This article describes the research project aimed at developing a Trilingual Machine translation System for English, Nepali, and Tamang language pairs. This project is expected to address knowledge and communication gaps caused by language barriers and mitigate disparities in the availability of information and knowledge sources in Tamang and Nepali.

This paper presents PCDT, a web-based platform for collecting sentence-aligned parallel corpora through a community-driven approach to support machine translation for under-resourced languages. The tool decentralizes the translation task to the target community and subsequently reviewed by language experts.

Despite being spoken by over 26 million people, Nepali lacks a standardized Information Retrieval (IR) benchmark, preventing systematic evaluation of retrieval models in this low-resource setting. While progress has been made in several Nepali NLP tasks, no publicly available test collection with queries and relevance judgments exists for ad-hoc retrieval task. We propose the creation of a first standardized Nepali IR benchmark supporting monolingual (Nepali queries \rightarrow Nepali documents), cross-lingual (English queries \rightarrow Nepali documents), and code-mixed (Nepali-English queries \rightarrow Nepali documents) retrieval. We argue that Nepali provides a compelling testbed for studying morphology-aware retrieval, code-mixed query processing, and multilingual embedding transfer.

Large Language Models (LLMs) have made significant advancements in Natural Language Processing (NLP) by excelling in various NLP tasks. This study specifically focuses on evaluating the performance of LLMs for Named Entity Recognition (NER) and Part-of-Speech (POS) tagging for a low-resource language, Nepali. The aim is to study the effectiveness of these models for languages with limited resources by conducting experiments involving various parameters and fine-tuning and evaluating two datasets namely, ILPRL and EBIQUITY. In this work, we have experimented with eight LLMs for Nepali NER and POS tagging. While some prior works utilized larger datasets than ours, our contribution lies in presenting a comprehensive analysis of multiple LLMs in a unified setting. The findings indicate that NepBERTa, trained solely in the Nepali language, demonstrated the highest performance with F1-scores of 0.76 and 0.90 in ILPRL dataset. Similarly, it achieved 0.79 and 0.97 in EBIQUITY dataset for NER and POS respectively. This study not only highlights the potential of LLMs in performing classification tasks for low-resource languages but also compares their performance with that of alternative approaches deployed for the tasks.

Conversational information retrieval has garnered considerable attention in recent years. A major challenge in conversational search is formulating the most effective query during the dialogue between the searcher and the conversational agent. Unlike traditional information retrieval systems that assume users can independently create queries, conversational settings allow agents to assist users in query formulation. This alleviates the burden on users by leveraging the multi-turn nature of the conversation to aid them in reaching their information goals. Conversational context plays a vital role in the query process. In this work, we focus on understanding and leveraging conversational context from two dimensions: conversational history and knowledge history. The goal is to identify and model the relevant portions from the search dialogue and the knowledge history and to use this within the search process to improve the overall performance of conversational information retrieval systems.