CrossLingual

Crosslingual, also spelled cross-lingual, is an umbrella term used in linguistics and natural language processing to describe relations, transfer, or analyses that span multiple languages. It emphasizes shared representations, resources, or models that enable knowledge from one language to benefit others. In NLP, crosslingual approaches aim to transfer information across languages to improve tasks in languages with limited data.

In crosslingual learning, a common goal is to leverage data in a resource-rich source language to perform tasks in a target language with few or no labeled examples. Techniques include aligning crosslingual word embeddings, learning multilingual representations through joint pretraining, sharing subword vocabularies, and using parallel or comparable corpora to bridge linguistic differences. Multilingual pretrained transformers trained on many languages, such as mBERT and XLM-R, enable zero-shot or few-shot transfer across languages.

Applications of crosslingual methods are broad. They include machine translation, cross-lingual information retrieval, cross-lingual sentiment analysis, named entity recognition, part-of-speech tagging, question answering, and cross-lingual knowledge base alignment. These methods support tools and systems that operate across language boundaries, including multilingual search engines and multilingual virtual assistants.

Challenges persist, such as data scarcity for many languages, script and typological diversity, morphological complexity, domain mismatch, and biases in training data. Evaluation across languages can be inconsistent, complicating comparisons and progress. Related concepts include cross-lingual transfer, multilingual learning, and zero-shot learning. Core resources and benchmarks include multilingual datasets and models for tasks like XNLI, CoNLL NER, and universal dependencies for POS tagging.