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.

Input: Consider Input: Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks (CNN) are able to extract higher level features that are invariant to local spectral and temporal variations. Recurrent neural networks (RNNs) are powerful in learning the longer term temporal context in the audio signals. CNNs and RNNs as classifiers have recently shown improved performances over established methods in various sound recognition tasks. We combine these two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it on a polyphonic sound event detection task. We compare the performance of the proposed CRNN method with CNN, RNN, and other established methods, and observe a considerable improvement for four different datasets consisting of everyday sound events.

Output: Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection

Input: Consider Input: We investigate evaluation metrics for endto-end dialogue systems where supervised labels, such as task completion, are not available. Recent works in end-to-end dialogue systems have adopted metrics from machine translation and text summarization to compare a model's generated response to a single target response. We show that these metrics correlate very weakly or not at all with human judgements of the response quality in both technical and non-technical domains. We provide quantitative and qualitative results highlighting specific weaknesses in existing metrics, and provide recommendations for future development of better automatic evaluation metrics for dialogue systems.

Output: How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation

Input: Consider Input: We improve the computational complexity of online learning algorithms that require to often recompute least squares regression estimates of parameters. We propose two stochastic gradient descent schemes with randomisation in order to efficiently track the true solutions of the regression problems achieving an O(d) improvement in complexity, where d is the dimension of the data. The first algorithm assumes strong convexity in the regression problem, and we provide bounds on the error both in expectation and high probability (the latter is often needed to provide theoretical guarantees for higher level algorithms). The second algorithm deals with cases where strong convexity of the regression problem cannot be guaranteed and uses adaptive regularisation. We again give error bounds in both expectation and high probability. We apply our approaches to the linear bandit algorithms PEGE and ConfidenceBall and demonstrate significant gains in complexity in both cases. Since strong convexity is guaranteed by the PEGE algorithm, we lose only logarithmic factors in the regret performance of the algorithm. On the other hand, in the ConfidenceBall algorithm we adaptively regularise to ensure strong convexity, and this results in an O(n1/5)1 deterioration of the regret.
Output: Online gradient descent for least squares regression: Non-asymptotic bounds and application to bandits