A common assumption in low-resource NLP is that cross-lingual transfer from a related language can substitute for target-language annotation when labelled data is scarce. We test this assumption for offensive content detection across five Indic languages by evaluating all twenty directed transfer pairs from a LLaMA3.1-8B model fine-tuned with Low-Rank Adaptation (LoRA) on the MACD benchmark. Only three of twenty pairs achieve tolerable transfer loss below 15%, all involving Malayalam as the source language. Telugu is the hardest transfer target (average loss 33.8%), while Malayalam is the most transferable source (average loss 16.8%). Confusion-matrix analysis reveals two distinct failure modes: Tamiland Kannada-trained models are conservative under-flaggers that miss 73–82% of offensive content with near-zero false alarms, while Malayalam-trained models are aggressive flaggers that miss far less (39%) but over-flag at 21%. These patterns do not follow typological structure: a Spearman correlation between URIEL typological similarity and transfer F1 yields ρ = −0.254 (p = 0.281), failing to conf irm the typological hypothesis. Our results indicate that cross-lingual shortcuts are unreliable for this task and that language-specific annotation cannot be avoided by appealing to linguistic family membership.

Natural language inference is a method of finding inferences in language texts. Understanding the meaning of a sentence and its inference is essential in many language processing applications. In this context, we consider the inference problem for a Dravidian language, Malayalam. Siamese networks train the text hypothesis pairs with word embeddings and language agnostic embeddings, and the results are evaluated against classification metrics for binary classification into entailment and contradiction classes. XLM-R embeddings based Siamese architecture using gated recurrent units and bidirectional long short term memory networks provide promising results for this classification problem.

Identifying offensive information from tweets is a vital language processing task. This task concentrated more on English and other foreign languages these days. In this shared task on Offensive Language Identification in Dravidian Languages, in the First Workshop of Speech and Language Technologies for Dravidian Languages in EACL 2021, the aim is to identify offensive content from code mixed Dravidian Languages Kannada, Malayalam, and Tamil. Our team used language agnostic BERT (Bidirectional Encoder Representation from Transformers) for sentence embedding and a Softmax classifier. The language-agnostic representation based classification helped obtain good performance for all the three languages, out of which results for the Malayalam language are good enough to obtain a third position among the participating teams.