Marlies van der Wees


2018

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Evaluation of Machine Translation Performance Across Multiple Genres and Languages
Marlies van der Wees | Arianna Bisazza | Christof Monz
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Dynamic Data Selection for Neural Machine Translation
Marlies van der Wees | Arianna Bisazza | Christof Monz
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Intelligent selection of training data has proven a successful technique to simultaneously increase training efficiency and translation performance for phrase-based machine translation (PBMT). With the recent increase in popularity of neural machine translation (NMT), we explore in this paper to what extent and how NMT can also benefit from data selection. While state-of-the-art data selection (Axelrod et al., 2011) consistently performs well for PBMT, we show that gains are substantially lower for NMT. Next, we introduce ‘dynamic data selection’ for NMT, a method in which we vary the selected subset of training data between different training epochs. Our experiments show that the best results are achieved when applying a technique we call ‘gradual fine-tuning’, with improvements up to +2.6 BLEU over the original data selection approach and up to +3.1 BLEU over a general baseline.

2016

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A Simple but Effective Approach to Improve Arabizi-to-English Statistical Machine Translation
Marlies van der Wees | Arianna Bisazza | Christof Monz
Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)

A major challenge for statistical machine translation (SMT) of Arabic-to-English user-generated text is the prevalence of text written in Arabizi, or Romanized Arabic. When facing such texts, a translation system trained on conventional Arabic-English data will suffer from extremely low model coverage. In addition, Arabizi is not regulated by any official standardization and therefore highly ambiguous, which prevents rule-based approaches from achieving good translation results. In this paper, we improve Arabizi-to-English machine translation by presenting a simple but effective Arabizi-to-Arabic transliteration pipeline that does not require knowledge by experts or native Arabic speakers. We incorporate this pipeline into a phrase-based SMT system, and show that translation quality after automatically transliterating Arabizi to Arabic yields results that are comparable to those achieved after human transliteration.

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Measuring the Effect of Conversational Aspects on Machine Translation Quality
Marlies van der Wees | Arianna Bisazza | Christof Monz
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Research in statistical machine translation (SMT) is largely driven by formal translation tasks, while translating informal text is much more challenging. In this paper we focus on SMT for the informal genre of dialogues, which has rarely been addressed to date. Concretely, we investigate the effect of dialogue acts, speakers, gender, and text register on SMT quality when translating fictional dialogues. We first create and release a corpus of multilingual movie dialogues annotated with these four dialogue-specific aspects. When measuring translation performance for each of these variables, we find that BLEU fluctuations between their categories are often significantly larger than randomly expected. Following this finding, we hypothesize and show that SMT of fictional dialogues benefits from adaptation towards dialogue acts and registers. Finally, we find that male speakers are harder to translate and use more vulgar language than female speakers, and that vulgarity is often not preserved during translation.

2015

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Translation Model Adaptation Using Genre-Revealing Text Features
Marlies van der Wees | Arianna Bisazza | Christof Monz
Proceedings of the Second Workshop on Discourse in Machine Translation

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Five Shades of Noise: Analyzing Machine Translation Errors in User-Generated Text
Marlies van der Wees | Arianna Bisazza | Christof Monz
Proceedings of the Workshop on Noisy User-generated Text

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What’s in a Domain? Analyzing Genre and Topic Differences in Statistical Machine Translation
Marlies van der Wees | Arianna Bisazza | Wouter Weerkamp | Christof Monz
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)