Despite the recent success of deep neural networks in natural language processing and
other spheres of artificial intelligence, their interpretability remains a challenge. We analyze …

Amid the rapid advancement of neural machine translation, the challenge of data sparsity
has been a major obstacle. To address this issue, this study proposes a general data …

Neural machine translation (NMT) models typically operate with a fixed vocabulary, but
translation is an open-vocabulary problem. Previous work addresses the translation of out-of …

Neural machine translation (MT) models obtain state-of-the-art performance while
maintaining a simple, end-to-end architecture. However, little is known about what these …

In this paper, we present Farasa, a fast and accurate Arabic segmenter. Our approach is
based on SVM-rank using linear kernels. We measure the performance of the segmenter in …

We contribute 5-gram counts and language models trained on the Common Crawl corpus, a
collection over 9 billion web pages. This release improves upon the Google n-gram counts …

Analysing translation quality in regards to specific linguistic phenomena has historically
been difficult and time-consuming. Neural machine translation has the attractive property …

Transfer learning based on pretraining language models on a large amount of raw data has
become a new norm to reach state-of-the-art performance in NLP. Still, it remains unclear …

Interactive machine translation research has focused primarily on predictive typing, which
requires a human to type parts of the translation. This paper explores an interactive setting in …

Transliteration is very important in the Indian language context due to the usage of multiple
scripts and the widespread use of romanized inputs. However, few training and evaluation …