TASK DEFINITION: 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.
PROBLEM: In this paper, we implicitly incorporate morpheme information into word embedding. Based on the strategy we utilize the morpheme information, three models are proposed. To test the performances of our models, we conduct the word similarity and syntactic analogy. The results demonstrate the effectiveness of our methods. Our models beat the comparative baselines on both tasks to a great extent. On the golden standard Wordsim-353 and RG-65, our models approximately outperform CBOW for 5 and 7 percent, respectively. In addition, 7 percent advantage is also achieved by our models on syntactic analysis. According to parameter analysis, our models can increase the semantic information in the corpus and our performances on the smallest corpus are similar to the performance of CBOW on the corpus which is five times ours. This property of our methods may have some positive effects on NLP researches about the corpus-limited languages.

SOLUTION: Implicitly Incorporating Morphological Information into Word Embedding

PROBLEM: This paper tests the hypothesis that distinctive feature classifiers anchored at phonetic landmarks can be transferred crosslingually without loss of accuracy. Three consonant voicing classifiers were developed: (1) manually selected acoustic features anchored at a phonetic landmark, (2) MFCCs (either averaged across the segment or anchored at the landmark), and (3) acoustic features computed using a convolutional neural network (CNN). All detectors are trained on English data (TIMIT), and tested on English, Turkish, and Spanish (performance measured using F1 and accuracy). Experiments demonstrate that manual features outperform all MFCC classifiers, while CNN features outperform both. MFCC-based classifiers suffer an overall error rate increase of up to 96.1% when generalized from English to other languages. Manual features suffer only an up to 35.2% relative error rate increase, and CNN features actually perform the best on Turkish and Spanish, demonstrating that features capable of representing long-term spectral dynamics (CNN and landmark-based features) are able to generalize cross-lingually with little or no loss of accuracy.

SOLUTION: LANDMARK-BASED CONSONANT VOICING DETECTION ON MULTILINGUAL CORPORA

PROBLEM: Algorithm portfolios represent a strategy of composing multiple heuristic algorithms, each suited to a different class of problems, within a single general solver that will choose the best suited algorithm for each input. This approach recently gained popularity especially for solving combinatoric problems, but optimization applications are still emerging. The COCO platform [6] [5] of the BBOB workshop series is the current standard way to measure performance of continuous black-box optimization algorithms. As an extension to the COCO platform, we present the Python-based COCOpf framework that allows composing portfolios of optimization algorithms and running experiments with different selection strategies. In our framework, we focus on black-box algorithm portfolio and online adaptive selection. As a demonstration, we measure the performance of stock SciPy [8] optimization algorithms and the popular CMA algorithm [4] alone and in a portfolio with two simple selection strategies. We confirm that even a naive selection strategy can provide improved performance across problem classes.

SOLUTION:
COCOpf: An Algorithm Portfolio Framework