Venue: ICLR 2018 Workshop

Reviews:

final_score: rating: 3: Clear rejection
review_content: This paper investigates several variations of the Siamese networks on zero-shot text classification.

According to the proposed three methods, although the trained model could be tested anywhere, it cannot succeed for most of the text classification tasks. For example, the class label description can be a sequence of words in many real-world tasks, but there is no mechanism in the proposed model to handle such class labels. 

Given the above counter example, it seems to me that the proposed model could only be guaranteed to work well on tasks, where the class labels are similar to SEO tags. In that sense, the problem is more like a recommender system (with out-of-vocabulary items) instead of zero-shot learning. While the proposed method is quite standard in the field of recommender systems.

As a result, I think the "train once, test anywhere" in the title is over claiming so I suggest the authors change the title. Also, I suggest the authors show some examples of the class labels in the testing tasks, in order to let the readers have some sense about how the task labels differ from SEO tags.
review_title: The setting looks like a recommendation problem, and the proposed approaches sounds standard in the field of recommender systems

final_score: rating: 3: Clear rejection
review_content: This paper proposes a text classification method for zero shot sentence or document classification. The paper is poorly written with lots of missing details. For example, the authors talk about category tree and did not clearly explain where this come from (are the tags already organized in a hierarchical structure)? How are relationships between classes and tags defined? Also, what are the embeddings for unseen tags?

review_title: review

final_score: rating: 4: Ok but not good enough - rejection
review_content: Note: I'm familiar with zero-shot learning for image classification/retrieval, yet not with text classification.

The main idea is to learn a classifier, from a "sentence" and a "label" and then predict the relatedness. At test time different "labels" are available than at train time. A zero-shot scenario thus. However, it is unclear [from the description] how the test-labels are transferred to the train-labels. From the description (page 3) it seems that the transfer is manually performed by selecting "relevant labels".

Major issues:
- The paper lacks any comparison to related methods (either visual or textual zero-shot methods, baseline results etc). 
- The paper is unclear in what it learns, relatedness (Fig 2/Sect 3) seems a binary task, what is than the zero-shot setting?
- Missing baselines (eg a retrieval baseline, based on the rank of the target label, given the sentence)

Minor:
- Figure 1 is not referenced to
- Notation in Figure 2 is different from description in section 3
review_title: Zero-shot text classification, yet with unclear transfer from unseen to seen topics

Meta-review: Based on the reviews, this paper has not been accepted for presentation at the ICLR workshop. However, the conversation and updates can continue to appear here on OpenReview.
Decision: Reject