Joint Entity and Relation Extraction with Set Prediction Networks (2011.01675v2)

Abstract: The joint entity and relation extraction task aims to extract all relational triples from a sentence. In essence, the relational triples contained in a sentence are unordered. However, previous seq2seq based models require to convert the set of triples into a sequence in the training phase. To break this bottleneck, we treat joint entity and relation extraction as a direct set prediction problem, so that the extraction model can get rid of the burden of predicting the order of multiple triples. To solve this set prediction problem, we propose networks featured by transformers with non-autoregressive parallel decoding. Unlike autoregressive approaches that generate triples one by one in a certain order, the proposed networks directly output the final set of triples in one shot. Furthermore, we also design a set-based loss that forces unique predictions via bipartite matching. Compared with cross-entropy loss that highly penalizes small shifts in triple order, the proposed bipartite matching loss is invariant to any permutation of predictions; thus, it can provide the proposed networks with a more accurate training signal by ignoring triple order and focusing on relation types and entities. Experiments on two benchmark datasets show that our proposed model significantly outperforms current state-of-the-art methods. Training code and trained models will be available at http://github.com/DianboWork/SPN4RE.

Joint Entity and Relation Extraction with Set Prediction Networks

This paper presents an innovative approach to the task of joint entity and relation extraction by leveraging set prediction networks. The task itself is to extract relational triples from sentences, with each triple consisting of a subject, relation, and object. Traditionally, seq2seq models have been employed to handle this, but these models convert the inherently unordered set of triples into an ordered sequence, thereby imposing an unnecessary burden on the model to predict the order of triples. The approach discussed in this paper circumvents this limitation by reformulating the task as a set prediction problem.

Methodology

The authors employ a transformer-based architecture with non-autoregressive parallel decoding to handle the extraction task. This setup allows the model to generate the entire set of triples at once, thus eliminating the need to learn the sequencing of triples. The core components of the proposed model include:

1. Sentence Encoder: Utilizing the BERT model, the encoder captures the contextual representation of each token in the sentence. This representation serves as the foundation for both entity and relation extraction.
2. Non-Autoregressive Decoder: By employing a non-autoregressive approach, the decoder can predict the full set of triples concurrently. Unlike conventional autoregressive decoders that produce sequential output, this mechanism outputs predictions in parallel, which aligns naturally with the unordered nature of relational triples.
3. Bipartite Matching Loss: To evaluate the predictions, the authors introduce a set-based loss function that emphasizes accurate matching between predicted and ground truth triples. This is achieved via bipartite matching, ensuring that the model is invariant to permutations of predictions. This loss function focuses on the relational and entity types, rather than their order, providing a robust and precise training signal.

Experimental Results

Empirical evaluations on two benchmark datasets—NYT and WebNLG—demonstrate that the proposed model significantly outperforms state-of-the-art methods. Noteworthy improvements are reported in terms of precision, recall, and F1 score. The results underscore the model's proficiency in handling sentences with varying numbers of triples and its capability in managing overlapping structures, which are common in real-world data.

Implications and Future Work

The proposed approach—by treating joint entity and relation extraction as a set prediction problem—paves the way for more efficient and effective solutions to similar challenges in natural language processing. The model effectively mitigates the issues associated with order sensitivity in traditional seq2seq frameworks, thus enhancing both accuracy and computational efficiency.

Looking forward, the exploration of cost-sensitive learning techniques to address the imbalanced distribution of relation types in datasets could further boost performance. Moreover, the application of set prediction networks across other information extraction tasks and natural language understanding applications presents a promising avenue for future research.

This work emphasizes the evolving landscape of NLP tasks, where understanding the fundamental nature of data—such as the unordered characteristic of triples—allows for the development of more intuitive and powerful models. The implications of such a reformulation in entity and relation extraction can spur advancements in automatic knowledge graph construction and other downstream AI applications.