Cross-lingual embeddings aim to represent words in multiple languages in a shared vector space by capturing semantic similarities across languages. They are a crucial component for scaling tasks to multiple languages by transferring knowledge from languages with rich resources to low-resource languages. A common approach to learning cross-lingual embeddings is to train monolingual embeddings separately for each language and learn a linear projection from the monolingual spaces into a shared space, where the mapping relies on a small seed dictionary. While there are high-quality generic seed dictionaries and pre-trained cross-lingual embeddings available for many language pairs, there is little research on how they perform on specialised tasks. In this paper, we investigate the best practices for constructing the seed dictionary for a specific domain. We evaluate the embeddings on the sequence labelling task of Curriculum Vitae parsing and show that the size of a bilingual dictionary, the frequency of the dictionary words in the domain corpora and the source of data (task-specific vs generic) influence the performance. We also show that the less training data is available in the low-resource language, the more the construction of the bilingual dictionary matters, and demonstrate that some of the choices are crucial in the zero-shot transfer learning case.
A common way to select a bilingual dictionary is by using either automatic translations of frequent words or word alignments. For instance, @cite_1 select the target word to which the source word is most frequently aligned in parallel corpora. @cite_17 use the 5,000 most frequent words from the source language with their translations. To investigate the impact of the dictionary on the embedding quality, @cite_4 evaluate different factors and conclude that carefully selecting highly reliable symmetric translation pairs improves the performance of benchmark word-translation tasks. The authors also demonstrate that increasing the lexicon size over 10,000 pairs show a slow and steady decrease in performance.
@cite_1 @cite_4 @cite_17
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Researchers in both machine translation (e.g., 1990) and bilingual lexicography (e.g., Klavans and Tzoukermann 1990) have recently become interested in studying bilingual corpora, bodies of text such as the Canadian Hansards (parliamentary proceedings), which are available in multiple languages (such as French and English). One useful step is to align the sentences, that is, to identify correspondences between sentences in one language and sentences in the other language.This paper will describe a method and a program (align) for aligning sentences based on a simple statistical model of character lengths. The program uses the fact that longer sentences in one language tend to be translated into longer sentences in the other language, and that shorter sentences tend to be translated into shorter sentences. A probabilistic score is assigned to each proposed correspondence of sentences, based on the scaled difference of lengths of the two sentences (in characters) and the variance of this difference. This probabilistic score is used in a dynamic programming framework to find the maximum likelihood alignment of sentences.It is remarkable that such a simple approach works as well as it does. An evaluation was performed based on a trilingual corpus of economic reports issued by the Union Bank of Switzerland (UBS) in English, French, and German. The method correctly aligned all but 4 of the sentences. Moreover, it is possible to extract a large subcorpus that has a much smaller error rate. By selecting the best-scoring 80 of the alignments, the error rate is reduced from 4 to 0.7 . There were more errors on the English-French subcorpus than on the English-German subcorpus, showing that error rates will depend on the corpus considered; however, both were small enough to hope that the method will be useful for many language pairs.To further research on bilingual corpora, a much larger sample of Canadian Hansards (approximately 90 million words, half in English and and half in French) has been aligned with the align program and will be available through the Data Collection Initiative of the Association for Computational Linguistics (ACL DCI). In addition, in order to facilitate replication of the align program, an appendix is provided with detailed c-code of the more difficult core of the align program. Bilingual lexicons are fundamental resources. Modern automated lexicon generation methods usually require parallel corpora, which are not available for most language pairs. Lexicons can be generated using non-parallel corpora or a pivot language, but such lexicons are noisy. We present an algorithm for generating a high quality lexicon from a noisy one, which only requires an independent corpus for each language. Our algorithm introduces non-aligned signatures (NAS), a cross-lingual word context similarity score that avoids the over-constrained and inefficient nature of alignment-based methods. We use NAS to eliminate incorrect translations from the generated lexicon. We evaluate our method by improving the quality of noisy Spanish-Hebrew lexicons generated from two pivot English lexicons. Our algorithm substantially outperforms other lexicon generation methods. We propose a novel context heterogeneity similarity measure between words and their translations in helping to compile bilingual lexicon entries from a non-parallel English-Chinese corpus. Current algorithms for bilingual lexicon compilation rely on occurrence frequencies, length or positional statistics derived from parallel texts. There is little correlation between such statistics of a word and its translation in non-parallel corpora. On the other hand, we suggest that words with productive context in one language translate to words with productive context in another language, and words with rigid context translate into words With rigid context. Context heterogeneity measures how productive the context of a word is in a given domain, independent of its absolute occurrence frequency in the text. Based on this information, we derive statistics of bilingual word pairs from a non-parallel corpus. These statistics can be used to bootstrap a bilingual dictionary compilation algorithm. The information available in languages other than English on the World Wide Web is increasing significantly. To cross language boundaries between different languages, dictionaries are the most typical tools. However, the general-purpose dictionary is less sensitive in genre and domain and it is impractical to manually construct tailored bilingual dictionaries or sophisticated multilingual thesauri for large applications. Corpus-based approaches, which do not have the limitation of dictionaries, provide a statistical translation model to cross the language boundary. The objective of this research work is to mine English Chinese parallel documents automatically from the World Wide Web. In this paper, we present an alignment method based on dynamic programming to identify the one-to-one Chinese and English title pairs. The longest common subsequence (LCS) is applied to find the most reliable Chinese translation of an English word. A score function is then proposed to determine the optimal title pairs. Experiments have been conducted to investigate the performance of the proposed method. The precision of the result is 0.995 while the recall is 0.8096.