Zero-Shot Entity Linking by Reading Entity Descriptions (1906.07348v1)

Abstract: We present the zero-shot entity linking task, where mentions must be linked to unseen entities without in-domain labeled data. The goal is to enable robust transfer to highly specialized domains, and so no metadata or alias tables are assumed. In this setting, entities are only identified by text descriptions, and models must rely strictly on language understanding to resolve the new entities. First, we show that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities. Second, we propose a simple and effective adaptive pre-training strategy, which we term domain-adaptive pre-training (DAP), to address the domain shift problem associated with linking unseen entities in a new domain. We present experiments on a new dataset that we construct for this task and show that DAP improves over strong pre-training baselines, including BERT. The data and code are available at https://github.com/lajanugen/zeshel.

• The paper presents a zero-shot entity linking approach that resolves entities solely from textual descriptions without in-domain labeled data.
• It employs BM25 for candidate generation and a deep-transformer model for candidate ranking, ensuring robust mention-context matching.
• The novel domain-adaptive pre-training strategy significantly boosts performance in unseen domains, yielding marked accuracy improvements.

An Overview of Zero-Shot Entity Linking by Reading Entity Descriptions

The paper presents a novel research problem: zero-shot entity linking without in-domain labeled data, relying only on textual descriptions to identify entities. This task is motivated by the need to enable entity linking to highly specialized domains where traditional resources such as alias tables or structured data are unavailable. The research explores how robust reading comprehension models and innovative pre-training methodologies can adapt to these conditions, challenging the generalization abilities of current models in the absence of previously observed entities.

Task Definition and Challenges

Entity linking traditionally thrives in environments where extensive labeled data and metadata about entities, such as alias tables and structured data, are available. In contrast, the zero-shot entity linking task assumes no such in-domain training data, requiring models to resolve entity mentions only through text understanding. This necessitates a robust linguistic comprehension to match unencountered mentions against dictionary entries composed solely of textual descriptions.

The paper constructs a new dataset featuring multiple domains (or "worlds") to evaluate the task, leveraging community-written encyclopedias from Wikia. This setting allows the examination of performance in unseen domains, stressing models under the dual challenges of linguistic nuances and the lack of domain-specific training examples.

Methodology

Model Architecture:

• Candidate Generation: The task first employs an information retrieval (IR) approach using BM25 to generate candidates, simulating the alias table functionality that is absent in this setup.
• Candidate Ranking: A deep-transformer based model is used, suitively adapted for reading comprehension tasks. The full-transformer model architecture encapsulates mention and context concatenated with entity descriptions, allowing comprehensive cross-attention. This architecture is paramount when handling unseen entities, as it facilitates a more holistic understanding of mention-context compatibility.

Domain-Adaptive Pre-training (DAP):

The paper introduces a novel pre-training strategy aimed at addressing domain transfer challenges. DAP comprises unsupervised pre-training on the target domain before task-specific fine-tuning using source-domain labeled data. This strategy aligns with three pre-existing approaches: task-adaptive, open-corpus pre-training, and mixed strategy combining both. DAP is shown to enhance performance when added as a pre-final stage before fine-tuning, focusing the model's capacity on encoding target-specific nuances.

Experimental Results

The empirical evaluation illustrates the efficacy of the zero-shot linking model augmented by the DAP approach:

• Baseline Performance: Reference models fall short without substantial pre-training, highlighting the inadequacy of using string-similarity-based or standard neural approaches.
• Domain Adaptation: The inclusion of DAP significantly boosts the model's capabilities to adapt to new test worlds, reflected in substantial accuracy improvements.
• Performance Analysis: Results show clear performance differentials across mention categories, underscoring the challenges posed by ambiguous or low-overlap mentions with entity titles.

Implications and Future Directions

The findings advocate for the advancement of natural language processing models capable of extrapolating knowledge to previously unseen contexts—an essential capability for specialized applications. Moreover, the absence of rich metadata compels models to advance in semantic reasoning purely from text. This approach could foster developments in various AI applications, including question answering, information retrieval, and general semantic understanding.

For future research, there is potential to integrate more refined candidates generation mechanisms and explore collective inference methods. Further, expanding on more generalized semantic representations for large-scale adaptations across variable domains remains a promising avenue. Such endeavors could break the barriers to entity linking in highly specialized or resource-scarce settings, unlocking wider applicability and utility of AI models.