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Jesse M. We built a BERT-based classifier to detect political leaning of short comments via the use of semi-unsupervised deep learning methods that produced an F1 score of 0. Salvanes,

We propose smoothed deep metric loss SDML and with our experiments on two QPR datasets we show that it significantly outperforms triplet loss in the noisy label setting. On the other hand, it is still a challenging problem to determine abusiveness in a text having no explicit abusive words.

This formulation dramatically reduces the task complexity involved in sentence compression. We utilize an iterative reranking based approach to further improve the rankings.

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For instance, a historian using a concept map browser might Ken Ott and Alex Chu ngo Asian gay boy BB to investigate the relationship between two politicians in a news archive. Furthermore, we combine our model and an abusive lexicon method.

Prior research has shown that geolocation can be substantially improved by including user network information. However, training supervised models for segment-level classification requires segment labels, which may be more difficult or expensive to obtain than review labels.

We evaluate the multi-task MTL model against singletask models and prior work. We categorize this dataset into 14 categories that have the potential to be censored on Weibo, and seek to quantify censorship by topic. Our study provides new benchmarks and baselines for generating highlights at the sub-sentence level.

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Detailed extended analyses of all submitted systems showed large relative improvements in accessing the most challenging multi-hop inference problems, while absolute performance remains low, highlighting the difficulty of generating detailed explanations through multi-hop reasoning.

In this paper, we present an unsupervised graph-based technique to mine paraphrases from a small set of sentences that roughly share the same topic or intent. Birachi, Nguyen Walter Scheirer David Chiang.

Ken Ott and Alex Chu ngo Asian gay boy BB

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