Original scientific paper
https://doi.org/10.32985/ijeces.14.3.6
Reordering of Source Side for a Factored English to Manipuri SMT System
Indika Maibam
orcid.org/0000-0001-7695-9929
; Department of Computer Science Indira Gandhi National Tribal University, Kangpokpi, Imphal, Manipur, India
Bipul Syam Purkayastha
; Department of Computer Science, Assam University, Silchar, Assam, India
Abstract
Similar languages with massive parallel corpora are readily implemented by large-scale systems using either Statistical Machine Translation (SMT) or Neural Machine Translation (NMT). Translations involving low-resource language pairs with linguistic divergence have always been a challenge. We consider one such pair, English-Manipuri, which shows linguistic divergence and belongs to the low resource category. For such language pairs, SMT gets better acclamation than NMT. However, SMT’s more prominent phrase- based model uses groupings of surface word forms treated as phrases for translation. Therefore, without any linguistic knowledge, it fails to learn a proper mapping between the source and target language symbols. Our model adopts a factored model of SMT (FSMT3*) with a part-of-speech (POS) tag as a factor to incorporate linguistic information about the languages followed by hand-coded reordering. The reordering of source sentences makes them similar to the target language allowing better mapping between source and target symbols. The reordering also converts long-distance reordering problems to monotone reordering that SMT models can better handle, thereby reducing the load during decoding time. Additionally, we discover that adding a POS feature data enhances the system’s precision. Experimental results using automatic evaluation metrics show that our model improved over phrase-based and other factored models using the lexicalised Moses reordering options. Our FSMT3* model shows an increase in the automatic scores of translation result over the factored model with lexicalised phrase reordering (FSMT2) by an amount of 11.05% (Bilingual Evaluation Understudy), 5.46% (F1), 9.35% (Precision), and 2.56% (Recall), respectively.
Keywords
factored SMT; reordering; factoring; English; Manipuri; Automatic evaluation;
Hrčak ID:
296696
URI
Publication date:
28.3.2023.
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