[PDF] Top 20 Semantic Role Features for Machine Translation
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Semantic Role Features for Machine Translation
... missing semantic roles in the target sentence. To use these features during de- coding, we need to keep track of the semantic role sequences (SRS) for partial translations, which can be ... See full document
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Semantic Roles for String to Tree Machine Translation
... Figure 2 depicts an example of a complete se- mantic rule. Numbers following grammatical cat- egories (for example, S-8 at the root) are the re- fined nonterminals produced by the split-merge parser. In general, the tree ... See full document
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Corpus Expansion for Statistical Machine Translation with Semantic Role Label Substitution Rules
... to Semantic Role Labeling (Palmer et ...and semantic constraints to select the ...with features derived from standard phrase based translation models and bilingual lan- guage models to ... See full document
5
A Semantic Evaluation of Machine Translation Lexical Choice
... improve translation performance, we evaluate the impact of training the PBSMT system on more than the Europarl data used for controlled comparison with ...combine translation, reordering and language models ... See full document
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Robust Machine Translation Evaluation with Entailment Features
... general semantic relatedness (observers/commentators), phrasal re- placements (and/as well as), and an active/passive alternation that implies structural change (is de- clared/are terming ...RTE features ... See full document
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Machine Translation Evaluation with Textual Entailment Features
... favorite translation deviates considerably from the ref- erence translation in lexical choice, syntactic structure, and word order, for which it is punished by T RAD M T ...propriate translation of ... See full document
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A Semantic Feature for Statistical Machine Translation
... important role played by source language structure and con- text within the task of human translation (Padilla & Bajo, ...statistical machine translation to tackle with source-context ... See full document
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Utilizing Target Side Semantic Role Labels to Assist Hierarchical Phrase based Machine Translation
... The second problem is that the SRL labels are only on the constituents of predicate and arguments. There is no analysis conducted inside the augments. That is different from syntactic parsing or depen- dency parsing, ... See full document
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Statistical Machine Translation Features with Multitask Tensor Networks
... Approaches to incorporating source context into a neural network model differ mainly in how they represent the source sentence and in how long is the history they keep. In terms of representa- tion of the source ... See full document
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Learning for Semantic Parsing with Statistical Machine Translation
... shallow semantic analysis, such as semantic role labeling and word-sense disam- ...of semantic parsing, which is the con- struction of a complete, formal, symbolic, mean- ing representation ... See full document
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Evaluating Machine Translation Utility via Semantic Role Labels
... useful translation is one from which human readers can successfully and accurately understand the essential meanings of the original input language sen- ...today’s machine trans- lation systems still often ... See full document
5
Semantic Evaluation of Machine Translation
... of machine translation in use that focus on surface word level suffer from their lack of tolerance of linguistic variance, and the incorporation of linguistic features can improve their ...lexical ... See full document
5
Structured vs Flat Semantic Role Representations for Machine Translation Evaluation
... evaluating translation adequacy, particularly at the sentence ...grammatical translation can achieve a high syntax- based score yet still make significant errors arising from confusion of semantic ... See full document
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11,001 New Features for Statistical Machine Translation
... Our syntax-based system transforms source Chinese strings into target English syntax trees. Following previous work in statistical MT (Brown et al., 1993), we envision a noisy-channel model in which a lan- guage model ... See full document
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Beyond BLEU:Training Neural Machine Translation with Semantic Similarity
... ding model trained on a large external corpus of paraphrase data. Using an embedding model to evaluate similarity allows the range of possible scores to be continuous and, as a result, introduces fine-grained ... See full document
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Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers
... five translation tasks: Machine Translation of News, Machine Translation of IT domain, Biomedical Translation, Multimodal Machine Translation, and Cross-lingual ... See full document
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Designing a Frame Semantic Machine Translation Evaluation Metric
... the semantic scale, POF is based on a num- ber of pre-existing works in which frame seman- tics has been applied to ...of translation is that, ideally, there is a one-to-one correspondence on the frame ... See full document
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Proceedings of the Human Informed Translation and Interpreting Technology Workshop (HiT IT 2019)
... the translation process, and of assisting human ...Assisted Translation (CAT) tools, electronic dictionaries, concordancers, spell-checkers, terminological databases, terminology extraction tools, ... See full document
10
Urdu to English Machine Translation using Bilingual Evaluation Understudy
... RBMT is a type of large scale rule based system. Therefore computational cost and resource requirements are high in order to create a RBMT system. It is formulated on the basis of morphological, syntactic and ... See full document
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