Machine Translation System for Indian Language: Survey

Parul, .; Garg, Kamal Deep

doi:10.1109/pdgc56933.2022.10053247

Cited by 2 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Parul and Garg (2022) provides a survey of different approaches to Machine Translation (MT) for Indian languages, including Rule-based Machine Translation (RBMT), Corpus-based Machine Translation (CBMT), and Neural Machine Translation (NMT). Researcher (Parul and Garg, 2022) highlights the initial slow progress in MT research and the subsequent popularity of NMT. The paper emphasizes that while there has been significant research on MT for top-level languages, there is a scarcity of research for low-level languages spoken by fewer people.…”

Section: Related Workmentioning

confidence: 99%

IACS-LRILT: Machine Translation for Low-Resource Indic Languages

Suman,

Mandal,

Pal

et al. 2023

Proceedings of the Eighth Conference on Machine Translation

View full text Add to dashboard Cite

Even though, machine translation has seen huge improvements in the the last decade, translation quality for Indic languages is still underwhelming, which is attributed to the small amount of parallel data available. In this paper, we present our approach to mitigate the issue of the low amount of parallel training data availability for Indic languages, especially for the language pair English-Manipuri and Assamese-English. Our primary submission for the Manipuri-to-English translation task provided the best scoring system for this language direction. We describe about the systems we built in detail and our findings in the process.

show abstract

Section: Related Workmentioning

confidence: 99%

IACS-LRILT: Machine Translation for Low-Resource Indic Languages

Suman,

Mandal,

Pal

et al. 2023

Proceedings of the Eighth Conference on Machine Translation

View full text Add to dashboard Cite

show abstract

“…Our methodology belongs to the class of guided mutational fuzz testing approaches [17,38]. Briefly, the idea is to start from a failing test, that gets repeatedly transformed through the random application of some predefined mutation operators.…”

Section: Overview Of Waf-a-molementioning

confidence: 99%

WAF-A-MoLE

Demetrio

Valenza

Costa

et al. 2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

View full text Add to dashboard Cite

Web Application Firewalls are widely used in production environments to mitigate security threats like SQL injections. Many industrial products rely on signature-based techniques, but machine learning approaches are becoming more and more popular. The main goal of an adversary is to craft semantically malicious payloads to bypass the syntactic analysis performed by a WAF.In this paper, we present WAF-A-MoLE, a tool that models the presence of an adversary. This tool leverages on a set of mutation operators that alter the syntax of a payload without affecting the original semantics. We evaluate the performance of the tool against existing WAFs, that we trained using our publicly available SQL query dataset. We show that WAF-A-MoLE bypasses all the considered machine learning based WAFs. CCS CONCEPTS• Security and privacy → Web application security.

show abstract

Machine Translation System for Indian Language: Survey

Cited by 2 publications

References 8 publications

IACS-LRILT: Machine Translation for Low-Resource Indic Languages

IACS-LRILT: Machine Translation for Low-Resource Indic Languages

WAF-A-MoLE

Contact Info

Product

Resources

About