Paper: Bio-RFX: Refining Biomedical Extraction via Advanced Relation Classification and Structural Constraints (ICLR-2024)

Reviewer comment (Novelty):
The approach could be generally applied to other domains, but the approach is presented as a model for the biomedical domain, and the scope is limited.

Author response:
This approach is designed to tackle specific issues in biomedical texts. The relation-first approach is based on the high prevalence of ambiguous terms in biomedical literature and serves as a hint for the entity type. The implementation of structural constraints is informed by domain-specific knowledge. For instance, the relation *activator* can only occur between a *chemical* and a *gene*. On the other hand, the relation *located_in* can occur between *sports_team* and *city*, *company* and *country*, *city* and *country*, among others, thus offering only a very weak constraint. We will explore the potential of applying our approach to other domains in our future research.

Reviewer comment (Experiments):
The comparison with existing state-of-the-art entity relation models is limited. The authors presented several approaches like OneRel and SPN in the related work section, and there are several SOTA models for entity relation tasks as follows, but the comparison is not performed.  
- Pere-Lluís Huguet Cabot and Roberto Navigli. 2021. REBEL: Relation Extraction By End-to-end Language generation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2370–2381, Punta Cana, Dominican Republic. Association for Computational Linguistics.  
- Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, and Dawn Song. 2022. DeepStruct: Pretraining of Language Models for Structure Prediction. In Findings of the Association for Computational Linguistics: ACL 2022, pages 803–823, Dublin, Ireland. Association for Computational Linguistics.  
- Deming Ye, Yankai Lin, Peng Li, and Maosong Sun. 2022. Packed Levitated Marker for Entity and Relation Extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4904–4917, Dublin, Ireland. Association for Computational Linguistics.

Author response:
Inspired by your constructive comments, we have updated the related work section in the revised version of the paper. We will further conduct comprehensive research into these SOTA models, extending them into biomedical fields and performing in-depth comparisons and discussions.

Reviewer comment (Evaluation):
Looking at the results in Table 1: What is the hypothesis for KECI performing better on RE task in DrugProt?

Author response:
Thank you for the valuable and detailed comments on the experimental results, which are very helpful for improving our paper. 

**Questions**

> Looking at the results in Table 1: What is the hypothesis for KECI performing better on RE task in DrugProt?

In addition to the brief explanation on the last line of page 7, we present a more detailed discussion here. The result in Table 1 can be attributed to two key factors: the integration of a background Knowledge Graph (KG) within KECI, and the large number of training samples in the DrugProt dataset.

KECI first constructs an initial span-graph from the text and then uses an entity linker to form a knowledge graph containing relevant background biomedical knowledge for the entity mentions in the text. To make the final predictions, KECI fuses the initial span graph and the knowledge graph into a more refined graph using an attention mechanism. The intricate network of interconnections among entities within the background KG equips KECI with a deeper understanding of sentence structures, thereby facilitating the formation of complex triplets. On the other hand, in order to lessen the demand for computational resources, we match all the possible subjects and objects to generate triplets, as stated in Section 3.2.3. 

Another contributing factor is the larger size of the DrugProt training set compared to other datasets, as detailed in Table 5 in Appendix C. This allows KECI to effectively strike a balance between utilizing training data and incorporating prior knowledge. However, when training data is limited, it will lead to overfitting to the background KG. For additional supporting experimental results and discussions, please refer to Section 4.4.