Using External knowledge to Enhanced PLM for Semantic Matching (2505.06605v1)

Abstract: Modeling semantic relevance has always been a challenging and critical task in natural language processing. In recent years, with the emergence of massive amounts of annotated data, it has become feasible to train complex models, such as neural network-based reasoning models. These models have shown excellent performance in practical applications and have achieved the current state-ofthe-art performance. However, even with such large-scale annotated data, we still need to think: Can machines learn all the knowledge necessary to perform semantic relevance detection tasks based on this data alone? If not, how can neural network-based models incorporate external knowledge into themselves, and how can relevance detection models be constructed to make full use of external knowledge? In this paper, we use external knowledge to enhance the pre-trained semantic relevance discrimination model. Experimental results on 10 public datasets show that our method achieves consistent improvements in performance compared to the baseline model.

Utilizing External Knowledge to Enhance Pre-trained LLMs for Semantic Matching

This paper addresses a critical question in the advancement of NLP: Can pre-trained LLMs (PLMs) effectively distinguish semantic relevance by solely relying on annotated data, or should they incorporate external knowledge for improved performance? The authors propose a novel approach for integrating external knowledge into PLMs, aiming to enhance semantic relevance modeling, a foundational task in NLP involving evaluating the semantic relationships between textual inputs.

Methodology

The researchers primarily focus on constructing a prior knowledge matrix based on lexical relations and embedding this information into the PLM's attention mechanism. This integration unfolds in two stages:

1. External Knowledge Representation: They exploit lexical relations such as synonymy, antonymy, hypernymy, and hyponymy from resources like WordNet to form a knowledge vector. This vector encodes relations between word pairs, thus providing an informative guide for distinguishing fine-grained semantic nuances in sentences.
2. Knowledge-Infused Attention Mechanism: The external knowledge is integrated into the standard attention mechanism used in transformers. The knowledge vectors inform the co-attention scores, leading to a refined alignment of sentence pairs that considers external semantic relationships. An adaptive fusion module dynamically balances the original semantic signal with the knowledge-infused signal, employing gating mechanisms to selectively integrate this composite view into the model's output.

Experimental Results

The approach was evaluated on ten large-scale datasets, including six GLUE benchmark datasets: MRPC, QQP, STS-B, MNLI, RTE, and QNLI. Notably, the model demonstrated an average accuracy improvement of 2.31% over BERT-large, indicating the effectiveness of incorporating external knowledge. On QQP, the model achieved a 91.8% accuracy, surpassing previous enhancements like SyntaxBERT. These results exemplify the model's ability to improve semantic modeling owing to the adaptive knowledge integration mechanism.

Robustness Testing

In addition to performance improvements on benchmark datasets, the paper highlights the model's robustness when faced with textual perturbations. Using transformations like swapping antonyms and synonyms, the proposed model outperformed baselines like BERT and SyntaxBERT, showing better resilience to lexical variations and semantic contradictions. The adaptation through gates allows the model to selectively modify the reliance on external knowledge, ensuring performance consistency even under syntactic disruptions.

Implications and Future Directions

The authors effectively demonstrate that integrating external structured knowledge into PLMs can enhance semantic understanding capabilities beyond what is achievable through annotated data alone. This suggests a fruitful avenue for future research in integrating knowledge from various linguistic resources to further improve model generalization and robustness. As AI continues to evolve, leveraging multifaceted knowledge sources could lead to substantial advancements in automated reasoning systems, potentially bridging the gap between human-like understanding and machine interpretations of language.

The paper paves the way for exploring complex interactions between external knowledge and PLMs in diverse applications, such as conversational agents, document retrieval systems, and other semantically-driven NLP applications. Future research may involve exploring more sophisticated forms of external knowledge and different fusion strategies to further optimize semantic matching capabilities across a broader array of languages and contexts.