With the explosive growth of the information on the Internet, the evaluation of the quality and credibility of web content has become more important than ever before.In this work, we focus on the quality assessment of texts.Recently, various methods have been proposed for the automated text scoring task and obtained competitive results.However, few studies have focused on both relevance and coherence, which are two important factors in evaluating text quality.To improve the scoring task, we propose two auxiliary tasks using negative sampling and integrate them into a multi-task learning framework.The first auxiliary task is relevance modeling and the other one is coherence modeling.We evaluate our model on the Automated Student Assessment Prize (ASAP) dataset.Experimental results show that our model achieves higher Quadratic Weighted Kappa (QWK) scores with an improvement of 1.5% on average.

Unlike advertising in traditional media, web search advertising content can be easily customized with little cost. In this paper, we apply content analysis and regression models on 11,818 unique ads related to the accommodation industry to empirically investigate how advertisers customize price information in their web search advertising content. To the best of our knowledge, our study is the first of this kind. We find that advertiser characteristics, such as website traffic, product quality, and position in the distribution chain, affect both the amount and forms of price information in its search advertising content. Moreover, the use of price information by an advertiser depends on query characteristics, such as search volume, cost per click ("CPC"), and specific words (e.g., trademark, location, price cue) in queries. Our empirical findings shed new light on how to effectively manage price information in search advertising, and suggest new research opportunities on web search advertising.