A bottleneck problem in machine learning-based relationship extraction (RE) algorithms, and particularly of deep learning-based ones, is the availability of training data in the form of annotated corpora. For specific domains, such as biomedicine, the long time and high expertise required for the development of manually annotated corpora explain that most of the existing one are relatively small (i.e., hundreds of sentences). Beside, larger corpora focusing on general or domain-specific relationships (such as citizenship or drug-drug interactions) have been developed. In this paper, we study how large annotated corpora developed for alternative tasks may improve the performances on biomedicine related tasks, for which few annotated resources are available. We experiment two deep learning-based models to extract relationships from biomedical texts with high performance. The first one combine locally extracted features using a Convolutional Neural Network (CNN) model, while the second exploit the syntactic structure of sentences using a Recursive Neural Network (RNN) architecture. Our experiments show that, contrary to the former, the latter benefits from a cross-corpus learning strategy to improve the performance of relationship extraction tasks. Indeed our approach leads to the best published performances for two biomedical RE tasks, and to state-of-the-art results for two other biomedical RE tasks, for which few annotated resources are available (less than 400 manually annotated sentences). This may be particularly impactful in specialized domains in which training resources are scarce, because they would benefit from the training data of other domains for which large annotated corpora does exist.
SUMMARY.
The paper presents a cross-corpus approach for relation extraction from text.
The main idea is complementing small training data for relation extraction with training data with different relation types.
The model is also connected with multitask learning approaches where the encoder for the input is the same but the output layer is different for each task. In this work, the output/softmax layer is different for each data type, while the encoder is shared.
The authors tried two different sentence encoders (cnn-based and tree-lstm), and final results are calculated on the low resource dataset. 
Experimental results show that the tree-rnn encoder is able to capture valuable information from auxiliary data, while the cnn based does not.
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OVERALL JUDGMENT
The paper shows an interesting approach to data augmentation with data of different type for relation extraction.
I would have appreciated a section where the authors explain briefly what relation extraction is maybe with an example.
The paper is overall clear, although the experimental section has to be improved I believe.
From section 5.2 I am not able to understand the experimental setting the authors used, is it 10-fold CV? Did the authors tune the hyperparameters for each fold?
Are the results in table 3 obtained with tree-lstm? 
What kind of ensembling did the authors chose for those experiments?
The author overstates that their model outperforms the state-of-the-art models they compare to, but that is not true for the EU-ADR dataset where in 2 out of 3 relation types the proposed model performs on par with the state-of-the-art model.
Finally, the authors used only one auxiliary dataset at the time, it would be interesting to see whether using all the auxiliary dataset together would improve results even more.
I would suggest the author also to check and revise citations (CNN's are not Collobert et al. invention, the same thing for the maximum likelihood objective) and more in general to improve the reference on relation extraction literature.