Greek Idioms Processing in the Machine Translation System CAT2

This paper describes Machine Translation (MT) and the associated processing of idioms. Particularly, this research examines the rule-based CAT 2 MT system and experiments with Greek sentences containing idioms. The paper also provides an in depth discussion of the resources and the procedure which have enhanced the translation of the quality of the idioms for the chosen German-Greek language pair. Greek is a morphologically rich language and the successful processing of Greek idioms within CAT 2 has proven that MT can translate idioms correctly, whatever the level of language complexity

Question Type Guided Attention in Visual Question Answering

Visual Question Answering (VQA) requires integration of feature maps with drastically different structures and focus of the correct regions. Image descriptors have structures at multiple spatial scales, while lexical inputs inherently follow a temporal sequence and naturally cluster into semantically different question types. A lot of previous works use complex models to extract feature representations but neglect to use high-level information summary such as question types in learning. In this work, we propose Question Type-guided Attention (QTA). It utilizes the information of question type to dynamically balance between bottom-up and top-down visual features, respectively extracted from ResNet and Faster R-CNN networks. We experiment with multiple VQA architectures with extensive input ablation studies over the TDIUC dataset and show that QTA systematically improves the performance by more than 5% across multiple question type categories such as "Activity Recognition", "Utility" and "Counting" on TDIUC dataset. By adding QTA on the state-of-art model MCB, we achieve 3% improvement for overall accuracy. Finally, we propose a multi-task extension to predict question types which generalizes QTA to applications that lack of question type, with minimal performance loss

On the Dynamics of Gender Learning in Speech Translation

Due to the complexity of bias and the opaque nature of current neural approaches, there is a rising interest in auditing language technologies. In this work, we contribute to such a line of inquiry by exploring the emergence of gender bias in Speech Translation (ST). As a new perspective, rather than focusing on the final systems only, we examine their evolution over the course of training. In this way, we are able to account for different variables related to the learning dynamics of gender translation, and investigate when and how gender divides emerge in ST. Accordingly, for three language pairs (en ? es, fr, it) we compare how ST systems behave for masculine and feminine translation at several levels of granularity. We find that masculine and feminine curves are dissimilar, with the feminine one being characterized by more erratic behaviour and late improvements over the course of training. Also, depending on the considered phenomena, their learning trends can be either antiphase or parallel. Overall, we show how such a progressive analysis can inform on the reliability and time-wise acquisition of gender, which is concealed by static evaluations and standard metrics

Good, but not always Fair: An Evaluation of Gender Bias for three Commercial Machine Translation Systems

Machine Translation (MT) continues to make significant strides in quality and is increasingly adopted on a larger scale. Consequently, analyses have been redirected to more nuanced aspects, intricate phenomena, as well as potential risks that may arise from the widespread use of MT tools. Along this line, this paper offers a meticulous assessment of three commercial MT systems - Google Translate, DeepL, and Modern MT - with a specific focus on gender translation and bias. For three language pairs (English-Spanish, English-Italian, and English-French), we scrutinize the behavior of such systems at several levels of granularity and on a variety of naturally occurring gender phenomena in translation. Our study takes stock of the current state of online MT tools, by revealing significant discrepancies in the gender translation of the three systems, with each system displaying varying degrees of bias despite their overall translation quality

Good, but not always Fair: An Evaluation of Gender Bias for three commercial Machine Translation Systems

Machine Translation (MT) continues to make significant strides in quality and is increasingly adopted on a larger scale. Consequently, analyses have been redirected to more nuanced aspects, intricate phenomena, as well as potential risks that may arise from the widespread use of MT tools. Along this line, this paper offers a meticulous assessment of three commercial MT systems - Google Translate, DeepL, and Modern MT - with a specific focus on gender translation and bias. For three language pairs (English/Spanish, English/Italian, and English/French), we scrutinize the behavior of such systems at several levels of granularity and on a variety of naturally occurring gender phenomena in translation. Our study takes stock of the current state of online MT tools, by revealing significant discrepancies in the gender translation of the three systems, with each system displaying varying degrees of bias despite their overall translation quality.Comment: Under review at HERMES Journa

Prototyping 1,4-butanediol (BDO) biosynthesis pathway in a cell-free transcription-translation (TX-TL) system

Current methods for assembling metabolic pathways require a process of repeated trial and error and have a long design-build-test cycle. Further, it remains a challenge to precisely tune enzyme expression levels for maximizing target metabolite production. Recently it was shown that a cell-free transcriptional-translation system (TX-TL) can be used to rapidly prototype novel complex biocircuits as well as metabolic pathways. TX-TL systems allow protein expression from multiple DNA pieces, opening up the possibility of modulating concentrations of DNA encoding individual pathway enzymes and testing the related effect on metabolite production. In this work, we demonstrate TX-TL as a platform for exploring the design space of metabolic pathways using a 1,4-BDO biosynthesis pathway as an example. Using TX-TL, we verified enzyme expression and enzyme activity and identified the conversion of 4-hydroxybutyrate to downstream metabolites as a limiting step of the 1,4-BDO pathway. We further tested combinations of various enzyme expression levels and found increasing downstream enzyme expression levels improved 1,4-BDO production

GDPR Privacy Policies in CLAUDETTE: Challenges of Omission, Context and Multilingualism

The latest developments in natural language processing and machine learning have created new opportunities in legal text analysis. In particular, we look at the texts of online privacy policies after the implementation of the European General Data Protection Regulation (GDPR). We analyse 32 privacy policies to design a methodology for automated detection and assessment of compliance of these documents. Preliminary results confirm the pressing issues with current privacy policies and the beneficial use of this approach in empowering consumers in making more informed decisions. However, we also encountered several serious issues in the process. This paper introduces the challenges through concrete examples of context dependence, omission of information, and multilingualism

Automated Implementation Process of Machine Translation System for Related Languages

The paper presents an attempt to automate all data creation processes of a rule-based shallow-transfer machine translation system. The presented methods were tested on four fully functional translation systems covering language pairs: Slovenian paired with Serbian, Czech, English and Estonian language. An extensive range of evaluation tests was performed to assess the applicability of the methods