Paraphrase generation has been widely used in various downstream tasks. Most
tasks benefit mainly from high quality paraphrases, namely those that are
semantically similar to, yet linguistically diverse from, the original
sentence. Generating high-quality paraphrases is challenging as it becomes
increasingly hard to preserve meaning as linguistic diversity increases. Recent
works achieve nice results by controlling specific aspects of the paraphrase,
such as its syntactic tree. However, they do not allow to directly control the
quality of the generated paraphrase, and suffer from low flexibility and
scalability. Here we propose $QCPG$, a quality-guided controlled paraphrase
generation model, that allows directly controlling the quality dimensions.
Furthermore, we suggest a method that given a sentence, identifies points in
the quality control space that are expected to yield optimal generated
paraphrases. We show that our method is able to generate paraphrases which
maintain the original meaning while achieving higher diversity than the
uncontrolled baseline. The models, the code, and the data can be found in
this https URL.

该研究提出一种名为 QCPG 的控制质量的引号生成模型，该模型能够直接控制生成的引语的质量维度，并且还提供了一种方法来确定预期能够产生最优引语的质量控制空间中的点。实验表明，该模型能够生成保持原始含义的引语并且比未受控的基线具有更高的多样性。

质量控制的释义生成

Quality Controlled Paraphrase Generation

We introduce a tree-structured attention neural network for sentences and
small phrases and apply it to the problem of sentiment classification. Our
model expands the current recursive models by incorporating structural
information around a node of a syntactic tree using both bottom-up and top-down
information propagation. Also, the model utilizes structural attention to
identify the most salient representations during the construction of the
syntactic tree. To our knowledge, the proposed models achieve state of the art
performance on the Stanford Sentiment Treebank dataset.

本文提出了一种基于树形结构的注意力神经网络模型，用于情感分类问题。该模型使用自下向上和自上而下的信息传播结合语法树中节点周围的结构信息。模型在构建句法树时，利用结构注意力识别最显著的表示。实验结果表明，该模型在斯坦福情感树库数据集上实现了最先进的性能表现。