Linguistic evaluation of support verb constructions by OpenLogos and google translate

This paper presents a systematic human evaluation of translations of English support verb constructions produced by a rule-based machine translation (RBMT) system (OpenLogos) and a statistical machine translation (SMT) system (Google Translate) for five languages: French, German, Italian, Portuguese and Spanish. We classify support verb constructions by means of their syntactic structure and semantic behavior and present a qualitative analysis of their translation errors. The study aims to verify how machine translation (MT) systems translate fine-grained linguistic phenomena, and how well-equipped they are to produce high-quality translation. Another goal of the linguistically motivated quality analysis of SVC raw output is to reinforce the need for better system hybridization, which leverages the strengths of RBMT to the benefit of SMT, especially in improving the translation of multiword units. Taking multiword units into account, we propose an effective method to achieve MT hybridization based on the integration of semantico-syntactic knowledge into SMT.info:eu-repo/semantics/acceptedVersio

Mixed up with machine Translation: Multi-word Units Disambiguation Challenge.

With the rapid evolution of the Internet, translation has become part of the daily life of ordinary users, not only of professional translators. Machine translation has evolved along with different types of computer-assisted translation tools. Qualitative progress has been made in the field of machine translation, but not all problems have been solved. The current times are auspicious for the development of more sophisticated evaluation tools that measure the performance of specific linguistic phenomena. One problem in particular, namely the poor analysis and translation of multi-word units, is an arena where investment in linguistic knowledge systems with the goal of improving machine translation would be beneficial. This paper addresses the difficulties multi-word units present to machine translation, by comparing translations performed by systems adopting different approaches to machine translation. It proposes a solution for improving the quality of the translation of multi-word units by adopting a methodology that combines Lexicon Grammar resources with OpenLogos lexical resources and semantico-syntactic rules. Finally, it discusses how an ideal machine translation evaluation tool might look to correctly evaluate the performance of machine translation engines with regards to multi-word units and thus to contribute to the improvement of translation quality

Taking on new challenges in multi-word unit processing for Machine Translation

This paper discusses the qualitative comparative evaluation performed on the results of two machine translation systems with different approaches to the processing of multi-word units. It proposes a solution for overcoming the difficulties multi-word units present to machine translation by adopting a methodology that combines the lexicon grammar approach with OpenLogos ontology and semantico-syntactic rules. The paper also discusses the importance of a qualitative evaluation metrics to correctly evaluate the performance of machine translation engines with regards to multi-word units

Machine translation of non-contiguous multiword units

Non-adjacent linguistic phenomena such as non-contiguous multiwords and other phrasal units containing insertions, i.e., words that are not part of the unit, are difficult to process and remain a problem for NLP applications. Non-contiguous multiword units are common across languages and constitute some of the most important challenges to high quality machine translation. This paper presents an empirical analysis of non-contiguous multiwords, and highlights our use of the Logos Model and the Semtab function to deploy semantic knowledge to align non-contiguous multiword units with the goal to translate these units with high fidelity. The phrase level manual alignments illustrated in the paper were produced with the CLUE-Aligner, a Cross-Language Unit Elicitation alignment tool.info:eu-repo/semantics/acceptedVersio

Multi-word unit processing in machine translation. Developing and using language resources for multi-word unit processing in machine translation

2011 - 2012XI n.s

MWE processing in Machine Translation: State of the Art and Open Challenges

The poster describes the state of the art and the open challenges in MWE processing in Machine Translatio

Sustainable Language Technology for African Languages

Peer reviewe

Contractions: to align or not to align, that is the question

This paper performs a detailed analysis on the alignment of Portuguese contractions, based on a previously aligned bilingual corpus. The alignment task was performed manually in a subset of the English-Portuguese CLUE4Translation Alignment Collection. The initial parallel corpus was pre-processed and a decision was made as to whether the contraction should be maintained or decomposed in the alignment. Decomposition was required in the cases in which the two words that have been concatenated, i.e., the preposition and the determiner or pronoun, go in two separate translation alignment pairs (PT - [no seio de] [a União Europeia] EN - [within] [the European Union]). Most contractions required decomposition in contexts where they are positioned at the end of a multiword unit. On the other hand, contractions tend to be maintained when they occur at the beginning or in the middle of the multiword unit, i.e., in the frozen part of the multiword (PT - [no que diz respeito a] EN - [with regard to] or PT - [além disso] EN - [in addition]. A correct alignment of multiwords and phrasal units containing contractions is instrumental for machine translation, paraphrasing, and variety adaptationinfo:eu-repo/semantics/acceptedVersio

Multiword expression processing: A survey

Multiword expressions (MWEs) are a class of linguistic forms spanning conventional word boundaries that are both idiosyncratic and pervasive across different languages. The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs. The issue of MWE handling is crucial for NLP applications, where it raises a number of challenges. The emergence of solutions in the absence of guiding principles motivates this survey, whose aim is not only to provide a focused review of MWE processing, but also to clarify the nature of interactions between MWE processing and downstream applications. We propose a conceptual framework within which challenges and research contributions can be positioned. It offers a shared understanding of what is meant by "MWE processing," distinguishing the subtasks of MWE discovery and identification. It also elucidates the interactions between MWE processing and two use cases: Parsing and machine translation. Many of the approaches in the literature can be differentiated according to how MWE processing is timed with respect to underlying use cases. We discuss how such orchestration choices affect the scope of MWE-aware systems. For each of the two MWE processing subtasks and for each of the two use cases, we conclude on open issues and research perspectives

PARSEME-It: an Italian corpus annotated with verbal multiword expressions

The paper describes the PARSEME-It corpus, developed within the PARSEME-It project which aims at the development of methods, tools and resources for multiword expressions (MWE) processing for the Italian language. The project is a spin-off of a larger multilingual project for more than 20 languages from several language families, namely the PARSEME COST Action. The first phase of the project was devoted to verbal multiword expressions (VMWEs). They are a particularly interesting lexical phenomenon because of frequent discontinuity and long-distance dependency. Besides they are very challenging for deep parsing and other Natural Language Processing (NLP) tasks. Notably, MWEs are pervasive in natural languages but are particularly difficult to be handled by NLP tools because of their characteristics and idiomaticity. They pose many challenges to their correct identification and processing: they are a linguistic phenomenon on the edge between lexicon and grammar, their meaning is not simply the addition of the meanings of the single constituents of the MWEs and they are ambiguous since in several cases their reading can be literal or idiomatic. Although several studies have been devoted to this topic, to the best of our knowledge, our study is the first attempt to provide a general framework for the identification of VMWEs in running texts and a comprehensive corpus for the Italian language