Spoken language understanding (SLU) is a critical component in building dialogue systems. When building models for novel natural language domains, a major challenge is the lack of data in the new domains, no matter whether the data is annotated or not. Recognizing and annotating "intent" and "slot" of natural languages is a time-consuming process. Therefore, spoken language understanding in low resource domains remains a crucial problem to address. In this paper, we address this problem by proposing a transfer-learning method, whereby a SLU model is transferred to a novel but data-poor domain via a deep neural network framework. We also introduce meta-learning in our work to bridge the semantic relations between seen and unseen data, allowing new intents to be recognized and new slots to be filled with much lower new training effort. We show the performance improvement with extensive experimental results for spoken language understanding in low resource domains. We show that our method can also handle novel intent recognition and slot-filling tasks. Our methodology provides a feasible solution for alleviating data shortages in spoken language understanding.
Summary: The authors present a network which facilitates cross-domain
learning for SLU tasks where the the goal is to resolve intents and
slots given input utterances. At a high level, the authors argue that
by fine-tuning a pre-trained version of the network on a small set of
examples from a target-domain they can more effectively learn the
target domain than without transfer learning.
Feedback:
* An overall difficulty with the paper is that it is hard to
distinguish the authors' contributions from previous works. For
example, in Section 3.1, the authors take the model of Goyal et al. as
a starting point but explain only briefly one difference
(contatenating hidden layers). In Section 3.2 the contributions
becomes even harder to disentangle. For example, how does this section
relate to other word-embeddings papers cited in this section? Is the
proposed method a combination of previous works, and if not, what are
the core new ideas?
* Some sections are ad-hoc and should be justified/explained
better. For example, the objective, which ultimately determines the
trained model behaviour uses a product of experts formulation, yet the
authors do not discuss this. Similarly, the overarching message, that
by fine-tuning a suitable model initialisation using small amounts of
data from the target domain is fairly weak as the authors do not
detail exactly how the model is fine-tuned. Presumably, given only a
small number of examples, this fine-tuning runs the risk of
overfitting, unless some form of regularisation is applied, but this
is not discussed.
* Lastly, there are some curious dips in the plots (e.g., Figure 2 bottom left, Figure 3 top left, bottom left), which deserve more explanation. Additionally, the evaluation section could be improved if the scores were to show error-bars. 
Minor: All plots should be modified so they are readable in grey-scale.