We show that most structured prediction problems can be solved in linear time and space by considering them as partial orderings of the tokens in the input string. Our method computes real numbers for each token in an input string and sorts the tokens accordingly, resulting in as few as 2 total orders of the tokens in the string. Each total order possesses a set of edges oriented from smaller to greater tokens. The intersection of total orders results in a partial order over the set of input tokens, which is then decoded into a directed graph representing the desired structure. Experiments show that our method achieves 95.4 LAS and 96.9 UAS by using an intersection of 2 total orders, 95.7 LAS and 97.1 UAS with 4 on the English Penn Treebank dependency parsing benchmark. Our method is also the first linear-complexity coreference resolution model and achieves 79.2 F1 on the English OntoNotes benchmark, which is comparable with state of the art.

翻译：我们证明了大多数结构化预测问题可以通过将输入字符串中的标记视为偏序关系，在线性时间和空间内解决。我们的方法为输入字符串中的每个标记计算实数，并据此对标记进行排序，从而产生多达2个标记的总序。每个总序拥有从较小标记指向较大标记的边集。总序的交集在输入标记集合上形成偏序，然后解码为表示所需结构的有向图。实验表明，我们的方法在英语宾州树库依存句法分析基准测试中，使用2个总序的交集达到了95.4的LAS和96.9的UAS，使用4个总序时达到95.7的LAS和97.1的UAS。我们的方法还是首个线性复杂度的共指消解模型，在英语OntoNotes基准测试上取得了79.2的F1值，与当前最优水平相当。