At the turn of the last century, the Argentine economy was on a path to prosperity that never fully developed. International trade and trade policies are often identified as a major culprit. In this paper, we review the history of Argentine trade policy to uncover its exceptional features and to explore its contribution to the Argentine debacle. Our analysis tells a story of bad trade policies,...

We decompose distributional preferences into fair-mindedness (weight on oneself vs. others) and equality-efficiency tradeoffs, and measure both at the individual level in a large and diverse sample of Americans. We find considerable heterogeneity in both the extent of fairmindedness and willingness to trade off equality and efficiency, much of which cannot be explained by standard socioeconomic...

Since synonyms are important lexical knowledge, various methods have been proposed for automatic synonym acquisition. Whereas most of the methods are based on the distributional hypothesis and utilize contextual clues, little attention has been paid to what kind of contextual information is useful for the purpose. As one of the ways to augment contextual information, we propose the use of indir...

This paper looks into the question to what extent intonative structure determines word order variation in a particular type of syntactic structures in Dutch. Certain subordinate clauses in this language may contain verbal groups consisting of an auxiliary (aux) and a participle (part) that appear in sentence-final position. The order of these verbal elements is fundamentally free so that both a...

We present a model for compositional distributional semantics related to the framework of Coecke et al. (2010), and emulating formal semantics by representing functions as tensors and arguments as vectors. We introduce a new learning method for tensors, generalising the approach of Baroni and Zamparelli (2010). We evaluate it on two benchmark data sets, and find it to outperform existing leadin...

In recent years, distributional models (DMs) have shown great success in representing lexical semantics. In this work we show that the extent to which DMs represent semantic knowledge is highly dependent on the type of knowledge. We pose the task of predicting properties of concrete nouns in a supervised setting, and compare between learning taxonomic properties (e.g., animacy) and attributive ...

We present a novel technique for learning semantic representations, which extends the distributional hypothesis to multilingual data and joint-space embeddings. Our models leverage parallel data and learn to strongly align the embeddings of semantically equivalent sentences, while maintaining sufficient distance between those of dissimilar sentences. The models do not rely on word alignments or...