Martin Boyanov

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We present the system we built for participating in SemEval-2016 Task 3 on Community Question Answering. We achieved the best results on subtask C, and strong results on subtasks A and B, by combining a rich set of various types of features: semantic, lexical, metadata, and user-related. The most important group turned out to be the metadata for the(More)
We present the system we built for participating in the PAN-2016 Author Profiling Task [9]. The task asked to predict the gender and the age group of a person given several samples of his/her writing, and it was offered for three different languages: English, Spanish, and Dutch. We participated in both subtasks, for all three languages. Our approach focused(More)
We propose to use question answering (QA) data from Web forums to train chatbots from scratch, i.e., without dialog training data. First, we extract pairs of question and answer sentences from the typically much longer texts of questions and answers in a forum. We then use these shorter texts to train seq2seq models in a more efficient way. We further(More)
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