We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations.
Many Natural Language Processing applications nowadays rely on pre-trained word representations estimated from large text corpora such as news collections, Wikipedia and Web Crawl.
To gain a better understanding of the variation we find in face description and the possible issues that this may raise, we also conducted an annotation study on a subset of the corpus.
We present new data and semantic parsing methods for the problem of mapping English sentences to Bash commands (NL2Bash).
In addition, we have observed that the class prior distributions differ significantly between the languages.
The goal of this work is to design a machine translation (MT) system for a low-resource family of dialects, collectively known as Swiss German, which are widely spoken in Switzerland but seldom written.