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 Input:
Speaker intent detection and semantic slot filling are two critical tasks in spoken language understanding (SLU) for dialogue systems. In this paper, we describe a recurrent neural network (RNN) model that jointly performs intent detection, slot filling, and language modeling. The neural network model keeps updating the intent prediction as word in the transcribed utterance arrives and uses it as contextual features in the joint model. Evaluation of the language model and online SLU model is made on the ATIS benchmarking data set. On language modeling task, our joint model achieves 11.8% relative reduction on perplexity comparing to the independent training language model. On SLU tasks, our joint model outperforms the independent task training model by 22.3% on intent detection error rate, with slight degradation on slot filling F1 score. The joint model also shows advantageous performance in the realistic ASR settings with noisy speech input.

Ex Output:
Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks


Ex Input:
While known algorithms for sensitivity analysis and parameter tuning in probabilistic networks have a running time that is exponential in the size of the network, the exact computational complexity of these problems has not been established as yet. In this paper we study several variants of the tuning problem and show that these problems are NP-complete in general. We further show that the problems remain NP-complete or PP-complete, for a number of restricted variants. These complexity results provide insight in whether or not recent achievements in sensitivity analysis and tuning can be extended to more general, practicable methods.

Ex Output:
The Computational Complexity of Sensitivity Analysis and Parameter Tuning


Ex Input:
Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in Ranjay Krishna Stanford University, Stanford, CA, USA E-mail: ranjaykrishna@cs.stanford.edu Yuke Zhu Stanford University, Stanford, CA, USA Oliver Groth Dresden University of Technology, Dresden, Germany Justin Johnson Stanford University, Stanford, CA, USA Kenji Hata Stanford University, Stanford, CA, USA Joshua Kravitz Stanford University, Stanford, CA, USA Stephanie Chen Stanford University, Stanford, CA, USA Yannis Kalantidis Yahoo Inc., San Francisco, CA, USA Li-Jia Li Snapchat Inc., Los Angeles, CA, USA David A. Shamma Yahoo Inc., San Francisco, CA, USA Michael S. Bernstein Stanford University, Stanford, CA, USA Li Fei-Fei Stanford University, Stanford, CA, USA an image. When asked “What vehicle is the person riding?”, computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) in order to answer correctly that “the person is riding a horse-drawn carriage.” In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 100K images where each image has an average of 21 objects, 18 attributes, and 18 pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. Together, these annotations represent the densest and largest dataset of image descriptions, objects, attributes, relationships, and question answers.

Ex Output:

Connecting Language and Vision Using Crowdsourced Dense Image Annotations