| # Cross_Lingual_Intent_Classifier | |
| ## Overview | |
| This model, **Cross_Lingual_Intent_Classifier**, is a state-of-the-art Natural Language Processing (NLP) model designed for classifying user intents across multiple languages. It is trained on a massive multilingual dataset encompassing high-resource languages (English, Spanish, French, German) and medium-resource languages (Italian, Portuguese). The core capability of this model is zero-shot or few-shot transfer of classification knowledge across languages, making it highly valuable for global conversational AI and virtual assistant applications. | |
| ## Model Architecture | |
| The model is based on the **XLM-RoBERTa (XLM-R)** architecture, specifically **XLMRobertaForSequenceClassification**. | |
| * **Base Model:** XLM-R large, pre-trained on 2.5TB of filtered CommonCrawl data in 100 languages. | |
| * **Head:** A classification head (a simple linear layer) is added on top of the pooled output of the last hidden state (CLS token). | |
| * **Training:** Fine-tuned on a mixed-language intent classification corpus using a standard cross-entropy loss function. The multilingual pre-training allows the model to map sentences from different languages into a shared, semantically rich representation space, enabling cross-lingual generalization. | |
| * **Intents:** Currently supports 6 core conversational intents: `Book_Flight`, `Get_Weather`, `Find_POI`, `Set_Reminder`, `Control_Device`, and `General_Query`. | |
| ## Intended Use | |
| This model is intended for the following use cases: | |
| * **Multilingual Chatbots:** Powering virtual assistants that need to understand user intent regardless of the input language, without requiring a separate model for each language. | |
| * **Zero-Shot Intent Transfer:** Using the model in a new language (not seen during fine-tuning) with reasonable performance due to its multilingual pre-training. | |
| * **Cross-Lingual Evaluation:** Benchmarking cross-lingual understanding capabilities in various NLP research projects. | |
| * **Data Labeling:** Automated classification of large volumes of multilingual customer service queries or voice commands. | |
| ## Limitations | |
| * **Low-Resource Languages:** While cross-lingual, performance degrades significantly for very low-resource or highly divergent languages not well-represented in the XLM-R pre-training corpus (e.g., certain African or indigenous languages). | |
| * **Domain Shift:** The model's performance may decrease if the intents or the conversational domain are highly specialized and differ greatly from the general-purpose intents it was trained on. | |
| * **Length Constraint:** Like most Transformer models, input sequences are typically capped at 512 tokens. Very long, multi-sentence utterances will be truncated. | |
| * **Dialect and Code-Switching:** The model handles standard, clean text better than heavily dialectal language or instances of complex code-switching (mixing two languages in one sentence). |