UDAAN: Machine Learning based Post-Editing tool for Document Translation
Ayush Maheshwari, Ajay Ravindran, Venkatapathy Subramanian, Ganesh, Ramakrishnan

TL;DR
UDAAN is an open-source post-editing tool that enhances document translation efficiency in Indic languages by integrating machine translation, lexicon suggestions, and user interaction logs, significantly reducing manual editing time.
Contribution
This paper presents UDAAN, a novel post-editing tool with domain-aware, lexicon-based MT and user interaction features, improving translation speed and quality for Indic languages.
Findings
Reduces translation time by approximately three times.
Supports multiple output formats including docs, LaTeX, and PDF.
Provides lexicon suggestions and source-target alignment visualization.
Abstract
We introduce UDAAN, an open-source post-editing tool that can reduce manual editing efforts to quickly produce publishable-standard documents in several Indic languages. UDAAN has an end-to-end Machine Translation (MT) plus post-editing pipeline wherein users can upload a document to obtain raw MT output. Further, users can edit the raw translations using our tool. UDAAN offers several advantages: a) Domain-aware, vocabulary-based lexical constrained MT. b) source-target and target-target lexicon suggestions for users. Replacements are based on the source and target texts lexicon alignment. c) Translation suggestions are based on logs created during user interaction. d) Source-target sentence alignment visualisation that reduces the cognitive load of users during editing. e) Translated outputs from our tool are available in multiple formats: docs, latex, and PDF. We also provide the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling
