Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 7: Good paper, accept
review_content: 
This paper presents a method for incorporating both lexicalized and delexicalized data for dialogue generation. This can be useful, for example if there is some overlapping information between a slot type and its value (for example, if a location of a pizza restaurant in a database is 'near X street', instead of 'X street', you want the dialogue system to learn to say 'the pizza is at X street' rather than 'the pizza is at near X street'). The proposed model seems to do this, and outperforms existing methods (Wen et al., 2015) by a decent margin on two datasets based on human evaluation. While the change is not ground-breaking, I think it's at an appropriate level for an ICLR workshop paper, and should be accepted.
review_title: Review

final_score: rating: 6: Marginally above acceptance threshold
review_content: In this paper, the authors extend Semantically Conditioned LSTMs [Wen et al, EMNLP`15] for dialogue generation from slot-value pairs. The main extensions are:
(a) They allow lexical values in addition to categorical values for slots. They model the lexical values by the average pooling of their word embeddings and concatenate them with the one-hot representation for slot-ids to form the encoder's input.
(b) They use pre-trained weights for the decoder LSTM based on an auto-encoder trained on sentences from the same domain.
 
Their experiments show that both lexical value modeling as well as transfer learning improve performance on all metrics on two different datasets although the improvements are not statistically significant most of the time. Example sentences produced by various models indicate that the proposed models are qualitatively better.

The paper lacks novelty -- it seems an incremental improvement to Semantically Conditioned LSTMs. 
 
Minor suggestion: For reasons of completeness, please show how the dialogue act vector (d_0) influences the decoder-LSTM. Although Wen et al is cited, it still makes sense to display it since it is an important component of the model.

review_title: an incremental improvement

Decision: Accept