A Two-Agent Game for Zero-shot Relation Triplet Extraction (2024)

Abstract: Relation triplet extraction is a fundamental task in natural language processing that aims to identify semantic relationships between entities in text. It is particularly challenging in the zero-shot setting, i.e., zero-shot relation triplet extraction (ZeroRTE), where the relation sets between training and test are disjoint. Existing methods deal with this task by integrating relations into prompts, which may lack sufficient understanding of the unseen relations. To address these limitations, this paper presents a novel Two-Agent Game (TAG) approach to deliberate and debate the semantics of unseen relations. TAG consists of two agents, a generator and an extractor. They iteratively interact in three key steps: attempting, criticizing, and rectifying. This enables the agents to fully debate and understand the unseen relations. Experimental results demonstrate consistent improvement over ALBERT-Large, BART, andGPT3.5, without incurring additional inference costs in all cases. Remarkably, our method outperforms strong baselines by a significant margin, achieving an impressive 6%-16% increase in F1 scores, particularly when dealingwith FewRel with five unseen relations.

## introduction relation triplet extraction (rte; @xcite is a pivotal task in information extraction @xcite , which aims to extract the relation triplets in the form of (head entity, tail entity, relation label) within a given sentence. rte boosts a broad spectrum of downstream applications across domains, such as machine reading comprehension @xcite and machine translation @xcite . existing methods (zheng et @xcite rely on large amounts of labeled data, limiting the scalability and applicability of these methods. to reduce the overreliance on labeled data, chia et al. (2022) introduce a challenging task, zero-shot relation triplet extraction (zerorte), where relation sets during the training and test stages are disjoint. for example, in fig. 1 , training samples may belong to the seen relation set {sibling, performer}, while test samples may belong to the unseen relation set {mother, writer}. generalizing knowledge from the training set to the test set is critical. existing methods deal with zerorte by formulating it into prompts to leverage the power of language models. the main idea is to fine-tune the model on seen relations from the training dataset. next, the model adapts to unseen relations by integrating the semantics of those unseen relations into the prompt templates @xcite @xcite . recent research based on large language models (llms) removes the fine-tuning stage and integrates the semantics of relations via chain-of-thought fewshot prompting @xcite or multi-turn question answering @xcite . despite achieving favorable results, simply integrating relations into prompts is superficial and may lead to an insufficient understanding of the un-seen relations. for example, consider the two relations, "located on terrain feature" and "contains administrative territorial entity". both deal with location but a subtle difference exists between the two relations. merely merging them into the prompt without distinguishing their nuances may cause confusion between similar relations, ultimately resulting in a misunderstanding of the relations. to tackle the above issues, we construct a twoagent game (tag) to facilitate a process where both agents can engage in deliberation and debate to grasp the semantics of unseen relations. tag aims to comprehensively understand the nuances of unseen relations to improve the performance in the subsequent test phase. specifically, tag consists of two agents: an extractor responsible for extracting relation triplets from sentences and a generator for generating sentences and triplets based on the given relation. they deliberate and debate through three key steps: attempting, criticizing, and rectifying. first, the generator attempts to understand the unseen relation by generating sentences and triplets associated with the relation. these initial attempts will serve as the target of debate in the later stages of the game. subsequently, the extractor criticizes the generator on the quality of the synthetic data via evaluating the semantic matching degree between sentences and triplets, with higher log-likelihood values indicating better quality. finally, we use the results of attempting as data and the results of criticizing as rewards to rectify both agents through reinforcement learning. in this way, both agents can learn the preference between synthetic data and tend to produce highquality data. through this deliberation and debate process, two agents gradually develop a comprehensive understanding of unseen relations. experiments demonstrate that tag achieves consistent improvement across different model scales from albert @xcite with 18m parameters to large language models like gpt-3.5. our contributions are three-fold: (1) we introduce a novel two-agent game (tag) method for ze-rorte that facilitates deliberation and debate on unseen relations. this approach enables the generator and extractor agents to enhance their understanding of these relations collaboratively. (2) tag is a versatile approach that can be applied to different backbone models, i.e., albert, bart, and gpt-3.5. moreover, during deployment, the generator agent can be omitted without incurring additional computation and storage costs for the original ex-tractor agent. (3) experimental results demonstrate that tag consistently improves the performance across different backbone models. this highlights the effectiveness and scalability of our approach. ## preliminaries before delving into the details, we introduce the task definitions of rte and zerorte. and then introduce the definitions of the two agents in our approach, the generator and extractor. to make the notations consistent throughout the paper, we define the important ones in ## methodology the above section illustrates how the generator and extractor function. our challenge is to make the two agents work collaboratively and improve the overall extraction performance. to achive this, we propose a two-agent game and collaborate through the following three key steps: (1) attempting: the generator attempts to express its understanding of the unseen relation by generating sentences and triplets for the relation; (2) criticizing: the extractor criticizes the generator on its synthetic data; (3) rectifying: both agents rectify on their extraction task and generation task individually. the cycle of attempting, criticizing, and rectifying goes iteratively to refine the two agents' abilities. the overall procedure is described in fig. 2 . ## experiments to validate the effectiveness of tag, we conduct experiments on both small and large language models to address the following questions: (1) can tag be applied to different extractor model structures? (2) is tag effective at different scales of model parameters? ## conclusion in this paper, we propose tag, a two-agent game for zerorte, by introducing a new generator agent to communicate with the original extractor agent. through iterative processes of attempting, criticizing, and rectifying, the generator and extractor agents engage in a deliberative and collaborative exploration of the semantics of unseen relations, facilitating a comprehensive understanding of these relations. experimental results demonstrate that tag consistently enhances performance across different model architectures and scales without incurring additional inference costs. remarkably, our method outperforms strong baselines by a significant margin, achieving an impressive 6%-16% increase in f1 scores, particularly when dealing with fewrel with five unseen relations. we believe that tag is a promising approach for zerorte and holds potential for applications in other natural language processing tasks.