In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[EX Q]: Recently, resources and tasks were proposed to go beyond state tracking in dialogue systems. An example is the frame tracking task, which requires recording multiple frames, one for each user goal set during the dialogue. This allows a user, for instance, to compare items corresponding to different goals. This paper proposes a model which takes as input the list of frames created so far during the dialogue, the current user utterance as well as the dialogue acts, slot types, and slot values associated with this utterance. The model then outputs the frame being referenced by each triple of dialogue act, slot type, and slot value. We show that on the recently published Frames dataset, this model significantly outperforms a previously proposed rule-based baseline. In addition, we propose an extensive analysis of the frame tracking task by dividing it into sub-tasks and assessing their difficulty with respect to our model.
[EX A]: A Frame Tracking Model for Memory-Enhanced Dialogue Systems

[EX Q]: Ensembling is a well-known technique in neural machine translation (NMT) to improve system performance. Instead of a single neural net, multiple neural nets with the same topology are trained separately, and the decoder generates predictions by averaging over the individual models. Ensembling often improves the quality of the generated translations drastically. However, it is not suitable for production systems because it is cumbersome and slow. This work aims to reduce the runtime to be on par with a single system without compromising the translation quality. First, we show that the ensemble can be unfolded into a single large neural network which imitates the output of the ensemble system. We show that unfolding can already improve the runtime in practice since more work can be done on the GPU. We proceed by describing a set of techniques to shrink the unfolded network by reducing the dimensionality of layers. On JapaneseEnglish we report that the resulting network has the size and decoding speed of a single NMT network but performs on the level of a 3-ensemble system.
[EX A]: Unfolding and Shrinking Neural Machine Translation Ensembles

[EX Q]: The challenge stated in the title can be divided into two main problems. The first problem is to reliably mimic the way that users interact with user interfaces. The second problem is to build an instructible agent, i.e. one that can be taught to execute tasks expressed as previously unseen natural language commands. This paper proposes a solution to the second problem, a system we call Helpa. End-users can teach Helpa arbitrary new tasks whose level of complexity is similar to the tasks available from today's most popular virtual assistants. Teaching Helpa does not involve any programming. Instead, users teach Helpa by providing just one example of a command paired with a demonstration of how to execute that command. Helpa does not rely on any pre-existing domain-specific knowledge. It is therefore completely domain-independent. Our usability study showed that end-users can teach Helpa many new tasks in less than a minute each, often much less.
[EX A]:
Towards A Virtual Assistant That Can Be Taught New Tasks In Any Domain By Its End-Users