Foundational Hebrew NLP tasks such as segmentation, tagging and parsing, have relied to date on various versions of the Hebrew Treebank (HTB, Sima'an et al. 2001).However, the data in HTB, a single-source newswire corpus, is now over 30 years old, and does not cover many aspects of contemporary Hebrew on the web.This paper presents a new, freely available UD treebank of Hebrew stratified from a range of topics selected from Hebrew Wikipedia.In addition to introducing the corpus and evaluating the quality of its annotations, we deploy automatic validation tools based on grew (Guillaume, 2021), and conduct the first crossdomain parsing experiments in Hebrew.We obtain new state-of-the-art (SOTA) results on UD NLP tasks, using a combination of the latest language modelling and some incremental improvements to existing transformer based approaches.We also release a new version of the UD HTB matching annotation scheme updates from our new corpus.

Translation has played an important role in trade, law, commerce, politics, and literature for thousands of years.Translators have always tried to be invisible; ideal translations should look as if they were written originally in the target language.We show that traces of the source language remain in the translation product to the extent that it is possible to uncover the history of the source language by looking only at the translation.Specifically, we automatically reconstruct phylogenetic language trees from monolingual texts (translated from several source languages).The signal of the source language is so powerful that it is retained even after two phases of translation.This strongly indicates that source language interference is the most dominant characteristic of translated texts, overshadowing the more subtle signals of universal properties of translation.

We present a computational analysis of three language varieties: native, advanced non-native, and translation. Our goal is to investigate the similarities and differences between non-native language productions and translations, contrasting both with native language. Using a collection of computational methods we establish three main results: (1) the three types of texts are easily distinguishable; (2) nonnative language and translations are closer to each other than each of them is to native language; and (3) some of these characteristics depend on the source or native language, while others do not, reflecting, perhaps, unified principles that similarly affect translations and non-native language.

Translation models used for statistical machine translation are compiled from parallel corpora that are manually translated. The common assumption is that parallel texts are symmetrical: The direction of translation is deemed irrelevant and is consequently ignored. Much research in Translation Studies indicates that the direction of translation matters, however, as translated language (translationese) has many unique properties. It has already been shown that phrase tables constructed from parallel corpora translated in the same direction as the translation task outperform those constructed from corpora translated in the opposite direction. We reconfirm that this is indeed the case, but emphasize the importance of also using texts translated in the “wrong” direction. We take advantage of information pertaining to the direction of translation in constructing phrase tables by adapting the translation model to the special properties of translationese. We explore two adaptation techniques: First, we create a mixture model by interpolating phrase tables trained on texts translated in the “right” and the “wrong” directions. The weights for the interpolation are determined by minimizing perplexity. Second, we define entropy-based measures that estimate the correspondence of target-language phrases to translationese, thereby eliminating the need to annotate the parallel corpus with information pertaining to the direction of translation. We show that incorporating these measures as features in the phrase tables of statistical machine translation systems results in consistent, statistically significant improvement in the quality of the translation.

We investigate the differences between language models compiled from original target-language texts and those compiled from texts manually translated to the target language. Corroborating established observations of Translation Studies, we demonstrate that the latter are significantly better predictors of translated sentences than the former, and hence fit the reference set better. Furthermore, translated texts yield better language models for statistical machine translation than original texts.