Document-level machine translation with word vector models

In this paper we apply distributional semantic information to document-level machine translation. We train monolingual and bilingual word vector models on large corpora and we evaluate them first in a cross-lingual lexical substitution task and then on the final translation task. For translation, we incorporate the semantic information in a statistical document-level decoder (Docent), by enforcing translation choices that are semantically similar to the context. As expected, the bilingual word vector models are more appropriate for the purpose of translation. The final document-level translator incorporating the semantic model outperforms the basic Docent (without semantics) and also performs slightly over a standard sentence level SMT system in terms of ULC (the average of a set of standard automatic evaluation metrics for MT). Finally, we also present some manual analysis of the translations of some concrete documentsPeer ReviewedPostprint (published version

Experiments on document level machine translation

Most of the current SMT systems work at sentence level. They translate a text assuming that sentences are independent, but, when one looks at a well formed document, it is clear that there exist many inter sentence relations. There is much contextual information that, unfortunately, is lost when translating sentences in an independent way. We want to improve translation coherence and cohesion using document level information. So, we are interested in develop new strategies to take advantage of context information to achieve our goal. For example, we want to approach this challenge developing postprocesses in order to try to fix a first translation obtained by an SMT system. Also we are interested in taking advantage of the document level translation framework given by the Docent decoder to implement and test some of our ideas. The analogous problem can be found regarding to automatic MT evaluation metrics because most of them are designed at sentence level so, they do not capture improvements in lexical cohesion and coherence or discourse structure. However, we will left this topic for future workPreprin

The UPC TweetMT participation : translating formal tweets using context information

In this paper, we describe the UPC systems that participated in the TweetMT shared task. We developed two main systems that were applied to the Spanish-Catalan language pair: a state-of-the-art phrase-based statistical machine translation system and a context-aware system. In the second approach, we define thePeer ReviewedPostprint (author’s final draft

Document-level machine translation with word vector models

In this paper we apply distributional semantic information to document-level machine translation. We train monolingual and bilingual word vector models on large corpora and we evaluate them first in a cross-lingual lexical substitution task and then on the final translation task. For translation, we incorporate the semantic information in a statistical document-level decoder (Docent), by enforcing translation choices that are semantically similar to the context. As expected, the bilingual word vector models are more appropriate for the purpose of translation. The final document-level translator incorporating the semantic model outperforms the basic Docent (without semantics) and also performs slightly over a standard sentence level SMT system in terms of ULC (the average of a set of standard automatic evaluation metrics for MT). Finally, we also present some manual analysis of the translations of some concrete documentsPeer Reviewe

A Shortest-path Method for Arc-factored Semantic Role Labeling

We introduce a Semantic Role Labeling (SRL) parser that finds semantic roles for a predicate together with the syntactic paths linking predicates and arguments. Our main contribution is to formulate SRL in terms of shortest-path inference, on the assumption that the SRL model is restricted to arc-factored features of the syntactic paths behind semantic roles. Overall, our method for SRL is a novel way to exploit larger variability in the syntactic realizations of predicate-argument relations, moving away from pipeline architectures. Experiments show that our approach improves the robustness of the predictions, producing arc-factored models that perform closely to methods using unrestricted features from the syntaxPeer ReviewedPostprint (published version

A shortest-path method for arc-factored semantic role labeling

We introduce a Semantic Role Labeling (SRL) parser that finds semantic roles for a predicate together with the syntactic paths linking predicates and arguments. Our main contribution is to formulate SRL in terms of shortest-path inference, on the assumption that the SRL model is restricted to arc-factored features of the syntactic paths behind semantic roles. Overall, our method for SRL is a novel way to exploit larger variability in the syntactic realizations of predicate-argument relations, moving away from pipeline architectures. Experiments show that our approach improves the robustness of the predictions, producing arc-factored models that perform closely to methods using unrestricted features from the syntaxPeer Reviewe

A shortest-path method for arc-factored semantic role labeling

We introduce a Semantic Role Labeling (SRL) parser that finds semantic roles for a predicate together with the syntactic paths linking predicates and arguments. Our main contribution is to formulate SRL in terms of shortest-path inference, on the assumption that the SRL model is restricted to arc-factored features of the syntactic paths behind semantic roles. Overall, our method for SRL is a novel way to exploit larger variability in the syntactic realizations of predicate-argument relations, moving away from pipeline architectures. Experiments show that our approach improves the robustness of the predictions, producing arc-factored models that perform closely to methods using unrestricted features from the syntaxPeer Reviewe

Document-level machine translation with word vector models

In this paper we apply distributional semantic information to document-level machine translation. We train monolingual and bilingual word vector models on large corpora and we evaluate them first in a cross-lingual lexical substitution task and then on the final translation task. For translation, we incorporate the semantic information in a statistical document-level decoder (Docent), by enforcing translation choices that are semantically similar to the context. As expected, the bilingual word vector models are more appropriate for the purpose of translation. The final document-level translator incorporating the semantic model outperforms the basic Docent (without semantics) and also performs slightly over a standard sentence level SMT system in terms of ULC (the average of a set of standard automatic evaluation metrics for MT). Finally, we also present some manual analysis of the translations of some concrete documentsPeer Reviewe

The UPC TweetMT participation : translating formal tweets using context information

In this paper, we describe the UPC systems that participated in the TweetMT shared task. We developed two main systems that were applied to the Spanish-Catalan language pair: a state-of-the-art phrase-based statistical machine translation system and a context-aware system. In the second approach, we define thePeer Reviewe

Experiments on document level machine translation

Most of the current SMT systems work at sentence level. They translate a text assuming that sentences are independent, but, when one looks at a well formed document, it is clear that there exist many inter sentence relations. There is much contextual information that, unfortunately, is lost when translating sentences in an independent way. We want to improve translation coherence and cohesion using document level information. So, we are interested in develop new strategies to take advantage of context information to achieve our goal. For example, we want to approach this challenge developing postprocesses in order to try to fix a first translation obtained by an SMT system. Also we are interested in taking advantage of the document level translation framework given by the Docent decoder to implement and test some of our ideas. The analogous problem can be found regarding to automatic MT evaluation metrics because most of them are designed at sentence level so, they do not capture improvements in lexical cohesion and coherence or discourse structure. However, we will left this topic for future wor