This article describes a linguistically informed method for integrating phrasal verbs into statistical machine translation ( SMT ) systems . In a case study involving English to Bulgarian SMT , we show that our method does not only improve translation quality but also outperforms similar methods previously applied to the same task . We attribute this to the fact that , in contrast to previous work on the subject , we employ detailed linguistic information . We found out that features which describe phrasal verbs as idiomatic or compositional contribute most to the better translation quality achieved by our method . Phrasal verbs are a type of multiword expressions ( MWEs ) and as such , their meaning is not derivable , or is only partially derivable , from the semantics of their lexemes . This , together with the high frequency of MWEs in every day communication ( see Jackendoff ( 1997 ) ) , calls for a special treatment of such expressions in natural language processing ( NLP ) applications . Here , we concentrate on statistical machine translation ( SMT ) where the word-to-word translation of MWEs often results in wrong translations ( Piao et al . , 2005 ) . Previous work has shown that the application of dedicated methods to identify MWEs and then integrate them in some way into the SMT process often improves translation quality . Generally , automatically extracted lexicons of MWEs are employed in the identification step . Further , various integration strategies have been proposed . The so called static strategy suggests training the SMT system on corpora in which each MWE is treated as a single unit , e.g. call off . This improves SMT indirectly by improving the alignment between source and target sentences in the training data . Various versions of this strategy are applied in Lambert and Banchs ( 2005 ) , Carpuat and Diab ( 2010 ) , and Simova and Kordoni ( 2013 ) . In all cases there is some improvement in translation quality , caused mainly by the better treatment of separable PVs , such as in turn the light on . Another strategy , which is referred to as dynamic , is to modify directly the SMT system . Ren et al . ( 2009 ) , for example , treat bilingual MWEs pairs as parallel sentences which are then added to training data and subsequently aligned with GIZA++ ( Och and Ney , 2003 ) . Other approaches perform feature mining and modify directly the automatically extracted translation table . Ren et al . ( 2009 ) and Simova and Kordoni ( 2013 ) employ Moses1 to build and train phrase-based SMT systems and then , in addition to the standard phrasal translational probabilities , they add a binary feature which indicates whether an MWE is present in a given source phrase or not . Carpuat and Diab ( 2010 ) employ the same approach but the additional feature indicates the number of MWEs in each phrase . All studies report improvements over a baseline system with no MWE knowledge but these improvements are comparable to those achieved by static methods . In this article , we further improve the dynamic strategy by adding features which , unlike all previous work , also encode some of the linguistic properties of MWEs . Since it is their peculiar linguistic nature that makes those expressions problematic for SMT , it is our thesis that providing more linguistic information to the translation process will improve it . In particular , we concentrate on a specific type of MWEs , namely phrasal verbs ( PVs ) . We add 4 binary features to the translation table which indicate not only the presence of a PV but also its transitivity , separability , and idiomaticity . We found that PVs are very suitable for this study since we can easily extract the necessary informa-tion from various language resources . To prove our claim , we perform a case study with an English to Bulgarian SMT system . Bulgarian lacks PVs in the same form they appear in English . It is often the case that an English PV is translated to a single Bulgarian verb . Such manyto-one mappings cause the so called translation asymmetries which make the translation of PVs very problematic . We perform automated and manual evaluations with a number of feature combinations which show that the addition of all 4 features proposed above improves translation quality significantly . Moreover , our method outperforms static and dynamic methods previously applied to the same test data . A notable increase in performance is observed for separable PVs where the verb and the particle(s ) were not adjacent in the input English sentence as well as for idiomatic PVs . This clearly demonstrates the importance of linguistic information for the proper treatment of PVs in SMT . We would like to point out that we view the work presented here as a preliminary study towards a more general linguistically informed method for handling similar types of translation asymmetries . The experiments with a single phenomenon , namely PVs , serve as a case study the purpose of which is to demonstrate the validity of our approach and the crucial role of properly integrated linguistic information into SMT . Our work , however , can be immediately extended to other phenomena , such as collocations and noun compounds . The remainder of the paper is organised as follows . Section 2 describes the asymmetries caused by PVs in English to Bulgarian translation . Section 3 provides details about the resources involved in the experiments . Section 4 describes our method and the experimental setup . Section 5 presents the results and discusses the improvements in translation quality achieved by the method . Sections 6 concludes the paper . In this article , we showed that the addition of linguistically informative features to a phrase-based SMT model improves the translation quality of a particular type of MWEs , namely phrasal verbs . In a case study involving SMT from English to Bulgarian , we showed that adding features which encode not only the presence of a PV in a given phrase but also its transitiveness , separability , and idiomaticity led to better translation quality compared to previous work which employs both static and dynamic strategies . In future research , we will extend our method to other language pairs which exhibit the same type of translation asymmetries when it comes to PVs . Such language pairs include , among others , English-Spanish and English-Portuguese . Further , we will apply our linguistically informed method to other phenomena which cause similar issues for SMT . Immediate candidate phenomena include other types of MWEs , collocations , and noun compounds . When it comes to MWEs , we will pay special attention to the compositionality aspect since it seems to have contributed most to the good performance achieve by our method in the study presented here .

Challenge: Although multiword expressions frequently appear in daily communications, statistical machine translation models often produce wrong translations calling for special treatments of such expressions.
Approach: They propose a linguistically informed method for integrating phrasal verbs by adding features such as the presence of phrasal nouns, transitivity, separability and idiomaticity.
Outcome: The proposed method outperforms similar models on an English to Bulgarian translation task evaluated by automatic and manual metrics showing the utility of introduced features.