Knowledge graphs (KGs) represent world's facts in structured forms. KG completion exploits the existing facts in a KG to discover new ones. Translation-based embedding model (TransE) is a prominent formulation to do KG completion. Despite the efficiency of TransE in memory and time, it is claimed that TransE suffers from several limitations in encoding relation patterns (such as symmetric, reflexive relations etc). To solve that, most of the attempts have circled around the revision of the TransE score function, resulting more complicated score functions (such as TransA/D/G/H/R etc). These attempts have totally disregarded effect of loss functions to this end. We show that loss functions are key factors in this regard and disregarding them results in conclusions which are inaccurate or even wrong. More concretely, we show that the claimed limitations are inaccurate, as the effect of the loss was ignored. In this regard, we pose theoretical investigations of the main limitations of TransE in the light of loss function. To the best of our knowledge, so far, this has not been comprehensively investigated. We show that by a proper selection of the loss function for training the TransE model, the main limitations are mitigated. That is achieved by setting upper-bound for the scores of positive samples, defining the region of truth (i.e. the region that a triple is considered positive by the model). Our theoretical proofs with experimental results fill the gap in understanding of the limitation of translation-based class of embedding models and confirm the importance of the selection of loss functions for training the models and on their performance.
The paper analyses the effect of different loss functions for TransE and argues that certain limitations of TransE can be mitigated by chosing more appropriate loss functions. Furthermore, the paper proposes TransComplEx -- an adaption of ideas from ComplEx/HolE  to TransE -- to mitigate issues that can not be overcome by a simply chosing a different loss.
Analyzing the behavior and short-comings of commonly-used models can be an important contribution to advance the state-of-the-art. This paper focuses on the performance of TransE, which is a popular representation learning approach for knowledge graph completion and as such fits well into ICLR.
Unfortunately, the current version of the paper seems to have issues regarding methodology and novelty.
Regarding the experimental evaluation: The paper compares the results of TransComplEx and the different loss functions to results that have previously been published in this field (directly, without retraining). However, it seems from Section 5 (Dataset), that this paper is using a modified dataset, as the TransE models are only trained on high-confidence triples. All prior work that I checked doesn't seem to do this, and hence the numbers are not comparable.
Even more serious: Following again Section 5 (Dataset), it seems that the paper imputes all missing triples in the training set for symmetric and transitive relations ("grounding"). Hence,  the models get to see _all_ true triples for these relation types and as such the models in this paper are trained on the test set.
Regarding novelty: The short-comings of TransE and improvements to the loss have been discussed quite extensively in prior work. Using complex representations in TransComplEx seems also a straightforward application of the insights of ComplEx/Hole. As such, the main novelty would lie in the experimental results which, unfortunately, seem problematic.