Grammatical gender assignment is governed by interacting phonological, semantic, and morphological factors whose relative weight varies across languages. We present the first large-scale computational analysis of gender in Hindi using a novel dataset of over 6,000 noun paradigms annotated for morphological, phonological, semantic, and lexical strata features. We tested inflection class against competing predictors with interpretable classifiers and neural networks and found that it dominates, predicting gender at ≈97% accuracy, while other features contribute negligibly. The dependence is asymmetric: inflection class nearly determines gender, whereas gender is only a partial predictor of inflection class. Inflection class is itself recoverable in part from phonological and etymological cues without reference to gender, which suggests a resolution to the bootstrapping problem: form-based cues scaffold inflection class, which in turn scaffolds gender. These findings establish Hindi as an extreme case of morphologically aligned gender and expose a limitation of language-agnostic approaches: methods that treat semantic and surface-form cues as broadly predictive of gender may miss Hindi’s near-deterministic dependence on inflection class. Our dataset enables probing whether multilingual models encode this dependency and whether it survives cross-lingual transfer.

In virtual assistant (VA) systems it is important to reject or redirect user queries that fall outside the scope of the system. One of the most accurate approaches for out-of-scope (OOS) rejection is to combine it with the task of intent classification on in-scope queries, and to use methods based on the similarity of embeddings produced by transformer-based sentence encoders. Typically, such encoders are fine-tuned for the intent-classification task, using cross-entropy loss. Recent work has shown that while this produces suitable embeddings for the intent-classification task, it also tends to disperse in-scope embeddings over the full sentence embedding space. This causes the in-scope embeddings to potentially overlap with OOS embeddings, thereby making OOS rejection difficult. This is compounded when OOS data is unknown. To mitigate this issue our work proposes to regularize the cross-entropy loss with an in-scope embedding reconstruction loss learned using an auto-encoder. Our method achieves a 1-4% improvement in the area under the precision-recall curve for rejecting out-of-sample (OOS) instances, without compromising intent classification performance.