Marion Weller

How to Produce Unseen Teddy Bears: Improved Morphological Processing of Compounds in SMT

Compounding in morphologically rich languages is a highly productive process which often causes SMT approaches to fail because of unseen words. We present an approach for translation into a compounding language that splits compounds into simple words for training and, due to an underspecified representation, allows for free merging of simple words into compounds after translation. In contrast to previous approaches, we use features projected from the source language to predict compound mergings. We integrate our approach into end-to-end SMT and show that many compounds matching the reference translation are produced which did not appear in the training data. Additional manual evaluations support the usefulness of generalizing compound formation in SMT.

Exploring the Planet of the APEs: a Comparative Study of State-of-the-art Methods for MT Automatic Post-Editing

Downstream processing of machine translation (MT) output promises to be a solution to improve translation quality, especially when the MT system’s internal decoding process is not accessible. Both rule-based and statistical automatic postediting (APE) methods have been proposed over the years, but with contrasting results. A missing aspect in previous evaluations is the assessment of different methods: i) under comparable conditions, and ii) on different language pairs featuring variable levels of MT quality. Focusing on statistical APE methods (more portable across languages), we propose the first systematic analysis of two approaches. To understand their potential, we compare them in the same conditions over six language pairs having English as source. Our results evidence consistent improvements on all language pairs, a relation between the extent of the gain and MT output quality, slight but statistically significant performance differences between the two methods, and their possible complementarity.

Distinguishing Degrees of Compositionality in Compound Splitting for Statistical Machine Translation

The paper presents an approach to morphological compound splitting that takes the degree of compositionality into account. We apply our approach to German noun compounds and particle verbs within a German‐English SMT system, and study the effect of only splitting compositional compounds as opposed to an aggressive splitting. A qualitative study explores the translational behaviour of non-compositional compounds.

How to Account for Idiomatic German Support Verb Constructions in Statistical Machine Translation

Support-verb constructions (i.e., multiword expressions combining a semantically light verb with a predicative noun) are problematic for standard statistical machine translation systems, because SMT systems cannot distinguish between literal and idiomatic uses of the verb. We work on the German to English translation direction, for which the identification of support-verb constructions is challenging due to the relatively free word order of German. We show that we achieve improved translation quality for verb-object supportverb constructions by marking the verbs when occuring in such constructions. Additional evaluations revealed that our systems produce more correct verb translations than a contrastive baseline system without verb markup.

Predicting Prepositions for SMT

Representation and Prediction Features Initial experiments showed that replacing prepositions by simple place-holders decreases the translation quality. As an extension to the basic approach with plain place-holders, we thus experiment with enriching the place-holders such that they contain more relevant information and represent the content of a preposition while still being in an abstract form. For example, the representation can be enriched by annotating the place-holder with the grammatical case of the preposition it represents: for overt prepositions, case is often an indicator of the content (e.g. direction/location), whereas for NPs, case indicates