Evaluating Gender Bias in Machine Translation

We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT). Our approach uses two recent coreference resolution datasets composed of English sentences which cast participants into non-stereotypical gender roles (e.g., "The doctor asked the nurse to help her in the operation"). We devise an automatic gender bias evaluation method for eight target languages with grammatical gender, based on morphological analysis (e.g., the use of female inflection for the word "doctor"). Our analyses show that four popular industrial MT systems and two recent state-of-the-art academic MT models are significantly prone to gender-biased translation errors for all tested target languages. Our data and code are made publicly available.Comment: Accepted to ACL 201

A big data approach towards sarcasm detection in Russian

We present a set of deterministic algorithms for Russian inflection and automated text synthesis. These algorithms are implemented in a publicly available web-service www.passare.ru. This service provides functions for inflection of single words, word matching and synthesis of grammatically correct Russian text. Selected code and datasets are available at https://github.com/passare-ru/PassareFunctions/ Performance of the inflectional functions has been tested against the annotated corpus of Russian language OpenCorpora, compared with that of other solutions, and used for estimating the morphological variability and complexity of different parts of speech in Russian.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0255

Прогнозирование политических предпочтений в социальных сетях (на материале ВКонтакте)

The authors hypothesize that textual information posted on personal pages on social media reflects the political views of users to some extent. Therefore, this textual information can be used to predict political views on social media. The authors conduct experiments on textual data from user pages and test two machine learning methods to classify pages that declare different political preferences. To undertake a study, the authors collected anonymous open textual data of users of the VKontakte social network (the number of pages is 10 123). Data collection was carried out using the VKontakte Application Programming Interface (VK API). As a result of the analysis of the collected data, the authors discovered two types of textual information. The first is a text filled by the user by selecting one of several possible values (binary or categorical variables). The field “Political Views” is one of these text fields, it provides nine options for selection. The second type of text information includes information entered by the user in an arbitrary form (interests, activities, etc.). The authors trained and tested two machine learning models to predict users’ political views based on the remaining text information from their pages: a) linear support vector classifier using text representations from the bag-of-words model; b) neural network using Multilingual BERT text embeddings. The results show that the models sufficiently successfully perform binary classification of users who have polar political views (for example, communists – libertarians, communists – ultra-conservatives). Nevertheless, the results for the groups of users that have close political views are significantly lower. In addition, the authors investigated the assumption that users often indicate “indifferent” political views as “moderate”. The authors classified the groups of users who declare indifferent or moderate views (the two largest categories in our dataset) and users who indicated other political preferences. The results demonstrate a sufficiently high performance for the classification of custom pages based on these two political views

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 171

This bibliography lists 186 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1977