Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Leveraging Large Amounts of Weakly Supervised Data for Multi-Language
  Sentiment Classification   This paper presents a novel approach for multi-lingual sentiment
classification in short texts. This is a challenging task as the amount of
training data in languages other than English is very limited. Previously
proposed multi-lingual approaches typically require to establish a
correspondence to English for which powerful classifiers are already available.
In contrast, our method does not require such supervision. We leverage large
amounts of weakly-supervised data in various languages to train a multi-layer
convolutional network and demonstrate the importance of using pre-training of
such networks. We thoroughly evaluate our approach on various multi-lingual
datasets, including the recent SemEval-2016 sentiment prediction benchmark
(Task 4), where we achieved state-of-the-art performance. We also compare the
performance of our model trained individually for each language to a variant
trained for all languages at once. We show that the latter model reaches
slightly worse - but still acceptable - performance when compared to the single
language model, while benefiting from better generalization properties across
languages.

17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph