  Various NLP problems -- such as the prediction of sentence similarity,
entailment, and discourse relations -- are all instances of the same general
task: the modeling of semantic relations between a pair of textual elements. A
popular model for such problems is to embed sentences into fixed size vectors,
and use composition functions (e.g. concatenation or sum) of those vectors as
features for the prediction. At the same time, composition of embeddings has
been a main focus within the field of Statistical Relational Learning (SRL)
whose goal is to predict relations between entities (typically from knowledge
base triples). In this article, we show that previous work on relation
prediction between texts implicitly uses compositions from baseline SRL models.
We show that such compositions are not expressive enough for several tasks
(e.g. natural language inference). We build on recent SRL models to address
textual relational problems, showing that they are more expressive, and can
alleviate issues from simpler compositions. The resulting models significantly
improve the state of the art in both transferable sentence representation
learning and relation prediction.
