This paper presents the findings of the second workshop on Challenges in Processing South Asian Languages (CHiPSAL 2026), held as part of LREC 2026. South Asia is one of the most linguistically diverse regions in the world, yet its languages remain severely underrepresented in language resources and technologies, particularly in the era of large language models (LLMs). The workshop brings together research addressing key challenges in this space, including data scarcity, morphological complexity, code-mixing, script diversity, and the lack of culturally grounded evaluation benchmarks. The workshop received 57 submissions, covering a wide range of languages, tasks, and modalities, including both widely spoken languages (e.g., Bengali, Hindi, Tamil, and Urdu) and extremely low-resource and endangered languages such as Burushaski, Limbu, and Nepal Bhasha (Newari). Several contributions introduce arguably first-of-their-kind resources and benchmarks for these languages, spanning both text and speech domains, and focusing on linguistically informed and culturally grounded data creation. In addition to the main track, the workshop hosted a shared task on Multimodal Hate and Sentiment Understanding in Low-Resource Memes for Nepali, attracting strong community participation. The results highlight the effectiveness of multimodal approaches while also revealing persistent challenges in modelling culturally nuanced and low-resource data. Across the accepted papers and shared task, key insights include the central role of high-quality data, the limitations of current multilingual models in low-resource settings, and the need for culturally aware and data-centric approaches. Overall, CHiPSAL 2026 demonstrates the growing momentum in South Asian language processing and highlights the importance of sustained, community-driven efforts to build inclusive and representative language technologies.

This paper describes our efforts for the development of a Proposition Bank for Urdu, an Indo-Aryan language. Our primary goal is the labeling of syntactic nodes in the existing Urdu dependency Treebank with specific argument labels. In essence, it involves annotation of predicate argument structures of both simple and complex predicates in the Treebank corpus. We describe the overall process of building the PropBank of Urdu. We discuss various statistics pertaining to the Urdu PropBank and the issues which the annotators encountered while developing the PropBank. We also discuss how these challenges were addressed to successfully expand the PropBank corpus. While reporting the Inter-annotator agreement between the two annotators, we show that the annotators share similar understanding of the annotation guidelines and of the linguistic phenomena present in the language. The present size of this Propbank is around 180,000 tokens which is double-propbanked by the two annotators for simple predicates. Another 100,000 tokens have been annotated for complex predicates of Urdu.

The paper describes a set of methods to automatically acquire the Urdu nouns (and its gender) on the basis of inflectional and contextual clues. The algorithms used are a blend of computer’s brute force on the corpus and careful design of distinguishing rules on the basis linguistic knowledge. As there are homograph inflections for Urdu nouns, adjectives and verbs, we compare potential inflectional forms with paradigms of inflections in strict order and gives best guess (of part of speech) for the word. We also worked on irregular plurals i.e. the plural forms that are borrowed from Arabic, Persian and English. Evaluation shows that not all the borrowed rules have same productivity in Urdu. The commonly used borrowed plural rules are shown in the result.

The paper presents a design schema and details of a new Urdu POS tagset. This tagset is designed due to challenges encountered in working with existing tagsets for Urdu. It uses tags that judiciously incorporate information about special morpho-syntactic categories found in Urdu. With respect to the overall naming schema and the basic divisions, the tagset draws on the Penn Treebank and a Common Tagset for Indian Languages. The resulting CLE Urdu POS Tagset consists of 12 major categories with subdivisions, resulting in 32 tags. The tagset has been used to tag 100k words of the CLE Urdu Digest Corpus, giving a tagging accuracy of 96.8%.

When dealing with languages of South Asia from an NLP perspective, a problem that repeatedly crops up is the treatment of complex predicates. This paper presents a first approach to the analysis of complex predicates (CPs) in the context of dependency bank development. The efforts originate in theoretical work on CPs done within Lexical-Functional Grammar (LFG), but are intended to provide a guideline for analyzing different types of CPs in an independent framework. Despite the fact that we focus on CPs in Hindi and Urdu, the design of the dependencies is kept general enough to account for CP constructions across languages.

In this paper, we present a system for transliterating the Arabic-based script of Urdu to a Roman transliteration scheme. The system is integrated into a larger system consisting of a morphology module, implemented via finite state technologies, and a computational LFG grammar of Urdu that was developed with the grammar development platform XLE (Crouch et al. 2008). Our long-term goal is to handle Hindi alongside Urdu; the two languages are very similar with respect to syntax and lexicon and hence, one grammar can be used to cover both languages. However, they are not similar concerning the script -- Hindi is written in Devanagari, while Urdu uses an Arabic-based script. By abstracting away to a common Roman transliteration scheme in the respective transliterators, our system can be enabled to handle both languages in parallel. In this paper, we discuss the pipeline architecture of the Urdu-Roman transliterator, mention several linguistic and orthographic issues and present the integration of the transliterator into the LFG parsing system.