Plural marking in argument supporting nominalizations

This paper investigates the conditions under which Argument Supporting Nominalizations (ASNs) can receive plural marking. Under ASNs, we discuss deverbal nouns that express an event and preserve argument structure. In our discussion we consider ASNs in Romanian, English and German

What Was a Relevant Translation in the 18th Century?

The paper applies RT to analyse an 18th century translation of a Latin text by the preeminent Romanian scholar Demetrius Cantemir. The translation diverges significantly from the original and was met with harsh criticism. Using the conceptual toolkit of RT, I argue that the differences between the original and its English translation were motivated by the translator’s desire to yield the same cognitive effect without putting the audience to unnecessary processing effort. Both effects and effort need to be evaluated by taking into account the respective cognitive environments of the source-text and the target-text audiences. The intertextual dimension of the text under scrutiny adds to the difficulty of communicating the same message in different languages and cultures

MaXM: Towards Multilingual Visual Question Answering

Visual Question Answering (VQA) has been primarily studied through the lens of the English language. Yet, tackling VQA in other languages in the same manner would require a considerable amount of resources. In this paper, we propose scalable solutions to multilingual visual question answering (mVQA), on both data and modeling fronts. We first propose a translation-based framework to mVQA data generation that requires much less human annotation efforts than the conventional approach of directly collection questions and answers. Then, we apply our framework to the multilingual captions in the Crossmodal-3600 dataset and develop an efficient annotation protocol to create MaXM, a test-only VQA benchmark in 7 diverse languages. Finally, we develop a simple, lightweight, and effective approach as well as benchmark state-of-the-art English and multilingual VQA models. We hope that our benchmark encourages further research on mVQA.Comment: EMNLP 2023 (Findings). https://github.com/google-research-datasets/max

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

TEACHING ENGLISH IN SEVERAL CENTRAL AND EASTERN EUROPEAN COUNTRIES

The Central and Eastern European countries find themselves at present, following a period of transition in all domains, education included. One of the greatest challenges is providing sufficient foreign language education so as to meet the growing demand especially after along period of time when foreign languages were seriously and damagingly neglected. This paper is an attempt to briefly present the way English language is taught in several Central and Eastern European Countries as well as to underline the importance of this educational process and maybe to offer some applicable solutions to teaching English in Romaniaeducation, English, methodology

Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary

We propose a method to transfer knowledge across neural machine translation (NMT) models by means of a shared dynamic vocabulary. Our approach allows to extend an initial model for a given language pair to cover new languages by adapting its vocabulary as long as new data become available (i.e., introducing new vocabulary items if they are not included in the initial model). The parameter transfer mechanism is evaluated in two scenarios: i) to adapt a trained single language NMT system to work with a new language pair and ii) to continuously add new language pairs to grow to a multilingual NMT system. In both the scenarios our goal is to improve the translation performance, while minimizing the training convergence time. Preliminary experiments spanning five languages with different training data sizes (i.e., 5k and 50k parallel sentences) show a significant performance gain ranging from +3.85 up to +13.63 BLEU in different language directions. Moreover, when compared with training an NMT model from scratch, our transfer-learning approach allows us to reach higher performance after training up to 4% of the total training steps.Comment: Published at the International Workshop on Spoken Language Translation (IWSLT), 201

Report on first selection of resources

The central objective of the Metanet4u project is to contribute to the establishment of a pan-European digital platform that makes available language resources and services, encompassing both datasets and software tools, for speech and language processing, and supports a new generation of exchange facilities for them.Peer ReviewedPreprin