Paper:
Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and twits have been generated on the Internet, which is written not only in English but also in other languages such as Chinese, Japanese, French and so on. Not only SNS sites but also worldwide news agency such as Thomson Reuters News 1 provide news reported in more than 20 languages, reflecting the significance of the multilingual information. In this research, by taking advantage of multi-lingual text resources provided by the Thomson Reuters News, we developed a bidirectional LSTM based method to calculate cross-lingual semantic text similarity for long text and short text respectively. Thus, users could understand the situation comprehensively, by investigating similar and related cross-lingual articles, when there an important news comes in.

Review:
* PAPER SUMMARY *
This paper proposes a siamese net architecture to compare text in different languages. The proposed architecture builds upon siamese RNN by Mueller and Thyagarajan. The proposed approach is evaluated on cross lingual bitext retrieval.
* REVIEW SUMMARY * 
This paper is hard to read and need proof-reading by a person proficient in English. The experiments are extremely limited, on a toy task. No other baseline than (Mueller and Thyagarajan, 2016) is considered. The related work section lacks important references. It is hard to find positive points that would advocate for a presentation at ICLR.
* DETAILED REVIEW *
On related work, the authors need to consider related work on cross lingual retrieval, multilingual document representation:
Bai, Bing, et al. "Learning to rank with (a lot of) word features." Information retrieval 13.3 (2010): 291-314. (Section 4).
Schwenk, H., Tran, K., Firat, O., & Douze, M. Learning Joint Multilingual Sentence Representations with Neural Machine Translation, ACL Workshop on Representation Learning for NLP, 2017
Karl Moritz Hermann and Phil Blunsom.  Multilingual models for compositional distributed semantics. In ACL 2014. pages 58–68.
Hieu Pham, Minh-Thang Luong, and Christopher D. Manning. Learning distributed representations for multilingual text sequences. In Workshop
on Vector Space Modeling for NLP. 2015
Xinjie Zhou, Xiaojun Wan, and Jianguo Xiao. Cross-lingual sentiment classification with bilingual document representation learning. In ACL 2016
...
On evaluation, the authors need to learn about standard retrieval evaluation metrics such as precision at top 10, etc and use them. For instance, this book will be a good read.
Baeza-Yates, Ricardo, and Berthier Ribeiro-Neto. Modern information retrieval. Vol. 463. New York: ACM press, 1999.
On learning objective, the authors might want to read about learn-to-rank objectives for information retrieval, for instance, 
Liu, Tie-Yan. "Learning to rank for information retrieval." Foundations and Trends in Information Retrieval 3.3 (2009): 225-331.
Burges, Christopher JC. "From ranknet to lambdarank to lambdamart: An overview." Learning 11, no. 23-581 (2010): 81.
Chapelle, Olivier, and Yi Chang. "Yahoo! learning to rank challenge overview." Proceedings of the Learning to Rank Challenge. 2011.
Herbrich, Ralf, Thore Graepel, and Klaus Obermayer. "Large margin rank boundaries for ordinal regression." (2000).
On experimental setup, the authors want to consider a setup with more than 8k training documents. More importantly, ranking a document set of 1k documents is extremely small, toyish. For instance, (Schwenk et al 2017) search through 1.5 million sentences. (Bai, Bing, et al 2009) search through 140k documents. Since you mainly introduces 2 modifications with respect to (Mueller and Thyagarajan, 2016), i.e  (i) not sharing the parameters on both branch of the siamese and (ii) the fully connected net on top, I would suggest to measure the effect of each of them both on multilingual data and on the SICK dataset used in (Mueller and Thyagarajan, 2016).