Entity-Relation Extraction as Multi-Turn Question Answering (1905.05529v4)

Abstract: In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and relations is transformed to the task of identifying answer spans from the context. This multi-turn QA formalization comes with several key advantages: firstly, the question query encodes important information for the entity/relation class we want to identify; secondly, QA provides a natural way of jointly modeling entity and relation; and thirdly, it allows us to exploit the well developed machine reading comprehension (MRC) models. Experiments on the ACE and the CoNLL04 corpora demonstrate that the proposed paradigm significantly outperforms previous best models. We are able to obtain the state-of-the-art results on all of the ACE04, ACE05 and CoNLL04 datasets, increasing the SOTA results on the three datasets to 49.4 (+1.0), 60.2 (+0.6) and 68.9 (+2.1), respectively. Additionally, we construct a newly developed dataset RESUME in Chinese, which requires multi-step reasoning to construct entity dependencies, as opposed to the single-step dependency extraction in the triplet exaction in previous datasets. The proposed multi-turn QA model also achieves the best performance on the RESUME dataset.

Entity-Relation Extraction as Multi-turn Question Answering

The paper "Entity-Relation Extraction as Multi-turn Question Answering" presents a novel approach in the domain of entity-relation extraction by reformulating the task as a multi-turn question answering (QA) problem. This method seeks to address the limitations of traditional entity-relation extraction frameworks by leveraging the advancements in machine reading comprehension (MRC) models.

Key Contributions

1. Task Reformulation: The authors propose transforming the entity-relation extraction task into a QA problem. Each entity type and relation type is characterized by a specific question template, allowing for entities and relations to be extracted by answering these questions, replete with crucial entity and relation class information.
2. Advantages of the Multi-turn QA Formulation:
   • Provides a method to capture hierarchical dependencies of tags, enabling progressive extraction of entities in subsequent turns.
   • Facilitates the joint modeling of entities and relations via QA, as certain question queries inherently define the intended relation class.
   • Utilizes well-established MRC frameworks for extracting text spans foreseen by the question context.
3. Empirical Evaluation and Results: The approach is empirically tested on ACE04, ACE05, and CoNLL04 datasets, surpassing prior state-of-the-art results with boosted performance metrics. Additionally, a newly constructed RESUME dataset requiring more complex multi-step reasoning further demonstrates the efficacy of the multi-turn QA approach.
4. Reinforcement Learning Integration: The authors extend the QA model with reinforcement learning, optimizing the explanation of hierarchical relations as actions defined over extracted answer spans. This integration is demonstrated to improve overall performance through policy optimization based on correct retrievals.

Experimental Setup and Results

The authors conducted experiments across multiple datasets, revealing the following notable results:

• On the ACE04 dataset, the proposed method achieved a relation F1 of 49.4, marking a significant improvement over previous models.
• For ACE05, a relation F1 of 60.2 was obtained, indicating enhanced relation extraction capabilities compared to other contemporary methods.
• The CoNLL04 dataset results presented an F1 of 68.9, setting a new benchmark in relation extraction tasks.
• The RESUME dataset, particularly complex due to its multi-layered entity dependencies, validated the proposed multi-turn QA paradigm's effectiveness in structurally extracting biographical information.

Implications and Future Directions

This paradigm shift in treating entity-relation extraction as a multi-turn QA task opens new avenues for integrating more complex reasoning into extraction models. The use of question templates to capture semantic and syntactic cues demonstrates a promising direction for entity-relation extraction in more intricate textual data.

Furthermore, future work can explore enhancing the robustness of these models through improved natural language question templates and potentially integrating more powerful contextual embeddings. The industry could leverage this approach to build more accurate and efficient systems for knowledge base construction from unstructured data.

In summary, this paper presents a substantial contribution to the field of information extraction, proving the viability of QA frameworks in extracting structured knowledge from nuanced textual contexts. Given the impressive results and the broad applicability of the proposed method, it is likely to inspire future research endeavors that continue to bridge the gap between traditional information extraction and natural language processing advancements.