Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion (2021)

Abstract: Bootstrapping has become the mainstream method for entity set expansion. Conventional bootstrapping methods mostly define the expansion boundary using seed-based distance metrics, which heavily depend on the quality of selected seeds and are hard to be adjusted due to the extremely sparse supervision. In this paper, we propose BootstrapGAN, a new learning method for bootstrapping which jointly models the bootstrapping process and the boundary learning process in a GAN framework. Specifically, the expansion boundaries of different bootstrapping iterations are learned via different discriminator networks; the bootstrapping network is the generator to generate new positive entities, and the discriminator networks identify the expansion boundaries by trying to distinguish the generated entities from known positive entities. By iteratively performing the above adversarial learning, the generator and the discriminators can reinforce each other and be progressively refined along the whole bootstrapping process. Experiments show that BootstrapGAN achieves the new state-of-the-art entity set expansion performance.

## introduction bootstrapping is a fundamental technique for entity set expansion (ese). it starts from a few seed entities (e.g., {london, beijing, paris}) and iteratively extracts new entities in the target category (e.g., {berlin, moscow, tokyo}) to expand the entity set, where new entities are often evaluated by their context similarities to seeds (e.g., sharing the same context pattern-"* is an important city") @xcite @xcite . during the above process, it is core to decide whether the new entities belong to the target category (within the expansion boundary) or not (outside the expansion boundary) @xcite . however, it is challenging to determine the expansion boundaries during the whole bootstrapping process, since only several seeds are used as the supervision at the beginning. firstly, it is obviously not enough to define a good boundary using only several positive entities. for example, as shown in figure 1 , when only using several positive entities to learn distance-based boundaries, the boundaries are usually far from optimum, which in turn influences the quality of following bootstrapping iterations. therefore, it is critical to enhancing the boundary learning with more supervision signals or prior knowledge @xcite . secondly, bootstrapping is a dynamic process containing multiple iterations. therefore, the boundary needs to be synchronously adjusted with the bootstrapping model, i.e., a good boundary should precisely restrict the current bootstrapping model from expanding negative entities. currently, most bootstrapping methods define expansion boundary using seed-based distance metrics, i.e., determining whether an entity should be expanded by comparing it with seeds. for instance, @xcite ; @xcite ; @xcite define the bound-ary using pattern matching statistics or distributional similarities. unfortunately, these heuristic metrics heavily depend on the selected seeds, making the boundary biased and unreliable @xcite . although some studies extend them with extra constraints @xcite or manual participants @xcite , the requirement of expert knowledge makes them ad-hoc and inflexible. some studies try to learn the distance metrics @xcite ), but they still suffer from weak supervision. furthermore, because the bootstrapping model and the boundary are mostly learned separately, it is hard for these methods to synchronously adjust the boundary when the bootstrapping model updates. to address the boundary learning problem, we propose a new learning method for bootstrapping-bootstrapgan, which defines expansion boundaries via learnable discriminator networks, and jointly models the bootstrapping process and the boundary learning process in the generative adversarial networks (gans) framework @xcite : we conduct experiments over two datasets, and our bootstrapgan achieves the new state-of-theart performance for entity set expansion. ## progressive adversarial learning for bootstrapping in this section, we introduce our boundary learning method for bootstrapping models-bootstrapgan (see figure 2 ), which contains a generator-the bootstrapping network that performs the bootstrapping process, and a set of discriminators that determine the expansion boundaries for different bootstrapping iterations. the bootstrapping network and the discriminator networks are progressively and adversarially trained during the bootstrapping process. ## related work bootstrapping bootstrapping is a widely used technique for information extraction @xcite @xcite @xcite , and also benefits many other nlp tasks, like question answering @xcite , named entity translation @xcite , knowledge base population @xcite , etc. to address the expansion boundary problem, most early methods @xcite heuristically decide boundaries using patternmatching statistics, but often result in a rapid quality degrading, which is known as the semantic drifting @xcite . to reduce semantic drifting, some studies leverage external resources or constraints, e.g., mutual exclusive constraints @xcite @xcite , lexical and statistical features @xcite , lookahead feedbacks @xcite , manually defined patterns @xcite . however, those heuristic constraints are usually not flexible due to their requirement for expert efforts. in contrast, recent studies focus on learning the distance metrics to determine boundaries using weak supervision @xcite @xcite . for example, @xcite propose an end-toend bootstrapping network learned by multi-view learning, and extend it by self-supervised and supervised pre-training @xcite . however, these methods usually learn a loose boundary using sparse supervision. furthermore, these methods' boundary learning process and model learning process are usually separately performed and therefore fail to be adjusted synchronously. adversarial learning in nlp adversarial learning @xcite is widely applied in nlp. for example, in sequential generation tasks, gan is mainly used to alleviate the problem of lacking explicitly defined criteria @xcite @xcite . gan has also been used in weakly supervised information extraction to identify informative instances and filter out noises @xcite , which inspires our method. ## conclusion due to very sparse supervision and the dynamic nature, one fundamental challenge of bootstrapping is how to learn precise expansion boundaries. in this paper, we propose an effective learning method for bootstrapping-bootstrapgan, which defines expansion boundaries via learnable discriminator networks and jointly models the bootstrapping process and the boundary learning process in the gans framework. experimental results show that, by adversarially learning and progressively refining the bootstrapping network and the discriminator networks, our method achieves the new state-of-the-art performance. in the future, we plan to leverage extra knowledge (e.g., knowledge graph) to improve bootstrapping learning.