MULTI-TASK LEARNING FOR DOCUMENT RANKING AND QUERY SUGGESTION
We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search. It consists of two major components, a document ranker and a query recommender. Document ranker combines current query and session information and compares the combined representation with document representation to rank the documents. Query recommender tracks users' query reformulation sequence considering all previous in-session queries using a sequence to sequence approach. As both tasks are driven by the users' underlying search intent, we perform joint learning of these two components through session recurrence, which encodes search context and intent. Extensive comparisons against state-of-the-art document ranking and query suggestion algorithms are performed on the public AOL search log, and the promising results endorse the effectiveness of the joint learning framework.
The work is interesting and novel. The novelty lies not in the methods used (existing methods are used), but in the way these methods are combined to solve two problems (that so far have been treated separately in IR) simultaneously. The fitness of the proposed architecture and methodological choices to the task at hand is sufficiently argued. 
The experimental evaluation is not the strongest, in terms of datasets and evaluation measures. While I understand why the AOL dataset was used, the document ranking experiments should also include runs on any of the conventional TREC datasets of documents, queries and actual (not simulated) relevance assessments. Simulating document relevance from clicks is a good enough approximation, but why not also use datasets with real human relevance assessments, especially since so many of them exist and are so easy to access?
When evaluating ranking, MAP and NDCG are indeed two popular measures. But the choice of NDCG@1,3,10 seems a bit adhoc. Why not NDCG@5? Furthermore, as the aim seems to be to assess early precision, why not also report MRR? 
The paper reports that the M-NSRF query suggestion method outperforms all baselines. This is not true. Table 2 shoes that M-NSRF is best for BLEU-1/2, but not for BLEU-3/4. 
Three final points:
- Out of the contributions enumerated at the end of Section 1, only the novel model and the code & data release are contributions. The rigorous comparison to soa and its detailed analysis are the necessary evaluation parts of any empirical paper.  
- The conclusion states that this work provides useful intuitions about the advantages of multi-task learning involving deep neural networks for IR tasks. What are these? Where were they discussed? They should be outlined here, or referred to somehow.
- Although the writing is coherent, there are a couple of recurrent English language mistakes (e.g. missing articles). The paper should be proofread and corrected.