American Translators Association Conference

This association was founded in 1959 and is now the largest professional association of translators and interpreters in the United States with more than 11,000 members in 90 countries. One of its primary missions is to promote the professional development of translators and interpreters. Annually, the ATA organizes a conference, a four-day international event offering language professionals more than 150 continuing education sessions, seminars, and workshops. This poster presents a selection of the sessions that I attended, including a summary of some of the dilemma’s and questions that translators and interpreters face

The translatability of metaphor in LSP: application of a decision-making model

The pragmatic approach to translation implies the consideration of translation as a useful test case for understanding the role of language in social life. Under this view this article analyses the decision-making stage translators go through in the course of formulating a TT. Hence this article contributes both to enhance the status of translation theory and to explain some of the decisions taken by the Spanish translators of three English Manuals of Economics. In short, we have argued that the use of a 'maximax' strategy for translating English metaphors as Spanish similarity-creating metaphors can be attributed to subjective factors, especially to the translators' cognitive system, their knowledge bases, the task specification, and the text type specific problem space. As a result, we have also claimed that proposals for translating microtextual problems —for example, metaphors — can benefit from the study of the above-mentioned subjective factors since they allow or inhibit the translators' choices in the decision-making stage of the translation process

Identifying the machine translation error types with the greatest impact on post-editing effort

Translation Environment Tools make translators' work easier by providing them with term lists, translation memories and machine translation output. Ideally, such tools automatically predict whether it is more effortful to post-edit than to translate from scratch, and determine whether or not to provide translators with machine translation output. Current machine translation quality estimation systems heavily rely on automatic metrics, even though they do not accurately capture actual post-editing effort. In addition, these systems do not take translator experience into account, even though novices' translation processes are different from those of professional translators. In this paper, we report on the impact of machine translation errors on various types of post-editing effort indicators, for professional translators as well as student translators. We compare the impact of MT quality on a product effort indicator (HTER) with that on various process effort indicators. The translation and post-editing process of student translators and professional translators was logged with a combination of keystroke logging and eye-tracking, and the MT output was analyzed with a fine-grained translation quality assessment approach. We find that most post-editing effort indicators (product as well as process) are influenced by machine translation quality, but that different error types affect different post-editing effort indicators, confirming that a more fine-grained MT quality analysis is needed to correctly estimate actual post-editing effort. Coherence, meaning shifts, and structural issues are shown to be good indicators of post-editing effort. The additional impact of experience on these interactions between MT quality and post-editing effort is smaller than expected

Guidelines to Study Differences in Expressiveness between Ontology Specification Languages: A Case Of Study

We focus on our experiences on translating ontologies between two ontology languages, FLogic and Ontolingua, in the framework of Methontology and ODE. Rather than building "ad hoc" translators between languages or using KIF, our option consists of translating through ODE intermediate representations. So, we have built direct translators from ODE intermediate representations to Ontolingua and FLogic, and we have also built reverse translators from these two languages to ODE intermediate representations. Expressiveness of the target languages is the main feature to analyse when automatically generating ontologies from ODE intermediate representations. Therefore, we analyse the expressiveness of Ontolingua and FLogic for creating classes, instances, relations, functions and axioms, which are the essential components in ontologies. The motivation for this analysis can be found in the (KA)² initiative and can be easily extended to any other domains and languages

Next generation translation and localization: Users are taking charge

Nonprofit translation activity driven by users and volunteer translators now represent a market force that easily rivals the mainstream translation and localization industries. While they still try to understand the drivers behind this nonprofit movement and occasionally attempt to tap in to these newly discovered “resources”, nonprofit translation efforts for good causes are growing at a phenomenal rate. This paper examines the case of The Rosetta Foundation as an example of a not-for-profit volunteer translation facilitator. The paper focuses on the motivating factors for volunteer translators. A survey was distributed to the several hundred volunteers who signed up as translators in the first few months of The Rosetta Foundation’s launch. The paper provides some background on what might well become the next generation of translation and localization and present the results of the survey. Finally, we will explore how The Rosetta Foundation, and other not-for-profit translation organisations might better motivate volunteers to contribute their skills and expertise

Integrating N-best SMT outputs into a TM system

In this paper, we propose a novel frame- work to enrich Translation Memory (TM) systems with Statistical Machine Translation (SMT) outputs using ranking. In order to offer the human translators multiple choices, instead of only using the top SMT output and top TM hit, we merge the N-best output from the SMT system and the k-best hits with highest fuzzy match scores from the TM system. The merged list is then ranked according to the prospective post-editing effort and provided to the translators to aid their work. Experiments show that our ranked output achieve 0.8747 precision at top 1 and 0.8134 precision at top 5. Our framework facilitates a tight integration between SMT and TM, where full advantage is taken of TM while high quality SMT output is availed of to improve the productivity of human translators