We present a data-driven approach to generating responses to Twitter status posts, based on phrase-based Statistical Machine Translation. We find that mapping conversational stimuli onto responses is more difficult than translating between languages, due to the wider range of possible responses, the larger fraction of unaligned words/phrases, and the presence of large phrase pairs whose alignment cannot be further decomposed. After addressing these challenges, we compare approaches based on SMT and Information Retrieval in a human evaluation. We show that SMT outperforms IR on this task, and its output is preferred over actual human responses in 15 % of cases. As far as we are aware, this is the first work to investigate the use of phrase-based SMT to directly translate a linguistic stimulus into an appropriate response. 1

It is popular for users in Web 2.0 era to freely annotate online resources with tags. To ease the annotation process, it has been great interest in automatic tag suggestion. We propose a method to suggest tags according to the text description of a resource. By considering both the description and tags of a given resource as summaries to the resource written in two languages, we adopt word alignment models in statistical machine translation to bridge their vocabulary gap. Based on the translation probabilities between the words in descriptions and the tags estimated on a large set of description-tags pairs, we build a word trigger method (WTM) to suggest tags according to the words in a resource description. Experiments on real world datasets show that WTM is effective and robust compared with other methods. Moreover, WTM is relatively simple and efficient, which is practical for Web applications. 1

Abstract—Using a total of 60,419 ad links collected from three search engines (i.e., Bing, Google, and Yahoo), we characterize the “mobile-friendliness ” of landing pages in sponsored search. In particular, we analyze the common and different characteristics between landing pages made for desktop vs. mobile device users, measure/validate the quantitative scores for their mobilefriendliness, and classify the results with respect to types of queries and landing pages. Based on our findings, we articulate that: (1) current landing pages (regardless of search engines or platforms) are not mobile-friendly enough, and (2) better datadriven methods (as opposed to current static methods) to help advertisers build mobile-friendly landing pages are needed. I.