Discourse-Related Language Contrasts in English-Croatian Human and Machine Translation

Abstract

We present an analysis of a number of coreference phenomena in English-Croatian human and machine translations. The aim is to shed light on the differences in the way these structurally different languages make use of discourse information and provide insights for discourse-aware machine translation system development. The phenomena are automatically identified in parallel data using annotation produced by parsers and word alignment tools, enabling us to pinpoint patterns of interest in both languages. We make the analysis more fine-grained by including three corpora pertaining to three different registers. In a second step, we create a test set with the challenging linguistic constructions and use it to evaluate the performance of three MT systems. We show that both SMT and NMT systems struggle with handling these discourse phenomena, even though NMT tends to perform somewhat better than SMT. By providing an overview of patterns frequently occurring in actual language use, as well as by pointing out the weaknesses of current MT systems that commonly mistranslate them, we hope to contribute to the effort of resolving the issue of discourse phenomena in MT applications.

Context in Neural Machine Translation: A Review of Models and Evaluations

“…A manual analysis of discourse phenomena in SMT, with quality estimation as the background objective, was presented by Scarton and Specia (2015), while other taxonomies of discourse-related errors, applied by manual analysts, have been inspired by contrastive linguistics at the discourse level, allowing comparison of cross-lingual contrasts in human and machine translation and concluding to NMT superiority (Lapshinova-Koltunski and Hardmeier, 2017;Šoštarić et al, 2018).…”

Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank

“…In sentence pairs drawn from comparable documents-written independently in each language but sharing a topic-sentences that contain translated fragments are rarely exactly equivalent (Fung and Cheung, 2004;Munteanu and Marcu, 2005), and sentence alignment errors yield coarse mismatches in meaning (Goutte et al, 2012). In translated sentence pairs, differences in discourse structure across languages (Li et al, 2014) can lead to sentence-level divergences or discrepancies in translation of pronouns (Lapshinova-Koltunski and Hardmeier, 2017;Šoštarić et al, 2018); translation lexical choice requires selecting between near synonyms that introduce languagespecific nuances (Hirst, 1995); typological divergences lead to structural mismatches (Dorr, 1994), and non-literal translation processes can lead to semantic drifts (Zhai et al, 2018).…”