RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction (2203.09101v1)

Abstract: Despite the importance of relation extraction in building and representing knowledge, less research is focused on generalizing to unseen relations types. We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) to encourage further research in low-resource relation extraction methods. Given an input sentence, each extracted triplet consists of the head entity, relation label, and tail entity where the relation label is not seen at the training stage. To solve ZeroRTE, we propose to synthesize relation examples by prompting LLMs to generate structured texts. Concretely, we unify LLM prompts and structured text approaches to design a structured prompt template for generating synthetic relation samples when conditioning on relation label prompts (RelationPrompt). To overcome the limitation for extracting multiple relation triplets in a sentence, we design a novel Triplet Search Decoding method. Experiments on FewRel and Wiki-ZSL datasets show the efficacy of RelationPrompt for the ZeroRTE task and zero-shot relation classification. Our code and data are available at github.com/declare-lab/RelationPrompt.

RelationPrompt: Leveraging Prompts for Zero-Shot Relation Triplet Extraction

The paper RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction addresses the challenge of extracting relation triplets in a zero-shot setting, a task formulated as Zero-Shot Relation Triplet Extraction (ZeroRTE). In this context, each extracted triplet comprises a head entity, relation label, and tail entity, with relation labels not seen during the training phase.

Methodology Overview

The primary methodology introduced is RelationPrompt, which synthesizes relation examples by prompting LLMs to generate structured text samples. This approach merges structured text strategies with LLM prompts to create a template for generating synthetic relation data. A significant innovation in this method is the Triplet Search Decoding technique, which overcomes challenges associated with extracting multiple relation triplets within a single sentence.

Core Components:

1. Relation Generator: Utilizes LLMs, specifically GPT-2, fine-tuned to condition on relation labels to generate synthetic examples representative of the desired relations.
2. Relation Extractor: Employs the BART model, fine-tuned on both real data and generated samples, to predict relation triplets from input sentences.
3. Triplet Search Decoding: A novel decoding technique that facilitates the extraction of multiple triplets by evaluating likelihood scores for potential triplet candidates, thereby enhancing flexibility and interpretability.

Experimental Evaluation

The methodology is evaluated on the FewRel and Wiki-ZSL datasets, split to form disjoint training and testing sets based on seen and unseen relation labels, with the results being averaged over multiple data folds.

Key Findings:

• Synthetic Data Quality: The RelationPrompt method demonstrates robust performance in generating high-quality synthetic data, which significantly improves ZeroRTE results when used in tandem with a capable relation extractor.
• ZeroRTE and ZeroRC Performance: The method outperforms previous state-of-the-art approaches like ZS-BERT on Zero-Shot Relation Classification (ZeroRC), showcasing its versatility and effectiveness across related but distinct tasks.
• Scalability and Generalization: RelationPrompt maintains performance levels even as the number of unseen relations increases, indicating strong generalization capabilities.

Implications and Future Directions

The work positions itself as a critical reference for advancing low-resource relation extraction methods by enabling zero-shot capabilities in relation triplet extraction. The implications are significant for fields requiring domain adaptation without extensive labeled datasets, such as open-domain question answering and knowledge graph construction.

Potential Areas for Future Work:

• Further refining the precision of generated samples to more accurately align head and tail entities with relation intent.
• Expanding the applicability of RelationPrompt to other structured prediction tasks, potentially broadening its impact across diverse areas in NLP involving complex relational data extraction.

In conclusion, the research presented advances our understanding and capabilities in zero-shot relation extraction through innovative use of prompt-based synthetic data generation, offering a promising path for handling unseen relations in natural language data.