text: 182,55,1174351372549943296,243126428,Jaemin Cho,"New #emnlp2019 paper on diverse sequence generation with @seo_minjoon @HannaHajishirzi TL;DR) Separating diversification from generation improves both diversity & accuracy in question generation and summarization • Paper: <LINK> • Code: <LINK> <LINK> @seo_minjoon @HannaHajishirzi We explicitly separate diversification from generation using a mixture-of-experts content selection module (called Selector) that guides an encoder-decoder model. @seo_minjoon @HannaHajishirzi Two-stage method: (1) Diversification: Selector samples different binary masks (called focus; m1, m2, and m3 in the figure) on a source sequence. (2) Generation: an encoder-decoder model generates different sequences from the source sequence guided by different masks. @seo_minjoon @HannaHajishirzi Not only does this improve the diversity of the generated sequences, but also improves accuracy (high fidelity) of them, since conventional MLE models often learn suboptimal mapping that is in the middle of the targets but not near any of them.",https://arxiv.org/abs/1909.01953,"Generating diverse sequences is important in many NLP applications such as question generation or summarization that exhibit semantically one-to-many relationships between source and the target sequences. We present a method to explicitly separate diversification from generation using a general plug-and-play module (called SELECTOR) that wraps around and guides an existing encoder-decoder model. The diversification stage uses a mixture of experts to sample different binary masks on the source sequence for diverse content selection. The generation stage uses a standard encoder-decoder model given each selected content from the source sequence. Due to the non-differentiable nature of discrete sampling and the lack of ground truth labels for binary mask, we leverage a proxy for ground truth mask and adopt stochastic hard-EM for training. In question generation (SQuAD) and abstractive summarization (CNN-DM), our method demonstrates significant improvements in accuracy, diversity and training efficiency, including state-of-the-art top-1 accuracy in both datasets, 6% gain in top-5 accuracy, and 3.7 times faster training over a state of the art model. Our code is publicly available at this https URL ",Mixture Content Selection for Diverse Sequence Generation,4,"['New #emnlp2019 paper on diverse sequence generation with @seo_minjoon @HannaHajishirzi\nTL;DR) Separating diversification from generation improves both diversity &amp; accuracy in question generation and summarization\n• Paper: <LINK>\n• Code: <LINK> <LINK>', '@seo_minjoon @HannaHajishirzi We explicitly separate diversification from generation using a mixture-of-experts content selection module (called Selector) that guides an encoder-decoder model.', '@seo_minjoon @HannaHajishirzi Two-stage method:\n(1) Diversification: Selector samples different binary masks (called focus; m1, m2, and m3 in the figure) on a source sequence.\n\n(2) Generation: an encoder-decoder model generates different sequences from the source sequence guided by different masks.', '@seo_minjoon @HannaHajishirzi Not only does this improve the diversity of the generated sequences, but also improves accuracy (high fidelity) of them, since conventional MLE models often learn suboptimal mapping that is in the middle of the targets but not near any of them.']",19,09,1017