Relation extraction has evolved from supervised relation extraction to zero-shot setting due to the continuous emergence of newly generated relations. Some pioneering works handle zero-shot relation extraction by reformulating it into proxy tasks, such as reading comprehension and textual entailment. Nonetheless, the divergence in proxy task formulations from relation extraction hinders the acquisition of informative semantic representations, leading to subpar performance. Therefore, in this paper, we take a data-driven view to handle zero-shot relation extraction under a three-step paradigm, including encoder training, relation clustering, and summarization. Specifically, to train a discriminative relational encoder, we propose a novel selective contrastive learning framework, namely, SCL , where selective importance scores are assigned to distinguish the importance of different negative contrastive instances. During testing, the prompt-based encoder is employed to map test samples into representation vectors, which are then clustered into several groups. Typical samples closest to the cluster centroid are selected for summarization to generate the predicted relation for all samples in the cluster. Moreover, we design a simple non-parametric threshold plugin to reduce false-positive errors in inference on unseen relation representations. Our experiments demonstrate that SCL outperforms the current state-of-the-art method by over 3% across all metrics.

Named Entity Recognition (NER) has so far evolved from the traditional flat NER to overlapped and discontinuous NER. They have mostly been solved separately, with only several exceptions that concurrently tackle three tasks with a single model. The current best-performing method formalizes the unified NER as word-word relation classification, which barely focuses on mention content learning and fails to detect entity mentions comprising a single word. In this paper, we propose a t wo-stage span-based framework with t emplates, namely, T 2 -NER , to resolve the unified NER task. The first stage is to extract entity spans, where flat and overlapped entities can be recognized. The second stage is to classify over all entity span pairs, where discontinuous entities can be recognized. Finally, multi-task learning is used to jointly train two stages. To improve the efficiency of span-based model, we design grouped templates and typed templates for two stages to realize batch computations. We also apply an adjacent packing strategy and a latter packing strategy to model discriminative boundary information and learn better span (pair) representation. Moreover, we introduce the syntax information to enhance our span representation. We perform extensive experiments on eight benchmark datasets for flat, overlapped, and discontinuous NER, where our model beats all the current competitive baselines, obtaining the best performance of unified NER.