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. We call them textual relational problems. A popular model for textual relational problems is to embed sentences into fixed size vectors and use composition functions (e.g. difference or concatenation) of those vectors as features for the prediction. Meanwhile, 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 work, we show that textual relational models implicitly use 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.
This paper presents a view of sentence-level prediction tasks as statistical relation learning problems. In particular the paper argues that composition functions used in recent SRL techniques developed for entity-to-entity relationship detection can be applied to sentence-level relation prediction tasks. 
Suppose there is a prediction training task defined over pairs of sentences (x1, x2). This task requires some function 'f' that composes the sentence representations h1 and h2 into a single representation which is then used to 
make the relation prediction i.e., we have a model g(f(h1, h2)) that is used to predict some relation between R(x1, x2).  This paper aims to show that with a better 'f' we can hope for a better result in transfer tasks (in addition to doing better on the training task). 
The paper argues that this setting, at a high level, is similar to the composition function used in entity-entity relation prediction. There have been many such methods in the recent past (e.g., TransE, ComplEx, RESCAL). This paper asks whether these composition functions can work well for sentence-level tasks.
The paper then presents experiments which compare the performance of different composition functions against a basic composition function used in InferSent.
Strengths of the paper:
1. I like the main question of what can we learn from SRL. This seeks to bridge some independent research threads.
2. The evaluation considers a range of composition functions used in SRL and applies them to the sentence tasks. 
3. Points out that some of the composition functions used in existing models are not particularly strong.
Issues:
I like the starting point for this paper very much and agree that the existing composition functions for sentence relations are rather weak. However, I am struggling to see if there is (i) a convincing conceptual argument for why SRL view of compositions is necessarily the answer for sentence level tasks, or (ii) a convincing empirical case for the same.  Some details on these points:
1) The parallels between entity-entity relations and sentence-sentence relations seems a bit of a stretch to me. There is always some level of abstraction at which two problems might look similar, which can be advantageous for repurposing solutions. However, in this case I think the SRL view of the world hides the complexities in sentence-sentence relation tasks (e.g. aligning relevant pieces of information, requiring more complex composition functions to derive meaning etc.). 
2) I am not sure what knowledge we are getting from an SRL view of the problem that is not already known already to the communities that work on sentence embedding. The minimum requirements laid out can be met easily by existing methods for sentence representations. For instance that we need to allow for asymmetric relations (entailment order) is very well known. As the authors themselves point out there are solutions for this problem.  
3) The empirical results don't appear convincing. The average gain for any particular method over InferSent is 0.3 in macro average. There is no single SRL based composition method that works consistently clear gains across most tasks. 
Here are some suggestions that I think will improve the paper (or at least help me buy the motivation): 
1. One question that might be useful to make a conceptual argument is how much work should be done in 'f' and should it change for the different type of target tasks.
If the idea is to transfer h for single sentence target task, then a powerful 'f' can render h1 and h2 to be simple enough, such that bulk of the work in extracting task related information might be done by 'f' itself. Therefore, transferred h may not be as powerful as it could have been with a less powerful 'f'.
If the idea is to transfer f(h1, h2) for sentence-pair target tasks, then a powerful 'f' might be a good thing. 
2) Another useful discussion would be to discuss why more powerful alignment based sentence representations are not being considered at least for comparison purposes. 
The paper wants to go from a simple 'f' (i.e. concat(h1,h2), h1-h2) to some other choices for 'f' that are known functions from SRL. 
There are several sentence-level representation functions such as ESIM [Chen et al., 2016] which uses a combined representation of premise and hypothesis sentences using soft alignment to specifically address the issues in comparing sentences. A similar representation is computed in BiDAF [Seo et al., 2017] in the context of matching question representation with sentence representations. 
To summarize, I really like the basic starting point for the paper and would love to see a more compelling presentation of the conceptual argument and a stronger empirical comparison.