SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features (2206.07023v2)

Abstract: Models based on large-pretrained LLMs, such as S(entence)BERT, provide effective and efficient sentence embeddings that show high correlation to human similarity ratings, but lack interpretability. On the other hand, graph metrics for graph-based meaning representations (e.g., Abstract Meaning Representation, AMR) can make explicit the semantic aspects in which two sentences are similar. However, such metrics tend to be slow, rely on parsers, and do not reach state-of-the-art performance when rating sentence similarity. In this work, we aim at the best of both worlds, by learning to induce $S$emantically $S$tructured $S$entence BERT embeddings (S$^3$BERT). Our S$^3$BERT embeddings are composed of explainable sub-embeddings that emphasize various semantic sentence features (e.g., semantic roles, negation, or quantification). We show how to i) learn a decomposition of the sentence embeddings into semantic features, through approximation of a suite of interpretable AMR graph metrics, and how to ii) preserve the overall power of the neural embeddings by controlling the decomposition learning process with a second objective that enforces consistency with the similarity ratings of an SBERT teacher model. In our experimental studies, we show that our approach offers interpretability -- while fully preserving the effectiveness and efficiency of the neural sentence embeddings.

Decomposing Sentence Embeddings with S$^3$BERT for Explainable Semantic Features

The paper "SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features" presents a novel approach to enhance the interpretability of sentence embeddings generated by SBERT—Sentence-BERT, a popular method for deriving efficient and effective sentence embeddings. The paper aims to address the challenge of rendering these embeddings more interpretable by integrating Abstract Meaning Representation (AMR) graph metrics to decompose sentence embeddings into meaningful and explainable sub-embeddings.

Objective and Methodology

The research introduces the concept of Semantically Structured Sentence BERT embeddings, termed S$^3$BERT. This approach leverages AMR graph metrics to decompose sentence embeddings into sub-embeddings that highlight explicit semantic features such as semantic roles, negation, and quantification. The objective is to amalgamate the interpretability of AMR graph metrics with the efficiency and performance of neural embeddings for tasks like semantic textual similarity (STS).

Two objectives are central to the creation of S$^3$BERT embeddings:

1. Semantic Decomposition Objective: It involves structuring the sentence embedding space into subspaces correlating with various AmR metrics, achieved through decomposition loss that aligns predictions from S$^3$BERT with scores from AMR metrics.
2. Consistency Objective: To prevent the catastrophic forgetting of SBERT's original capabilities, this objective maintains alignment between resultant S$^3$BERT embeddings and the semantic similarity outputs of a frozen SBERT model.

Experimental Evaluation

The authors conducted extensive experiments to evaluate whether S$^3$BERT could effectively partition the sentence embedding space into interpretable semantic features without sacrificing performance. The paper reports significant results indicating that compared to standard SBERT and other baselines, S$^3$BERT provides more interpretable embeddings and preserves, even slightly improving, the accuracy of semantic similarity tasks.

Key findings include:

• Improved explanation capacity of S$^3$BERT in tasks involving negation, named entities, and quantifier similarity.
• When tested on datasets such as the STS benchmark and SICK for semantic similarity evaluation, S$^3$BERT either matches or slightly surpasses SBERT's performance.
• Highlighting individual semantic aspects such as negation, quantification, and coreference offers a nuanced understanding of sentence similarity that aligns with human judgments more closely than traditional methods.

Implications and Future Directions

The research offers crucial insights into enhancing the interpretability of neural representations in NLP tasks. Importantly, the paper bridges robust AMR-derived explainability with the high performance of neural embeddings. This integration can potentially lead to more transparent AI systems that provide cogent justifications for their semantic similarity judgments, an essential feature for critical applications in AI where explainability is paramount.

Future developments in this line of research could explore:

• Extension of S$^3$BERT embeddings to broader NLP tasks beyond sentence similarity, potentially including sentiment analysis, paraphrase detection, and question answering, where nuanced semantic understanding is beneficial.
• Wider adoption of AMR and similar structured meaning representations to ground neural embedding spaces in interpretable semantic features.
• Advancements in AMR parsing to further refine the quality and precision of semantic decomposition.

By enhancing interpretability without compromising efficacy, S$^3$BERT has laid a foundation for future exploration of explainable artificial intelligence in semantic similarity and beyond. The approach exemplifies a promising step toward creating machine learning models that are not only accurate but also transparent and interpretable for end-user trust and understanding.