Token-Level Language Identification for IndicTriMix

IndicTriMix replaces document-level language classification with a sequence labeling architecture to identify language switches within Hindi, Gujarati, and Bengali code-mixed text. Traditional models aggregate global vocabulary statistics, which causes language-specific signals to vanish when a single utterance switches rapidly between languages. IndicTriMix shifts the identification to the token level, allowing the model to detect boundaries between language components within a single sequence.

The Mechanism of Sequence Labeling

IndicTriMix treats text as a sequence of discrete units rather than a single vector representing a document. The transformer’s self-attention layers calculate the dependency of each token on every other token in the input, producing a contextual embedding for every individual unit. These embeddings pass into a final linear projection layer, often followed by a softmax function. This layer maps the high-dimensional output of the transformer to a probability distribution over the language tag vocabulary. The model selects the tag with the highest probability for each position, effectively turning global linguistic context into a hard classification for every word.

Resolving Cross-Lingual Interference

Multilingual embeddings often fail on code-mixed data because they prioritize semantic alignment. These models are trained to map concepts with the same meaning in different languages to the same location in vector space. When you need to label the language of a specific token, this alignment works against you; the model has already been optimized to ignore the linguistic origin of a token in favor of its semantic role. By fine-tuning a model like MuRIL on token-labeled data, the classification head learns to distinguish the structural features of Hindi, Gujarati, and Bengali even when the underlying semantic vectors are highly similar. You are essentially forcing the model to re-weight these embeddings to recover the linguistic identity that the pre-training process suppressed.

Implementation Constraints

Consider a user typing a query that mixes Hindi and Gujarati: 'How do I reach the station in Hindi-Gujarati-Bengali?'. A standard model might see the entire string as a single unit and guess the language based on the most frequent vocabulary. IndicTriMix identifies the language tag for each token, allowing you to segment the input and route parts of the query to specific translation or processing engines tailored to each language. The primary challenge is that this approach requires data where every single token is manually or semi-automatically tagged. What remains uncertain is how well this architecture generalizes to scripts with significantly less lexical overlap, or to scripts that do not share the same morphological structure as the three languages in the current set.