In this article, we report a large-scale corpus study aimed at tackling the (controversial) question to what extent the European national varieties of Dutch, that is, Belgian and Netherlandic Dutch, exhibit morpho-syntactic differences. Instead of relying on a manual selection of cases of morphosyntactic variation, we first marshal large bilingual parallel corpora and machine translation software to identify semiautomatically, in an extensively data-driven fashion, loci of variation from various “corners” of Dutch grammar. We then gauge the distribution of con-structional alternatives in a nationally as well as stylistically stratified corpus for a representative selection of twenty alternation patterns. We find that natiolectal variation in the grammar of Dutch is far more prevalent than often assumed, especially in less edited text types, and that it shows up in inflection phenomena, lexically conditioned syntactic variation, and pure word order permutations. Another key finding is that many cases of synchronic probabilistic asymmetries reflect a diachronic difference between the two varieties: Netherlandic Dutch often tends to be ahead in cases of ongoing grammatical change, with Belgian Dutch holding on somewhat longer to obsolescent features of the grammar.*

The western European present perfect is subject to substantial crosslinguistic variation. The literature, however, focuses on individual languages or on comparisons of a restricted number of languages. We piece together the puzzle and do so in a data-driven way by comparing the use of the present perfect through a parallel corpus based on the French novel L’Étranger and its translations in Italian, German, Dutch, European Spanish, British English, and Modern Greek. We introduce and showcase Translation Mining, a software suite combining a parallel corpus database with annotation and analysis tools. Translation Mining allows us to generate descriptive statistics of tense use across languages but also to visualize variation through its multidimensional scaling component and to link the variation we find to the underlying data through its integrated setup. We confirm that the present perfect competes with the past and we reveal the fine-grained scalar nature of the variation. To complete the puzzle, we ascertain the dimensions of variation, ranging from lexical and compositional semantics to dynamic semantics and pragmatics.1

This article presents the first study on using a parallel corpus to teach Cantonese, the variety of Chinese spoken in Hong Kong. We evaluated this approach with Mandarin-speaking undergraduate students at the beginner level. Exploiting their knowledge of Mandarin, a closely related language, the students studied Cantonese with authentic material in a Cantonese-Mandarin parallel corpus, transcribed from television programs. They were given a list of Mandarin words that yield a range of possible Cantonese translations, depending on the linguistic context. Leveraging sentence and word alignments in the parallel corpus, the students independently searched for example sentences to discover these translation equivalents. Experimental results showed that, in both the short- and long-term, this data-driven learning approach helped students improve their knowledge of Cantonese vocabulary. These results suggest the potential of applying parallel corpora at even the beginners’ level for other L1-L2 pairs of closely related languages.