Determining the Placement of German Verbs in English–To–German SMT
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Determining the placement of German verbs in English–to–German SMT Anita Gojun Alexander Fraser Institute for Natural Language Processing University of Stuttgart, Germany {gojunaa, fraser}@ims.uni-stuttgart.de Abstract German language model, making the translations difficult to understand. When translating English to German, exist- A common approach for handling the long- ing reordering models often cannot model range reordering problem within PSMT is per- the long-range reorderings needed to gen- forming syntax-based or part-of-speech-based erate German translations with verbs in the (POS-based) reordering of the input as a prepro- correct position. We reorder English as a cessing step before translation (e.g., Collins et al. preprocessing step for English-to-German SMT. We use a sequence of hand-crafted (2005), Gupta et al. (2007), Habash (2007), Xu reordering rules applied to English parse et al. (2009), Niehues and Kolss (2009), Katz- trees. The reordering rules place English Brown et al. (2011), Genzel (2010)). verbal elements in the positions within the We reorder English to improve the translation clause they will have in the German transla- to German. The verb reordering process is im- tion. This is a difficult problem, as German plemented using deterministic reordering rules on verbal elements can appear in different po- English parse trees. The sequence of reorderings sitions within a clause (in contrast with En- glish verbal elements, whose positions do is derived from the clause type and the composi- not vary as much). We obtain a significant tion of a given verbal complex (a (possibly dis- improvement in translation performance. contiguous) sequence of verbal elements in a sin- gle clause). Only one rule can be applied in a given context and for each word to be reordered, 1 Introduction there is a unique reordered position. We train a standard PSMT system on the reordered English Phrase-based SMT (PSMT) systems translate training and tuning data and use it to translate the word sequences (phrases) from a source language reordered English test set into German. into a target language, performing reordering of This paper is structured as follows: in section target phrases in order to generate a fluent target 2, we outline related work. In section 3, English language output. The reordering models, such as, and German verb positioning is described. The for example, the models implemented in Moses reordering rules are given in section 4. In sec- (Koehn et al., 2007), are often limited to a cer- tion 5, we show the relevance of the reordering, tain reordering range since reordering beyond this present the experiments and present an extensive distance cannot be performed accurately. This re- error analysis. We discuss some problems ob- sults in problems of fluency for language pairs served in section 7 and conclude in section 8. with large differences in constituent order, such as English and German. When translating from 2 Related work English to German, verbs in the German output are often incorrectly left near their position in En- There have been a number of attempts to handle glish, creating problems of fluency. Verbs are also the long-range reordering problem within PSMT. often omitted since the distortion model cannot Many of them are based on the reordering of a move verbs to positions which are licensed by the source language sentence as a preprocessing step 726 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 726–735, Avignon, France, April 23 - 27 2012. c 2012 Association for Computational Linguistics before translation. Our approach is related to the dev set which is then translated and evaluated. work of Collins et al. (2005). They reordered Only those rule sequences are extracted which German sentences as a preprocessing step for maximize the translation performance of the German-to-English SMT. Hand-crafted reorder- reordered dev set. ing rules are applied on German parse trees in For the extraction of reordering rules, Gen- order to move the German verbs into the posi- zel (2010) uses shallow constituent parse trees tions corresponding to the positions of the English which are obtained from dependency parse trees. verbs. Subsequently, the reordered German sen- The trees are annotated using both Penn Tree- tences are translated into English leading to better bank POS tags and using Stanford dependency translation performance when compared with the types. However, the constraints on possible re- translation of the original German sentences. orderings are too restrictive in order to model all We apply this method on the opposite trans- word movements required for English-to-German lation direction, thus having English as a source translation. In particular, the reordering rules in- language and German as a target language. How- volve only the permutation of direct child nodes ever, we cannot simply invert the reordering rules and do not allow changing of child-parent rela- which are applied on German as a source lan- tionships (deleting of a child or attaching a node guage in order to reorder the English input. While to a new father node). In our implementation, a the reordering of German implies movement of verb can be moved to any position in a parse tree the German verbs into a single position, when re- (according to the reordering rules): the reordering ordering English, we need to split the English ver- can be a simple permutation of child nodes, or at- bal complexes and, where required, move their tachment of these nodes to a new father node (cf. parts into different positions. Therefore, we need movement of bought and read in figure 11). to identify exactly which parts of a verbal com- Thus, in contrast to Genzel (2010), our ap- plex must be moved and their possible positions proach does not have any constraints with respect in a German sentence. to the position of nodes marking a verb within the Reordering rules can also be extracted automat- tree. Only the syntactic structure of the sentence ically. For example, Niehues and Kolss (2009) restricts the distance of the linguistically moti- automatically extracted discontiguous reordering vated verb movements. rules (allowing gaps between POS tags which can include an arbitrary number of words) from 3 Verb positions in English and German a word-aligned parallel corpus with POS tagged 3.1 Syntax of German sentences source side. Since many different rules can be ap- plied on a given sentence, a number of reordered Since in this work, we concentrate on verbs, we sentence alternatives are created which are en- use the notion verbal complex for a sequence con- coded as a word lattice (Dyer et al., 2008). They sisting of verbs, verbal particles and negation. dealt with the translation directions German-to- The verb positions in the German sentences de- English and English-to-German, but translation pend on clause type and the tense as shown in ta- improvement was obtained only for the German- ble 1. Verbs can be placed in 1st, 2nd or clause- to-English direction. This may be due to miss- final position. Additionally, if a composed tense ing information about clause boundaries since En- is given, the parts of a verbal complex can be glish verbs often have to be moved to the clause interrupted by the middle field (MF) which con- end. Our reordering has access to this kind of tains arbitrary sentence constituents, e.g., sub- knowledge since we are working with a full syn- jects and objects (noun phrases), adjuncts (prepo- tactic parser of English. sitional phrases), adverbs, etc. We assume that the Genzel (2010) proposed a language- German sentences are SVO (analogously to En- independent method for learning reordering glish); topicalization is beyond the scope of our rules where the rules are extracted from parsed work. source language sentences. For each node, all In this work, we consider two possible posi- possible reorderings (permutations) of a limited tions of the negation in German: (1) directly in number of the child nodes are considered. The 1The verb movements shown in figure 1 will be explained candidate reordering rules are applied on the in detail in section 4. 727 1st 2nd MF clause- position in declarative, or in the 1st position in in- final terrogative clauses, in German, the entire verbal subject finV any ∅ decl complex can additionally be placed at the clause subject finV any mainV end in subordinate or infinitival clauses (cf. row finV subject any ∅ int/perif sub/inf in table 1). finV subject any mainV Because of these differences, for nearly all relCon subject any finV sub/inf types of English clauses, reordering is needed in relCon subject any VC order to place the English verbs in the positions Table 1: Position of the German subjects and verbs which correspond to the correct verb positions in in declarative clauses (decl), interrogative clauses and German. Only English declarative clauses with clauses with a peripheral clause (int/perif ), subordi- simple present and simple past tense have the nate/infinitival (sub/inf ) clauses. mainV = main verb, same verb position as their German counterparts. finV = finite verb, VC = verbal complex, any = arbi- We give statistics on clause types and their rele- relCon trary words, = relative pronoun or conjunction. vance for the verb reordering in section 5.1. We consider extraponed consituents in perif, as well as optional interrogatives in int to be in position 0. 4 Reordering of the English input front of the main verb, and (2) directly after the The reordering is carried out on English parse finite verb. The two negation positions are illus- trees. We first enrich the parse trees with clause trated in the following examples: type labels, as described below.