Syntactic and semantic features for statistical and neural machine translation
Machine Translation (MT) for language pairs with long distance dependencies and word reordering, such as German-English, is prone to producing output that is lexically or syntactically incoherent. Statistical MT (SMT) models used explicit or latent syntax to improve reordering, however failed at capturing other long distance dependencies. This thesis explores how explicit sentence-level syntactic information can improve translation for such complex linguistic phenomena. In particular, we work at the level of the syntactic-semantic interface with representations conveying the predicate-argument structures. These are essential to preserving semantics in translation and SMT systems have long struggled to model them. String-to-tree SMT systems use explicit target syntax to handle long-distance reordering, but make strong independence assumptions which lead to inconsistent lexical choices. To address this, we propose a Selectional Preferences feature which models the semantic affinities between target predicates and their argument fillers using the target dependency relations available in the decoder. We found that our feature is not effective in a string-to-tree system for German-English and that often the conditioning
NaÌdejde, Maria
Machine Translation ; sentence-level syntactic context ; syntactic-semantic interface ; Selectional Preferences ; syntax-based statistical MT system ; Verb Lexicon model ; neural networks
Syntactic and semantic features for statistical and neural machine translation. Machine Translation (MT) for language pairs with long distance dependencies and word reordering, such as German-English, is prone to producing output that is lexically or syntactically incoherent. Statistical MT (SMT) models used explicit or latent syntax to improve reordering, however failed at capturing other long distance dependencies. This thesis explores how explicit sentence-level syntactic information can improve translation for such complex linguistic phenomena. In particular, we work at the level of the syntactic-semantic interface with representations conveying the predicate-argument structures. These are essential to preserving semantics in translation and SMT systems have long struggled to model them. String-to-tree SMT systems use explicit target syntax to handle long-distance reordering, but make strong independence assumptions which lead to inconsistent lexical choices. To address this, we propose a Selectional Preferences feature which models the semantic affinities between target predicates and their argument fillers using the target dependency relations available in the decoder. We found that our feature is not effective in a string-to-tree system for German-English and that often the conditioning
syntactic_semantic feature statistical neural machine_translation machine_translation language_pair long_distance dependency word reordering german_english prone produce output lexically syntactically incoherent statistical smt model use explicit latent syntax improve reordering however_fail capture long_distance dependency explores explicit sentence_level syntactic_information improve translation complex linguistic_phenomenon particular work level syntactic_semantic interface representation convey predicate_argument structure essential preserve semantics translation_smt system long struggle model them string tree smt system use explicit target syntax handle long_distance reordering make_strong independence assumption lead inconsistent lexical_choice address propose selectional feature model semantic affinity target predicate_argument use target dependency_relation available find feature effective string tree system german_english often condition