Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering

The dominant neural architectures in question answer retrieval are based on recurrent or convolutional encoders configured with complex word matching layers. Given that recent architectural innovations are mostly new word interaction layers or attention-based matching mechanisms, it seems to be a well-established fact that these components are mandatory for good performance. Unfortunately, the memory and computation cost incurred by these complex mechanisms are undesirable for practical applications. As such, this paper tackles the question of whether it is possible to achieve competitive performance with simple neural architectures. We propose a simple but novel deep learning architecture for fast and efficient question-answer ranking and retrieval. More specifically, our proposed model, \textsc{HyperQA}, is a parameter efficient neural network that outperforms other parameter intensive models such as Attentive Pooling BiLSTMs and Multi-Perspective CNNs on multiple QA benchmarks. The novelty behind \textsc{HyperQA} is a pairwise ranking objective that models the relationship between question and answer embeddings in Hyperbolic space instead of Euclidean space. This empowers our model with a self-organizing ability and enables automatic discovery of latent hierarchies while learning embeddings of questions and answers. Our model requires no feature engineering, no similarity matrix matching, no complicated attention mechanisms nor over-parameterized layers and yet outperforms and remains competitive to many models that have these functionalities on multiple benchmarks.Comment: Accepted at WSDM 201

Does citizenship matter? The economic impact of naturalizations in Germany

The paper analyzes whether citizenship acquisition affects the labor market performance of immigrants in Germany. The study uses actual micro data from the IAB employment sample, which covers more than 80% of the whole labor force in Germany. The econometric analysis is carried out using both cross-sectional and panel data techniques, which allow to disentangle the effects of self-selection and legal impact of citizenship acquisition. The estimates from a simple OLS specification suggest the existence of a wage premium of naturalized immigrants. Panel estimates show an immediate positive naturalization effect on wages and an accelerated wage growth in the years after the naturalization event. Both results are consistent with the argument that naturalization increases the labor market opportunities of immigrants in various ways.Naturalization, self-selection, socioeconomic integration

Spain’s referendum on the European Constitutional Treaty: a quantitative analysis within the conceptual framework of first and second order elections

In contrast to the attention devoted to the rejection of the EU Constitutional Treaty at French and Dutch referenda; the Spanish referendum, where this Treaty was ratified, remained under-researched by political scientists. This paper analyses the voting behaviour at the Spanish referendum on the EU Constitutional Treaty with the use of quantitative methods and the concept of first and second-order elections. This paper finds that the Spanish referendum was a second-order referendum, because the effects of domestic political issues in Spain had a greater impact on the electoral behaviour of Spanish voters than had genuinely European issues. This finding raises doubts over the suitability of using direct democracy in the EU in order to raise the legitimacy and democratic accountability of the European project

Cross-national differences in the labour force attachment of mothers in Western and Eastern Europe

This paper examines cross-national differences in the labour force attachment of two specific subgroups of mothers: the stay-at-home mothers (homemakers) and those on maternity or parental leave. The justification for focusing on homemakers is that these women constitute an untapped source of labour and are among those who would need to join the labour market in order to reach the EU employment target. As to those on leave, their temporary absence from work means that they will soon be facing a time when they have to decide whether or not to return to the labour market. They are therefore also a key group to consider. In this paper, the characteristics and labour market intentions of these two subgroups of women are analysed using data from the Generations and Gender Survey (GGS) for ten countries: five Western European countries (Austria, Belgium, France, Germany and Italy), and five Eastern European countries (Bulgaria, Georgia, Lithuania, Romania and the Russian Federation). This paper is structured as follows. It first reviews the literature on women’s employment by focusing on both individual and macro-level factors, including values, education and family circumstances. It then moves on to a presentation of the data and methods, followed by the results of the data analysis. The paper concludes with a discussion of the results and their policy implications.

Religious practices among Islamic immigrants: Moroccan and Turkish men in Belgium

This study examines the religious participation of Islamic immigrants in Belgium using data from the Migration History and Social Mobility Survey collected in 1994–1996 from 2,200 men who had immigrated from Turkey and Morocco. Religious participation is measured as mosque attendance, fasting during Ramadan, and sacrificing a sheep at the Festival of Sacrifice. Results show that the religious participation of Islamic immigrants depends on both premigration and postmigration characteristics. Religious participation is higher among immigrants who: (1) attended a Koranic school in their country of origin, (2) were socialized in a religious region of their home country, (3) received little schooling, (4) currently live in an area of Belgium with a greater number of mosques, and (5) associate with a high number of co-ethnics. These results suggest that the religious participation of Islamic immigrants in Belgium is an outcome of characteristics unique to immigrants as well as processes common among the general population.

Zero-Shot Cross-Lingual Transfer with Meta Learning

Learning what to share between tasks has been a topic of great importance recently, as strategic sharing of knowledge has been shown to improve downstream task performance. This is particularly important for multilingual applications, as most languages in the world are under-resourced. Here, we consider the setting of training models on multiple different languages at the same time, when little or no data is available for languages other than English. We show that this challenging setup can be approached using meta-learning, where, in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first. We experiment using standard supervised, zero-shot cross-lingual, as well as few-shot cross-lingual settings for different natural language understanding tasks (natural language inference, question answering). Our extensive experimental setup demonstrates the consistent effectiveness of meta-learning for a total of 15 languages. We improve upon the state-of-the-art for zero-shot and few-shot NLI (on MultiNLI and XNLI) and QA (on the MLQA dataset). A comprehensive error analysis indicates that the correlation of typological features between languages can partly explain when parameter sharing learned via meta-learning is beneficial.Comment: Accepted as long paper in EMNLP2020 main conferenc