@inproceedings{ahmad-etal-2026-dr,
    title = "{DR}-{RAG}: Addressing Retrieval Misalignment in Low-Resource {U}rdu Question Answering",
    author = "Ahmad, Saad  and
      Hammad, Muhammad  and
      Zeeshan, Muhammad  and
      Ullah, Faizad  and
      Karim, Asim",
    editor = "Sarveswaran, Kengatharaiyer  and
      Vaidya, Ashwini",
    booktitle = "Proceedings of the Second workshop on Challenges in Processing {S}outh {A}sian Languages ({CH}i{PSAL}2026)",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/cawl-year/2026.chipsal-1.6/",
    doi = "10.63317/4wwyss5zkwxs",
    pages = "49--58",
    abstract = "Retrieval-Augmented Generation performs well on English QA benchmarks, but degrades considerably in morphologically rich, low-resource languages. Urdu presents a particularly challenging case: heavy inflectional morphology, Nastaliq script inconsistencies, and limited training data produce a systematic mismatch between query representations and indexed document content that standard retrieval architectures cannot bridge. We propose DR-RAG (Dual-Representation Retrieval-Augmented Generation), which addresses this through dual indexing. Each document is represented as overlapping text chunks and as automatically generated question-answer pairs. Queries are first matched against the QA index, which aligns more reliably with natural query phrasing than declarative document chunks. When retrieval confidence falls below {\ensuremath{\tau}} = 0.80, the system falls back to chunk-based retrieval, maintaining coverage without sacrificing precision. Evaluated on Urdu UQA and English SQuAD 2.0, DR-RAG improves Urdu METEOR by 38{\texttimes}, ROUGE-1 by 140{\%}, and reduces generation latency by 43{\%}. LLM-as judge scores show higher faithfulness (3.03 vs 1.93) and overall quality (2.99 vs 2.21) over MultiVector. English performance remains competitive throughout. These results indicate that representation-level alignment between queries and indexed content, rather than increased model complexity, is the critical factor for reliable retrieval in underserved South Asian languages."
}