Enforcing Consistent Translation of German Compound Coreferences

Mascarell, Laura; Fishel, Mark; Korchagina, Natalia

doi:10.5167/uzh-98540

Cited by 6 publications

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Disambiguating Discourse Connectives for Statistical Machine Translation

Meyer

Hajlaoui²,

Popescu-Belis

2015

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

Abstract-This paper shows that the automatic labeling of discourse connectives with the relations they signal, prior to machine translation (MT), can be used by phrase-based statistical MT systems to improve their translations. This improvement is demonstrated here when translating from English to four target languages -French, German, Italian and Arabic -using several test sets from recent MT evaluation campaigns. Using automatically labeled data for training, tuning and testing MT systems is beneficial on condition that labels are sufficiently accurate, typically above 70%. To reach such an accuracy, a large array of features for discourse connective labeling (morphosyntactic, semantic and discursive) are extracted using stateof-the-art tools and exploited in factored MT models. The translation of connectives is improved significantly, between 0.7% and 10% as measured with the dedicated ACT metric. The improvements depend mainly on the level of ambiguity of the connectives in the test sets.

show abstract

Disambiguating Discourse Connectives for Statistical Machine Translation

Meyer

Hajlaoui²,

Popescu-Belis

2015

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

show abstract

Consistent Translation of Repeated Nouns using Syntactic and Semantic Cues

Mascarell

Popescu-Belis

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

View full text Add to dashboard Cite

We propose a method to decide whether two occurrences of the same noun in a source text should be translated consistently, i.e. using the same noun in the target text as well. We train and test classifiers that predict consistent translations based on lexical, syntactic, and semantic features. We first evaluate the accuracy of our classifiers intrinsically, in terms of the accuracy of consistency predictions, over a subset of the UN Corpus. Then, we also evaluate them in combination with phrase-based statistical MT systems for Chinese-to-English and Germanto-English. We compare the automatic post-editing of noun translations with the re-ranking of the translation hypotheses based on the classifiers' output, and also use these methods in combination. This improves over the baseline and closes up to 50% of the gap in BLEU scores between the baseline and an oracle classifier.

show abstract

Detecting Document-level Context Triggers to Resolve Translation Ambiguity

Mascarell

Fishel

2015

Proceedings of the Second Workshop on Discourse in Machine Translation

View full text Add to dashboard Cite

Most current machine translation systems translate each sentence independently, ignoring the context from previous sentences. This discourse unawareness can lead to incorrect translation of words or phrases that are ambiguous in the sentence. For example, the German term Typen in the phrase diese Typen can be translated either into English types or guys. However, knowing that it co-refers to the compound Körpertypen ("body types") in the previous sentence helps to disambiguate the term and translate it into types. We propose a method of automatically detecting document-level trigger words (like Körpertypen), whose presence helps to disambiguate translations of ambiguous terms. In this preliminary study we analyze the method and its limitations, and outline future work directions.

show abstract

Enforcing Consistent Translation of German Compound Coreferences

Cited by 6 publications

References 8 publications

Disambiguating Discourse Connectives for Statistical Machine Translation

Disambiguating Discourse Connectives for Statistical Machine Translation

Consistent Translation of Repeated Nouns using Syntactic and Semantic Cues

Detecting Document-level Context Triggers to Resolve Translation Ambiguity

Contact Info

Product

Resources

About