KI-Glossar
Das vollständige Wörterbuch der Künstlichen Intelligenz
Neural Machine Translation (NMT)
Machine translation approach using deep neural networks to model the correspondence between languages, forming the foundation of modern cross-lingual QA systems.
Multilingual Representations
Shared vector spaces where words and phrases from different languages are projected into the same semantic space, enabling knowledge transfer between languages.
Interlingual Alignment
Process of matching linguistic and semantic structures between different languages to facilitate information transfer in QA systems.
Language Projection
Technique consisting of translating only the question or documents into a common pivot language before applying a monolingual QA system.
Multilingual Tokenization
Process of segmenting text into basic units (tokens) taking into account the morphological and syntactic specificities of multiple languages simultaneously.
Cross-Lingual Fine-Tuning
Adaptation phase of a pre-trained multilingual model on task-specific data for QA in one or more target languages.
Language Shift
Phenomenon where model performance degrades when transitioning from a high-resource to a low-resource language in cross-lingual QA systems.
Multilingual Contextual Embeddings
Dynamic vector representations that capture the meaning of words based on their context, shared across multiple languages for better semantic understanding.
Back-Translation
Data augmentation method where texts are translated into a target language and then re-translated back to the source language to generate synthetic training data.
Language Weighting
Training data sampling strategy aimed at balancing the contribution of each language to avoid the domination of high-volume data languages.
Language Detection
Essential component of cross-lingual QA systems that automatically identifies the language of the input question to guide appropriate processing.
Cross-Lingual Normalization
Process of standardizing textual representations across languages to reduce linguistic variations and improve the robustness of QA systems.
Universal Language Models
Models designed to capture linguistic regularities common to all human languages, serving as the foundation for truly multilingual QA systems.
Code-Switching Adaptation
Ability of a QA system to process questions containing a mixture of multiple languages, a common phenomenon in natural multilingual contexts.
Cross-Lingual Evaluation
Specific methodologies for measuring the performance of QA systems across language pairs, including metrics like XNLI or MLQA.