Zero-Shot Information Extraction as a Unified Text-to-Triple Translation (2109.11171v1)

Abstract: We cast a suite of information extraction tasks into a text-to-triple translation framework. Instead of solving each task relying on task-specific datasets and models, we formalize the task as a translation between task-specific input text and output triples. By taking the task-specific input, we enable a task-agnostic translation by leveraging the latent knowledge that a pre-trained LLM has about the task. We further demonstrate that a simple pre-training task of predicting which relational information corresponds to which input text is an effective way to produce task-specific outputs. This enables the zero-shot transfer of our framework to downstream tasks. We study the zero-shot performance of this framework on open information extraction (OIE2016, NYT, WEB, PENN), relation classification (FewRel and TACRED), and factual probe (Google-RE and T-REx). The model transfers non-trivially to most tasks and is often competitive with a fully supervised method without the need for any task-specific training. For instance, we significantly outperform the F1 score of the supervised open information extraction without needing to use its training set.

Overview of Zero-Shot Information Extraction as a Unified Text-to-Triple Translation Framework

The paper "Zero-Shot Information Extraction as a Unified Text-to-Triple Translation" presents a novel approach to various information extraction tasks by introducing a unified framework known as DeepEx. The central premise of this work is to cast information extraction problems into a text-to-triple translation framework, thereby enabling zero-shot transfer capabilities. This methodology leverages pre-trained LLMs (LMs) to convert task-specific inputs into structured output triples without requiring task-specific datasets or additional training.

Methodology

The authors propose treating any information extraction task as a "text-to-triple" problem, where the goal is to translate input text into output triples that carry the required structured information. This formulation allows them to bypass the need for task-specific models and data, which is significant given the often sparse availability of labeled data in information extraction domains.

1. Input and Output Design: The framework uses a simple input/output design strategy where noun phrases (NPs) are identified in the input text, which are then translated into structured triples in the output. For example, "Fisher is a graduate of the London Opera Centre" can be extracted as the triple (Fisher; is a graduate of; London Opera Centre).
2. Zero-Shot Translation Process: This involves two main steps:
   • Generation: The pre-trained LLMs generate candidate triples based on the attention mechanisms that capture relational information within the input text. This step utilizes beam search to efficiently explore possible sequences of relational triples.
   • Ranking: A contrastive learning model ranks these candidate triples, selecting the most pertinent ones related to the input sentence. This model is pre-trained on a large, task-agnostic relational dataset (T-REx).
3. Decoding: The triples obtained are directly used to make task predictions, making the system intuitive and reducing the need for complex decoding strategies.

Evaluation and Results

The framework is evaluated across a variety of tasks, including open information extraction, relation classification, and factual probes. The performance is particularly notable in open information extraction, where DeepEx outperforms several fully supervised systems by a significant margin in F1 score and precision-recall AUC. This demonstrates the framework's ability to effectively leverage latent knowledge encoded within pre-trained LMs, achieving state-of-the-art results in a zero-shot setting.

1. Open Information Extraction: Across datasets like OIE2016, WEB, NYT, and PENN, DeepEx provides an average increase of 37.5 points in F1 score over state-of-the-art supervised systems.
2. Relation Classification: Although traditionally considered a supervised task, DeepEx achieves competitive results in FewRel and TACRED while being trained in a zero-shot configuration.
3. Factual Probe: The framework also shows strong performance on T-REx, demonstrating its utility in extracting factual knowledge from text.

Implications and Future Directions

The implications of this paper are significant for the field of natural language processing. By demonstrating that task-agnostic frameworks can achieve competitive or superior performance in traditionally supervised tasks, this work opens the door to more efficient and scalable information extraction methods. The methodology offers a path toward more adaptable AI systems capable of learning across tasks without the overhead of task-specific training.

Future research might explore improving the ranking model by employing larger LLMs or integrating additional context from globally pre-trained datasets. Enhancements in input encoding strategies or more sophisticated attention mechanisms could also amplify the model's zero-shot capabilities.

In summary, this paper presents a compelling approach to unified information extraction, challenging conventional paradigms in the field and setting a foundation for further exploration into zero-shot learning methods in NLP.