Inducing Relational Knowledge from BERT (1911.12753v1)

Abstract: One of the most remarkable properties of word embeddings is the fact that they capture certain types of semantic and syntactic relationships. Recently, pre-trained LLMs such as BERT have achieved groundbreaking results across a wide range of Natural Language Processing tasks. However, it is unclear to what extent such models capture relational knowledge beyond what is already captured by standard word embeddings. To explore this question, we propose a methodology for distilling relational knowledge from a pre-trained LLM. Starting from a few seed instances of a given relation, we first use a large text corpus to find sentences that are likely to express this relation. We then use a subset of these extracted sentences as templates. Finally, we fine-tune a LLM to predict whether a given word pair is likely to be an instance of some relation, when given an instantiated template for that relation as input.

Inducing Relational Knowledge from BERT

The paper "Inducing Relational Knowledge from BERT" by Bouraoui, Camacho-Collados, and Schockaert presents an explorative approach to distilling relational knowledge from pre-trained LLMs, specifically BERT. The core objective is to determine whether BERT captures relational knowledge superior to traditional static word embeddings, such as those generated by Skip-gram and GloVe.

Methodology

This research tackles the problem of relation induction, aiming to predict new instances of a relation from a handful of example pairs. The authors introduce a novel method that starts by identifying sentences within a large corpus that express relations for known word pairs. Following this, the sentences are refined to extract templates indicative of the target relation. These templates are used to instantiate new word pairs and assess their relational validity. The final step involves fine-tuning BERT to classify whether given pairs and the instantiated templates denote a valid relational instance.

Numerical Findings and Comparisons

The authors discern the effectiveness of their method using standard benchmark datasets—Google analogy, BATS, and DiffVec—comprising various relation categories, including morphological, lexical, and commonsense. Their findings reveal contrasting results when compared to traditional methods using word vectors for relation induction, namely the SVM and Trans baselines. Notably, their approach using BERT exhibits superior performance in capturing semantic relations and commonsense knowledge compared to these baselines, especially in the Google and DiffVec datasets where BERT’s handling of family and commonsense relations like causality showcase substantial enhancement.

Implications and Future Directions

The paper highlights that with BERT, relational knowledge can be automatically distilled in a manner that bypasses the hand-crafting of templates, marking a significant stride in automated relational reasoning. The results underscore an enriched modeling capacity for semantic and commonsense relations, thereby suggesting the potential for BERT to improve applications in areas demanding intricate relationship understanding, such as knowledge graph expansion and commonsense reasoning tasks.

However, the paper delineates limitations for BERT concerning morphological relations encapsulated within word embeddings, a challenge attributed to the implicit nature of these relations in text corpora. Future research could explore more extensive datasets or hybrid models that combine the merits of neural LLMs and word embeddings, thus potentially paving the way for enhanced morphological knowledge induction.

The implications of this paper are substantial, asserting that while LLMs like BERT hold untapped potential in relational reasoning, the complexities involved necessitate refined methodologies for harnessing their full capabilities. This work also prompts further inquiry into the broader scope of relational comprehension across current and forthcoming LLMs, possibly revamping relational induction tasks in NLP.