We further use as features the embeddings of the claim, of the best-scoring snippet, and of the best-scoring sentence triplet from a Web page. We calculate these embeddings (i) as the average of the embeddings of the words in the text, and also (ii) using LSTM encodings, which we train for the task as part of a deep neural network (NN). We also use a task-specific embedding of the claim together with all the above evidence about it, which comes from the last hidden layer of the NN. 
 Question: What algorithm and embedding dimensions are used to build the task-specific embeddings?
 task-specific embedding of the claim together with all the above evidence about it, which comes from the last hidden layer of the NN