A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation

A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation Maria N˘adejde1, Alexandra Birch1, Philipp Koehn2 1School of Informatics, University of Edinburgh 2Department of Computer Science, Johns Hopkins University m.nadejde@sms.ed.ac.uk, a.birch@ed.ac.uk, phi@jhu.edu Abstract String-to-tree MT systems translate verbs without lexical or syntactic context on the source side and with limited target- side context. The lack of context is one reason why verb translation recall is as low as 45.5%. We propose a verb lexicon model trained with a feed- forward neural network that predicts the target verb condi- tioned on a wide source-side context. We show that a syntac- tic context extracted from the dependency parse of the source sentence improves the model’s accuracy by 1.5% over a base- line trained on a window context. When used as an extra feature for re-ranking the n-best list produced by the string-to-tree MT system, the verb lexi- con model improves verb translation recall by more than 7%. 1. Introduction Syntax-based MT systems handle long distance reordering with synchronous translation rules. Below we show an ex- ample of a German-English synchronous rule which contains one lexical token on the source side sich which is a reflexive pronoun and several non-terminals1: root → ⟨V BZ sich NP PP, NP V BZ PP ⟩ The non-terminals, VBZ (verb part-of-speech tag), NP (noun phrase) and PP (prepositional phrase) represent the re- ordering of the verb and its arguments according to the target side word order. However the rule does not contain a lexical head for the verb, the subject or the prepositional modifier. Therefore the entire predicate argument structure is trans- lated by subsequent independent rules. The verb in particular will be translated by a lexical rule which is the equivalent of a one word phrase-pair. The language model context is also limited, and will capture at most the verb and one main argu- ment. Due to the lack of larger source or target context the verb is often mistranslated, as shown in Figure 1. In this work we propose to improve lexical choices for verbs by learning a 1String-to-tree translation rules have generic (X) non-terminal labels on the source-side that correspond one-to-one with syntactic non-terminal la- bels on the target side. verb-specific lexicon model conditioned on a wide syntactic source-side context. Several Discriminative Word Lexicon (DWL) models with source-side features have addressed the problem of word sense disambiguation in phrase-based MT [1, 2, 3]. However this is the first work that addresses specifically the problem of verb translation in string-to-tree systems. We train a verb-specific lexicon model since verbs have the most outgoing dependency relations, are central to semantic struc- tures and therefore would benefit most from a source-side syntactic context. The proposed verb lexicon model is trained with a feed- forward neural network (FFNN) which, unlike DWL models, allows parameter sharing across target words and avoids ex- ploding feature spaces. Previous lexicon models trained with FFNN [4] using global source-side context were inefficient to train and did not scale to large …

Content truncated to 500 words