In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Ex Input:
Beyond understanding what is being discussed, human communication requires an awareness of what someone is feeling. One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill that is trivial for humans. Research in this area is made difficult by the paucity of suitable publicly available datasets both for emotion and dialogues. This work proposes a new task for empathetic dialogue generation and EmpatheticDialogues, a dataset of 25k conversations grounded in emotional situations to facilitate training and evaluating dialogue systems. Our experiments indicate that dialogue models that use our dataset are perceived to be more empathetic by human evaluators, while improving on other metrics as well (e.g. perceived relevance of responses, BLEU scores), compared to models merely trained on large-scale Internet conversation data. We also present empirical comparisons of several ways to improve the performance of a given model by leveraging existing models or datasets without requiring lengthy re-training of the full model.

Ex Output:
We improve existing dialogue systems for responding to people sharing personal stories, incorporating emotion prediction representations and also release a new benchmark and dataset of empathetic dialogues.


Ex Input:
Using variational Bayes neural networks, we develop an algorithm capable of accumulating knowledge into a prior from multiple different tasks. This results in a rich prior capable of few-shot learning on new tasks. The posterior can go beyond the mean field approximation and yields good uncertainty on the performed experiments. Analysis on toy tasks show that it can learn from significantly different tasks while finding similarities among them. Experiments on Mini-Imagenet reach state of the art with 74.5% accuracy on 5 shot learning. Finally, we provide two new benchmarks, each showing a failure mode of existing meta learning algorithms such as MAML and prototypical Networks.

Ex Output:
A scalable method for learning an expressive prior over neural networks across multiple tasks.


Ex Input:
We propose a novel generative adversarial network for visual attributes manipulation (ManiGAN), which is able to semantically modify the visual attributes of given images using natural language descriptions. The key to our method is to design a novel co-attention module to combine text and image information rather than simply concatenating two features along the channel direction. Also, a detail correction module is proposed to rectify mismatched attributes of the synthetic image, and to reconstruct text-unrelated contents. Finally, we propose a new metric for evaluating manipulation results, in terms of both the generation of text-related attributes and the reconstruction of text-unrelated contents. Extensive experiments on benchmark datasets demonstrate the advantages of our proposed method, regarding the effectiveness of image manipulation and the capability of generating high-quality results.

Ex Output:

We propose a novel method to manipulate given images using natural language descriptions.