Which models do they use for phrase-based SMT?
Phrase-Based SMT systems were trained using Moses, grow-diag-final-and heuristic were used for extracting phrases,  and lexicalised reordering and Batch MIRA for tuning.
Resources for the non-English languages are scarce and this paper addresses this problem in the context of machine translation, by automatically extracting parallel sentence pairs from the multilingual articles available on the Internet. In this paper, we have used an end-to-end Siamese bidirectional recurrent neural network to generate parallel sentences from comparable multilingual articles in Wikipedia. Subsequently, we have showed that using the harvested dataset improved BLEU scores on both NMT and phrase-based SMT systems for the low-resource language pairs: English--Hindi and English--Tamil, when compared to training exclusively on the limited bilingual corpora collected for these language pairs.
Both neural and statistical machine translation approaches are highly reliant on the availability of large amounts of data and are known to perform poorly in low resource settings. Recent crowd-sourcing efforts and workshops on machine translation have resulted in small amounts of parallel texts for building viable machine translation systems for low-resource pairs BIBREF0 . But, they have been shown to suffer from low accuracy (incorrect translation) and low coverage (high out-of-vocabulary rates), due to insufficient training data. In this project, we try to address the high OOV rates in low-resource machine translation systems by leveraging the increasing amount of multilingual content available on the Internet for enriching the bilingual lexicon. Comparable corpora such as Wikipedia, are collections of topic-aligned but non-sentence-aligned multilingual documents which are rich resources for extracting parallel sentences from. For example, Figure FIGREF1 shows that there are equivalent sentences on the page about Donald Trump in Tamil and English, and the phrase alignment for an example sentence is shown in Table TABREF4 . Table TABREF2 shows that there are at least tens of thousands of bilingual articles on Wikipedia which could potentially have at least as many parallel sentences that could be mined to address the scarcity of parallel sentences as indicated in column 2 which shows the number of sentence-pairs in the largest available bilingual corpora for xx-en. As shown by BIBREF1 ( BIBREF1 ), the illustrated data sparsity can be addressed by extending the scarce parallel sentence-pairs with those automatically extracted from Wikipedia and thereby improving the performance of statistical machine translation systems. In this paper, we will propose a neural approach to parallel sentence extraction and compare the BLEU scores of machine translation systems with and without the use of the extracted sentence pairs to justify the effectiveness of this method. Compared to previous approaches which require specialized meta-data from document structure or significant amount of hand-engineered features, the neural model for extracting parallel sentences is learned end-to-end using only a small bootstrap set of parallel sentence pairs.