Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 1: Reject
review_content: This submission belongs to the area of multi-view modelling. In particular, the submission describes construction of multi-view language models that (i) can generate text simultaneously in multiple languages, (ii) can generate text in one or more languages conditioned on text from another language. This submission extends previously proposed KERMIT from two views to more than two views. I believe this paper could be of interest to multi-view modelling/learning community. 

Though the original KERMIT approach is very interesting and you application of it to more than two views is also interesting I find the presentation to be poor. In particular I find section 2 to be hard if not impossible to understand without referring to the original paper where the story, equations, nomenclature are much more clearly explained. Even though your extension from two views to multiple is simple I find reliance on a diagram to be a mistake as I find your description not to be very clear. Given that there are no equations to support the reader and that the original equations are not adequate I find it hard to understand Sections 2 and 3. The key experimental result in Table 1 is only briefly commented on despite featuring multiple models with different strength and weaknesses, multiple types of inference. If space is of concern I would suggest removing Figure 2 (or changing input from non-English to English and removing or removing another qualitative table).

review_title: Official Blind Review #1

final_score: rating: 3: Weak Reject
review_content: This paper proposes a multichannel generative language model (MGLM), which models the joint distribution p(channel_1, ..., channel_k) over k channels. MGLM can be used for both conditional generation (e.g., machine translation) and unconditional sampling. In the experiments, MGLM uses the Multi30k dataset where multiple high quality channels are available, in the form of multilingual translations.

I feel that this paper is not ready for publication at ICLR due to the following major issues:

* Missing important related work: This paper seems unaware of an important related work "Multi-Task Learning for Multiple Language Translation" by Dong et al, ACL 2015. In fact, Dong et al. investigated the problem of learning a machine translation model that can simultaneously translate sentences from one source language to multiple target languages. Although machine translation is just an example of MGLM, Dong et al. is highly relevant to the conditional generation with MGLM, needless to say that they share the same multi-language translation problem domain. Thus, this paper will be much stronger if comparison with important baseline methods is provided.

* Limited novelty: This paper extends Chan et al.'s KERMIT by applying its objective on tasks with more than 2 sequences, in order to learn the joint distribution p(channel_1, ..., channel_k) over k channel sequences. Most of the math in this paper can be found in the original Chan et al.'s paper. The extension to the multichannel case is incremental as it is hard to justify the challenge of such extensions.

Besides, as minor suggestions, it would help readers if more illustrations of Figure 1 (especially the inference part) can be provided.
review_title: Official Blind Review #3

final_score: rating: 3: Weak Reject
review_content: [Paper summary]
This work is an extension of KERMIT (Chan et al., 2019) to multiple languages and the proposed model is called “multichannel generative language models”. KERMIT is an extension of “Insertion Transformer” (Stern et. al, 2019), a non-autoregressive model that can jointly determine which word and which place the translated words should be inserted. KERMIT shares the encoder and decoder of insertion Transformer, and the source sentence and target sentence are concatenated to train a generative model (also, various loss functions are included). In this work, parallel sentences from more than two languages are concatenated together and fed into KERMIT. Each language is associated with a language embedding. This work demonstrates that a joint distribution p(x1, . . . , xk) over k channels/languages can be properly modeled through a single model. The authors carry out experiments on multi30k dataset.

[Pros] Some discoveries of this work are interesting, including: (1) It is possible to use a single model to translate a sentence into different languages in a non-autoregressive way. (2) The unconditional multilingual generation in Section 4.5 is interesting, especially, the generation order is determined by the model rather than left-to-right.

[Questions]
1.	The authors work on multi30k dataset, which is not a typical dataset for machine translation. 
(A)	The dataset and the corresponding information is at https://github.com/multi30k/dataset. The number of words in a sentence is smaller than 15, which is too short for a machine translation. Also, the pattern of sentences is relatively simple.
(B)	For real world application, I am not sure whether it is possible to collect a large amount of k-parallel data where $k>2$. Therefore, the application scenario is limited. What if we have a large amount of bilingual data instead of k-parallel data? How should we leverage the large amount of monolingual data?
2.	For novelty, this is an extension of KERMIT to a multilingual version, which limits the novelty of this wok.
3.	The best results on En->De in Table 1 are inconsistent. On tst16, bilingual en<->de is the best; on tst17, en<->{rest} is the best; on mscoco, any<->rest is the best. In Table 2, seems using bilingual data only is the best choice. This makes me confuse about how to use your proposed method. However, 

review_title: Official Blind Review #2

Meta-review: This paper presents a multi-view generative model which is applied to multilingual text generation. Although all reviewers find the overall approach is important and some results are interesting, the main concern is about the novelty. At the technical level, the proposed method is the extension of the original two-view KERMIT to multiviews, which I have to say incremental. At a higher level, multi-lingual language generation itself is not a very novel idea, and the contribution of the proposed method should be better positioned comparing to related studies. (for example, Dong et al, ACL 2015 as suggested by R#3). Also, some reviewers pointed out the problems in presentation and unconvincing experimental setup. I support the reviewers’ opinions and would like to recommend rejection this time.
I recommend authors to take in the reviewers’ comments and polish the work for the next chance. 
Decision: Reject