T rans A dv: A Translation-based Adversarial Learning Framework for Zero-Resource Cross-Lingual Named Entity Recognition (2022)

Abstract: Zero-Resource Cross-Lingual Named Entity Recognition aims at training an NER model of the target language using only labeled source language data and unlabeled target language data. Existing methods are mainly divided into three categories: model transfer based, data transfer based and knowledge transfer based. Each method has its own disadvantages, and combining more than one of them often leads to better performance. However, the performance of data transfer based methods is often limited by inevitable noise in the translation process. To handle the problem, we propose a framework named TransAdv to mitigate lexical and syntactic errors of word-by-word translated data, better utilizing the data by multi-level adversarial learning and multi-model knowledge distillation. Extensive experiments are conducted over 6 target languages with English as the source language, and the results show that TransAdv achieves competitive performance to the state-of-the-art models.

## introduction named entity recognition (ner) is a fundamental task that aims to locate named entities in a given sentence and assign them to predefined types, i.e., person, location, organization, etc. in recent years, neural ner models have achieved remarkable performance on this task with a large amount of labeled data. however, many low-resource languages do not have enough data for supervised learning. therefore, transferring labeled data or trained models from high-resource to low-resource languages is gaining increasing attention. in this paper, we concentrate on zero-resource cross-lingual ner where no labeled data in the target language is available. existing methods fall into three main categories: i) model transfer based methods @xcite , which train a source model on the labeled source * corresponding author. each kind of method has its drawbacks, @xcite is the first to unify the three kinds of methods with great success. however, the noise in the translation process significantly limits its performance. there are two common translation strategies for cross-lingual ner tasks: i) sentence translation followed by entity alignment, where the propagation of entity alignment errors is inevitable; ii) directly word-by-word translation @xcite , where the generated sentence is noisy in terms of word order. to better utilize the translated data, we propose a translation-based adversarial learning framework named transadv for zero-resource cross-lingual ner, and the overview architecture is shown in figure 1 . the contributions of our work can be summarized as follows: ## conclusion in this paper, we propose a framework named tranadv for zero-resource cross-lingual ner, which mitigates lexical and syntactic errors of word-by-word translated data and better utilizes it through multi-level adversarial learning and multi-model knowledge distillation. we evaluate transadv over 6 target languages with english as the source language. experimental results show that transadv achieves competitive performance to the state-of-the-art models.