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:
Words can be represented by composing the representations of subword units such as word segments, characters, and/or character n-grams. While such representations are effective and may capture the morphological regularities of words, they have not been systematically compared, and it is not understood how they interact with different morphological typologies. On a language modeling task, we present experiments that systematically vary (1) the basic unit of representation, (2) the composition of these representations, and (3) the morphological typology of the language modeled. Our results largely confirm previous findings that character representations are effective across many languages, though we find that a previously unstudied combination of character trigram representations composed with bi-LSTMs outperforms most other settings. However, we also find room for improvement: character models do not match the predictive accuracy of a model with access to explicit morphological analyses.

Ex Output:
From Characters to Words to in Between: Do We Capture Morphology?


Ex Input:
Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agent-coordination problems. A DCOP problem is a problem where several agents coordinate their values such that the sum of the resulting constraint costs is minimal. It is often desirable to solve DCOP problems with memory-bounded and asynchronous algorithms. We introduce Branch-and-Bound ADOPT (BnB-ADOPT), a memory-bounded asynchronous DCOP search algorithm that uses the message-passing and communication framework of ADOPT (Modi, Shen, Tambe, & Yokoo, 2005), a well known memory-bounded asynchronous DCOP search algorithm, but changes the search strategy of ADOPT from best-first search to depth-first branch-and-bound search. Our experimental results show that BnB-ADOPT finds cost-minimal solutions up to one order of magnitude faster than ADOPT for a variety of large DCOP problems and is as fast as NCBB, a memory-bounded synchronous DCOP search algorithm, for most of these DCOP problems. Additionally, it is often desirable to find bounded-error solutions for DCOP problems within a reasonable amount of time since finding cost-minimal solutions is NP-hard. The existing bounded-error approximation mechanism allows users only to specify an absolute error bound on the solution cost but a relative error bound is often more intuitive. Thus, we present two new bounded-error approximation mechanisms that allow for relative error bounds and implement them on top of BnB-ADOPT.

Ex Output:
An Asynchronous Branch-and-Bound DCOP Algorithm


Ex Input:
<lb>This paper addresses the problem of ad hoc microphone array calibration where only partial<lb>information about the distances between microphones is available. We construct a matrix<lb>consisting of the pairwise distances and propose to estimate the missing entries based on a novel<lb>Euclidean distance matrix completion algorithm by alternative low-rank matrix completion and<lb>projection onto the Euclidean distance space. This approach confines the recovered matrix to the<lb>EDM cone at each iteration of the matrix completion algorithm. The theoretical guarantees of<lb>the calibration performance are obtained considering the random and locally structured missing<lb>entries as well as the measurement noise on the known distances. This study elucidates the links<lb>between the calibration error and the number of microphones along with the noise level and the<lb>ratio of missing distances. Thorough experiments on real data recordings and simulated setups<lb>are conducted to demonstrate these theoretical insights. A significant improvement is achieved<lb>by the proposed Euclidean distance matrix completion algorithm over the state-of-the-art<lb>techniques for ad hoc microphone array calibration.

Ex Output:

Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees