Title: Representing Sentence Structure in a Tree Metric Space
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: tree metric space, tree representation learning, Transformer, tree analyses, tree metric, random trees

Abstract: This paper proposes building a sentence tree metric space through
representation learning of sentence structure. Our method 
represents every sentence tree structure as a vector, with the
Euclidean distance applied to construct the sentence tree metric. In
comparison with the previous, representative tree-metric methods of
the (tree edit distance) TED, tree kernels, and PQ-grams, our method  has
the best computational complexity, scaling to handle a million trees,
yet it performs well in predicting tree structure and learning
TED-like distances, even without TED for supervision. Our large-scale
sentence metric space analyses provide novel ways to study sentence
structures from recent language technology, by evaluating parsers and
tree-annotated corpora, and with tree structures acquired by recent
large language models (LLMs). These analyses also address the nature
of natural language trees not only within languages but in comparison
with random trees.

TL;DR: We propose a fast tree representation learning method that constructs a sentence metric space. The approach jointly predicts tree structure and class, offering benefits for evaluating LLMs and parsers, and analyzing sentences w.r.t. random trees.