Neural-based word embeddings using solely distributional information have
consistently produced useful meaning representations for downstream tasks.
However, existing approaches often result in representations that are hard to
interpret and control. Natural language definitions, on the other side, possess
a recursive, self-explanatory semantic structure that can support novel
representation learning paradigms able to preserve explicit conceptual
relations and constraints in the vector space.
This paper proposes a neuro-symbolic, multi-relational framework to learn
word embeddings exclusively from natural language definitions by jointly
mapping defined and defining terms along with their corresponding semantic
relations. By automatically extracting the relations from definitions corpora
and formalising the learning problem via a translational objective, we
specialise the framework in hyperbolic space to capture the hierarchical and
multi-resolution structure induced by the definitions. An extensive empirical
analysis demonstrates that the framework can help impose the desired structural
constraints while preserving the mapping required for controllable and
interpretable semantic navigation. Moreover, the experiments reveal the
superiority of the hyperbolic word embeddings over the euclidean counterparts
and demonstrate that the multi-relational framework can obtain competitive
results when compared to state-of-the-art neural approaches (including
Transformers), with the advantage of being significantly more efficient and
intrinsically interpretable.

本文提出了一种神经符号、多关系框架来仅通过自然语言定义学习单词嵌入，特别地，通过在超几何空间中学习，将维护向量空间中的显式概念关系和约束。实验结果表明，这种方法可以帮助强制执行期望的结构约束，同时保留可控和可解释的语义导航，而且超几何词嵌入优于欧几里得空间的词嵌入。