which contains 2961317/19536/20287 pairs.

Experiments
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Experimental Setups
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### Datasets

Our experiments are conducted on two text generating tasks: open-domain dialogue generation and recommendation sentence generation. We evaluate the performance of our algorithm and compare it with several strong baselines on the two tasks. For the first task, the public dialogue dataset Daily Dialog (DD) [@li2017dailydialog] is used. DD dataset is collected from different websites under 10 topics. It contains 13118 multi-turn dialogue sessions in English, and is split into training/validation/testing set of 11118/1000/1000 sessions. The average number of turns per session is 8.85 and the average number of tokens per utterance is 13.85. To avoid too long dialogue contexts, we first split long sessions into multiple short full speaker turns containing no more than 6 utterances with an utterance level sliding window. As a result, the final DD dataset contains 39567/3681/3471 full speaker turns in training/validation/testing set. For each full speaker turn, we use all utterances but the last one as the dialogue context to predict the last one. Need to note that though there may exist various responses for a question, DD dataset essentially only contains one-to-one data. To better model and evaluate the diversity, the newly 