We present a model and methodology for learning paraphrastic sentence
embeddings directly from bitext, removing the time-consuming intermediate step
of creating paraphrase corpora. Further, we show that the resulting model can
be applied to cross-lingual tasks where it both outperforms and is orders of
magnitude faster than more complex state-of-the-art baselines.

从双语语料中直接学习释义句子嵌入的模型和方法，有效消除创建释义文本体的耗时环节。进一步地，该模型可用于跨语言任务，其性能优于且比最先进的复杂基线算法快几个数量级。

来自平行翻译的简单有效的释义相似度

Simple and Effective Paraphrastic Similarity from Parallel Translations

We consider the problem of learning general-purpose, paraphrastic sentence
embeddings in the setting of Wieting et al. (2016b). We use neural machine
translation to generate sentential paraphrases via back-translation of
bilingual sentence pairs. We evaluate the paraphrase pairs by their ability to
serve as training data for learning paraphrastic sentence embeddings. We find
that the data quality is stronger than prior work based on bitext and on par
with manually-written English paraphrase pairs, with the advantage that our
approach can scale up to generate large training sets for many languages and
domains. We experiment with several language pairs and data sources, and
develop a variety of data filtering techniques. In the process, we explore how
neural machine translation output differs from human-written sentences, finding
clear differences in length, the amount of repetition, and the use of rare
words.

本研究使用神经机器翻译通过回译双语句子对生成句子的解释，并对语料对进行了筛选处理，在此基础上建立通用的释义句子表示模型，结果显示该模型对于很多语言领域其正确率与手动编写的英语释义句子相当。

从回译双语语料中学习复述句嵌入

Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext

We consider the problem of learning general-purpose, paraphrastic sentence
embeddings based on supervision from the Paraphrase Database (Ganitkevitch et
al., 2013). We compare six compositional architectures, evaluating them on
annotated textual similarity datasets drawn both from the same distribution as
the training data and from a wide range of other domains. We find that the most
complex architectures, such as long short-term memory (LSTM) recurrent neural
networks, perform best on the in-domain data. However, in out-of-domain
scenarios, simple architectures such as word averaging vastly outperform LSTMs.
Our simplest averaging model is even competitive with systems tuned for the
particular tasks while also being extremely efficient and easy to use.
In order to better understand how these architectures compare, we conduct
further experiments on three supervised NLP tasks: sentence similarity,
entailment, and sentiment classification. We again find that the word averaging
models perform well for sentence similarity and entailment, outperforming
LSTMs. However, on sentiment classification, we find that the LSTM performs
very strongly-even recording new state-of-the-art performance on the Stanford
Sentiment Treebank.
We then demonstrate how to combine our pretrained sentence embeddings with
these supervised tasks, using them both as a prior and as a black box feature
extractor. This leads to performance rivaling the state of the art on the SICK
similarity and entailment tasks. We release all of our resources to the
research community with the hope that they can serve as the new baseline for
further work on universal sentence embeddings.

研究使用 Paraphrase Database 监督学习通用的、同义句的句子嵌入，比较 6 种组成结构并在相似度数据集和三个 NLP 任务上进行评估，在领域内复杂的结构（如 LSTM）有更好的性能，但在领域外使用简单架构如词平均比 LSTM 表现更好，同时演示如何将预训练的句子嵌入与监督任务相结合。