SenseBERT: Driving Some Sense into BERT (1908.05646v2)

Abstract: The ability to learn from large unlabeled corpora has allowed neural LLMs to advance the frontier in natural language understanding. However, existing self-supervision techniques operate at the word form level, which serves as a surrogate for the underlying semantic content. This paper proposes a method to employ weak-supervision directly at the word sense level. Our model, named SenseBERT, is pre-trained to predict not only the masked words but also their WordNet supersenses. Accordingly, we attain a lexical-semantic level LLM, without the use of human annotation. SenseBERT achieves significantly improved lexical understanding, as we demonstrate by experimenting on SemEval Word Sense Disambiguation, and by attaining a state of the art result on the Word in Context task.

• The paper introduces SenseBERT, which integrates WordNet supersenses with BERT using a joint training objective to reduce lexical ambiguity.
• It employs a weakly supervised semantic pre-training task with an expanded vocabulary to capture nuanced word meanings.
• Empirical results demonstrate state-of-the-art performance on SemEval supersense disambiguation and WiC tasks, highlighting enhanced semantic comprehension.

An Expert Analysis of SenseBERT: A Semantic Enhancement of BERT

The paper introduces SenseBERT, a novel adaptation of BERT designed to enhance the lexical-semantic understanding in LLMs by incorporating word-sense information from WordNet. The primary motivation is to address the ambiguity inherent in word forms that can have multiple meanings depending on context, thereby pushing beyond the surface-level masking used in traditional BERT implementations.

Methodological Insights

SenseBERT achieves semantic enhancement through a weakly supervised learning approach that integrates WordNet supersenses into the pre-training phase. This method involves:

• Semantic Pre-training Task: Unlike BERT, which focuses on word-form prediction, SenseBERT integrates a pre-training task that predicts the supersense of a masked word. Supersenses are broad semantic categories assigned to word senses, as defined by WordNet, which help mitigate the ambiguity found in fine-grained word-sense annotations.
• Joint Training Mechanism: The architecture of SenseBERT involves a joint training objective. It combines traditional word prediction with this new sense prediction task, using the transformer model’s input and output mappings, thereby facilitating learning without the necessity for manually annotated data.
• Expanded Vocabulary and Masking Strategies: The authors experiment with an expanded 60,000-token vocabulary to better manage out-of-vocabulary words, and prioritize masking single-supersense words to strengthen the semantic signal during training.

Empirical Results

The effectiveness of SenseBERT is quantitatively validated through its performance on two tasks directly dependent on semantic comprehension:

1. SemEval Supersense Disambiguation: On this task, which substitutes precise sense annotations with WordNet supersenses, SenseBERT not only surpassed vanilla BERT, yielding substantial improvements in frozen settings (no fine-tuning) but also improved fine-tuned performance.
2. Word in Context (WiC) Task: The WiC task requires discerning whether a word is used in the same sense across two contexts. SenseBERT’s state-of-the-art performance on this task underscores its enhanced capability in grasping word semantics compared to traditional models.

Moreover, the authors provide visualizations of the learned semantic space, demonstrating the clear categorization of supersenses by part-of-speech and semantic similarity, further validating SenseBERT's conceptual underpinning.

Implications and Future Directions

The implications of SenseBERT are both practical and theoretical. Practically, it offers a LLM with superior lexical-semantic comprehension suitable for applications requiring precise context understanding, such as translation and nuanced sentiment analysis. Theoretically, it suggests a pathway for integrating semantic knowledge into unsupervised pre-training frameworks.

The advancement opens avenues for future research in enhancing LLMs through other layers of semantic abstraction, potentially improving their ability to generalize and understand human language more deeply. Incorporating other structured knowledge bases and exploring multi-modal semantic integration offers exciting opportunities to expand on the work presented.

In conclusion, the development of SenseBERT marks a notable step forward in the field of natural language processing by demonstrating how subtle adjustments in pre-training strategies can yield significant improvements in semantic comprehension. The results achieved highlight the potential for similar approaches explicitly incorporating semantic knowledge into machine learning models.