IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages

India has a rich linguistic landscape with languages from 4 major language families spoken by over a billion people. 22 of these languages are listed in the Constitution of India (referred to as scheduled languages) are the focus of this work. Given the linguistic diversity, high-quality and accessible Machine Translation (MT) systems are essential in a country like India. Prior to this work, there was (i) no parallel training data spanning all the 22 languages, (ii) no robust benchmarks covering all these languages and containing content relevant to India, and (iii) no existing translation models which support all the 22 scheduled languages of India. In this work, we aim to address this gap by focusing on the missing pieces required for enabling wide, easy, and open access to good machine translation systems for all 22 scheduled Indian languages. We identify four key areas of improvement: curating and creating larger training datasets, creating diverse and high-quality benchmarks, training multilingual models, and releasing models with open access. Our first contribution is the release of the Bharat Parallel Corpus Collection (BPCC), the largest publicly available parallel corpora for Indic languages. BPCC contains a total of 230M bitext pairs, of which a total of 126M were newly added, including 644K manually translated sentence pairs created as part of this work. Our second contribution is the release of the first n-way parallel benchmark covering all 22 Indian languages, featuring diverse domains, Indian-origin content, and source-original test sets. Next, we present IndicTrans2, the first model to support all 22 languages, surpassing existing models on multiple existing and new benchmarks created as a part of this work. Lastly, to promote accessibility and collaboration, we release our models and associated data with permissive licenses at https://github.com/ai4bharat/IndicTrans2

Participatory analytics for transport decision-making

This thesis investigates the design and evaluation of several software platforms that facilitate participatory outcomes in transport decision-making across operational, local and strategic scales. These platforms act as instruments to explore aspects of the research question: "How can urban dashboards be contextualised, designed & evaluated in a way that is sensitive to the changing role of digital democracy, immersive technologies and the increasingly collaborative nature of planning?". The concept of participatory urban dashboards is introduced, followed by process of participatory analytics. This process involves bringing more people on board with both using the dashboard (e.g., together or collaboratively) and allowing a more general audience of citizens or stakeholders to make sense and validate what is displayed. The research is applied to the city of Sydney, Australia. Sydney is a growing, global city with a wide variety of transport infrastructure ambitions and a strong, open-data ecosystem. Sydney’s transport system underpins the case studies of the operational, local and strategic digital artefacts assessed in this research. Participatory analytics outcomes as a result of interacting with these digital prototypes are evaluated. This will, in turn, help direct research and real-life applications and development of these tools. Further, it aims to build on research gap calling for further understanding of context-specific, user-centric design and evaluation of these participatory analytics tools