Sentence Encoding with Tree-constrained Relation Networks
This work addresses the challenge of effectively encoding sentence semantics for natural language processing applications, representing an incremental advancement over basic relation networks.
The paper tackled the problem of sentence representation by extending relation networks with syntax constraints and recurrence to capture higher-order relations, achieving reliable performance improvements in sentence classification, sentence pair classification, and machine translation tasks.
The meaning of a sentence is a function of the relations that hold between its words. We instantiate this relational view of semantics in a series of neural models based on variants of relation networks (RNs) which represent a set of objects (for us, words forming a sentence) in terms of representations of pairs of objects. We propose two extensions to the basic RN model for natural language. First, building on the intuition that not all word pairs are equally informative about the meaning of a sentence, we use constraints based on both supervised and unsupervised dependency syntax to control which relations influence the representation. Second, since higher-order relations are poorly captured by a sum of pairwise relations, we use a recurrent extension of RNs to propagate information so as to form representations of higher order relations. Experiments on sentence classification, sentence pair classification, and machine translation reveal that, while basic RNs are only modestly effective for sentence representation, recurrent RNs with latent syntax are a reliably powerful representational device.