. New anchor point finding and noise elimination techniques are introduced. We obtained a 73.1% precision. We also show how the results can be used in the compilation of domain-specific noun phrases.

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.

Abstract. We present a new method for aligning sentences with their translations in a parallel bilingual corpus. Previous approaches have generally been based either on sentence length or word correspondences. Sentence-length-based methods are relatively fast and fairly accurate. Word-correspondence-based methods are generally more accurate but much slower, and usually depend on cognates or a bilingual lexicon. Our method adapts and combines these approaches, achieving high accuracy at a modest computational cost, and requiring no knowledge of the languages or the corpus beyond division into words and sentences. 1

We propose a new algorithm, DK-vec, for aligning pairs of Asian/Indo-European noisy parallel texts without sentence boundaries. The algorithm uses frequency, position and recency information as features for pattern matching. Dynamic Time Warping is used as the matching technique between word pairs. This algorithm produces a small bilingual lexicon which provides anchor points for alignment.

this paper, we present an initial algorithm for translating technical terms using a pair of non-parallel corpora. Evalution results show translation precisions at around 30% when only the top candidate is considered. While this precision is lower than that achieved with parallel corpora, we show that top 20 candidate output from our algorithm allows translators to increase their accuracy by 50.9%. In the following sections, we first describe a pair of non-parallel corpora we use for experiments, and then we introduce the Word Relation Matrix (WoRM), a statistical word feature representation for technical term translation from non-parallel corpora. We evaluate the effectiveness of this feature with two sets of experiments, using English/English, and English/Japanese non-parallel corpora. 2. BACKGROUND

The first step in Chinese NLP is to tokenize or segment character sequences into words, since the text contains no word delimiters. Recent heavy activity in this area has shown the biggest stumbling block to be words that are absent from the lexicon, since successful tokenizers to date have been based on dictionary lookup (e.g., Chang & Chen 1993;

We describe a method for identifying subsentential phrasal translation examples in sentencealigned parallel corpora, using only a probabilistic translation lexicon for the language pair. Our method differs from previous approaches in that (1) it is founded on a formal basis, making use of an inversion transduction grammar (ITG) formalism that we recently developed for bilingual language modeling, and (2) it requires no language-specific monolingual grammars for the source and target languages. Instead, we devise a generic, language-independent constituent-matching ITG with inherent expressiveness properties that correspond to a desirable level of matching flexibility. Bilingual parsing, in conjunction with a stochastic version of the ITG formalism, performs the phrasal translation extraction. The Hong Kong University of Science & Technology Technical Report Series Department of Computer Science TMI-95 WU 2 1 Introduction Phrasal translation examples at the subsentential level are an...