  Language Identification (LID) is a challenging task, especially when the
input texts are short and noisy such as posts and statuses on social media or
chat logs on gaming forums. The task has been tackled by either designing a
feature set for a traditional classifier (e.g. Naive Bayes) or applying a deep
neural network classifier (e.g. Bi-directional Gated Recurrent Unit,
Encoder-Decoder). These methods are usually trained and tested on a huge amount
of private data, then used and evaluated as off-the-shelf packages by other
researchers using their own datasets, and consequently the various results
published are not directly comparable. In this paper, we first create a new
massive labelled dataset based on one year of Twitter data. We use this dataset
to test several existing language identification systems, in order to obtain a
set of coherent benchmarks, and we make our dataset publicly available so that
others can add to this set of benchmarks. Finally, we propose a shallow but
efficient neural LID system, which is a ngram-regional convolution neural
network enhanced with an attention mechanism. Experimental results show that
our architecture is able to predict tens of thousands of samples per second and
surpasses all state-of-the-art systems with an improvement of 5%.
