Adversarial Learning for Multi-Lingual Entity Linking (2024)

Abstract: Entity linking aims to identify mentions from the text and link them to a knowledge base. Further, Multi-lingual Entity Linking (MEL) is a more challenging task, where the language-specific mentions need to be linked to a multi-lingual knowledge base. To tackle the MEL task, we propose a novel model that employs the merit of adversarial learning and few-shot learning to generalize the learning ability across languages. Specifically, we first randomly select a fraction of language-agnostic unlabeled data as the language signal to construct the language discriminator. Based on it, we devise a simple and effective adversarial learning framework with two characteristic branches, including an entity classifier and a language discriminator with adversarial training. Experimental results on two benchmark datasets indicate the excellent performance in few-shot learning and the effectiveness of the proposed adversarial learning framework.

## introduction entity linking (el), a process of disambiguating entity mentions with a target knowledge base (kb), is one of the tasks in information retrieval @xcite and real applications involving information extraction @xcite and question answering @xcite , etc. many state-of-the-art studies generally pay attention to english kb and do not put enough energy into the low-resource and challenging languages, such as persian. in addition, the vast majority of low-resource languages are only provided with a limited annotated text, even without labeled data. therefore, the crosslingual entity linking (xel) task was proposed for several pairs of source text and kb languages @xcite @xcite , where mentions expressed in a language are linked to a kb delivered in another. test ...... donde los viernes cada tarde, como ... friday is the fifth and final day of the working week .... ce jeune homme est atteint d'un autisme, le syndrome ... autism is associated with a combination of genetic and .... however, xel restricted the scope of el to some extent since this popular method generally relies on the hypothesis of one single pivotal kb language as well as one limited kb. subsequently, multi-lingual entity linking (mel) has gained attention as the generalization of xel and some datasets @xcite @xcite have been collected for it. compared to tac-kbp 2014, tac-kbp 2015 @xcite was broadened from monolingual to trilingual coverage in three languages. recently, mewsli-9 @xcite was introduced as a large dataset featuring all entities to numerous cross-lingual systems with almost 300,000 mentions through 9 kinds of languages. given a text and entity mentions, there are two primary steps for multi-lingual entity linking: (1) candidate generation, possible entities are engendered for the mention, and (2) entity ranking, a score between the representation of mention and a candidate entity is computed. in this work, we con-sider the multi-lingual entity linking task illustrated in figure 1 that mainly takes entity ranking into consideration, and adopts a language-adversarial training approach to improve the performance. first, a multi-lingual pre-trained transformer model xlm-roberta (xlm-r) @xcite which builds robust representations of text in a wide range of languages, is utilized to build a single representation of mention including surrounding context and name of mention, and entity with description. the abundant source languages are leveraged to compute the similarity between mention and entity. second, we design a dedicated and simple adversarial learning approach to construct a language discriminator, which cleverly selects a small part from the test data (excluded during testing) and effectively generalizes to unseen languages for better robustness. in addition, previous studies @xcite contended that an adversarial training network could be regarded as a way that minimizes the jensen-shannon (js) divergence between two distributions, in our case the feature distributions of the source language and target language. for the discontinuities of js divergence, wasserstein distance was proposed to enhance the stability of hyperparameter selection. furthermore, a gradient penalty is introduced in our adversarial training approach to optimize the discriminator loss that hopes to enlarge the difference between source and target language as much as possible. the main contributions of our work are summarized as follows: ## conclusion in this paper, we propose a novel model that applies adversarial learning and few-shot learning method to better generalize the learning ability across languages for the multi-lingual entity linking task. to be more exact, a fraction of language-agnostic unlabeled data are selected randomly as the language signal to build the language discriminator. moreover, we design a simple and effective adversarial learning framework with two branches of an entity classifier and a language discriminator. experimental results on two benchmark datasets empirically illustrate that the proposed adversarial learning framework is significantly effective.