Standard evaluations of deep learning models for semantics using naturalistic
corpora are limited in what they can tell us about the fidelity of the learned
representations, because the corpora rarely come with good measures of semantic
complexity. To overcome this limitation, we present a method for generating
data sets of multiply-quantified natural language inference (NLI) examples in
which semantic complexity can be precisely characterized, and we use this
method to show that a variety of common architectures for NLI inevitably fail
to encode crucial information; only a model with forced lexical alignments
avoids this damaging information loss.

本文提出了一种生成数据集的方法，用于生成特定的自然语言推理例子，从而更精确地表征语义复杂性，并指出仅强制词汇对齐的模型可以避免关键信息的损失。

使用多重量化语句进行自然语言推理神经模型的压力测试

Stress-Testing Neural Models of Natural Language Inference with  Multiply-Quantified Sentences

This work exploits translation data as a source of semantically relevant
learning signal for models of word representation. In particular, we exploit
equivalence through translation as a form of distributed context and jointly
learn how to embed and align with a deep generative model. Our EmbedAlign model
embeds words in their complete observed context and learns by marginalisation
of latent lexical alignments. Besides, it embeds words as posterior probability
densities, rather than point estimates, which allows us to compare words in
context using a measure of overlap between distributions (e.g. KL divergence).
We investigate our model's performance on a range of lexical semantics tasks
achieving competitive results on several standard benchmarks including natural
language inference, paraphrasing, and text similarity.

使用翻译数据作为词表示模型的语义相关学习信号的源，利用翻译中的等价性作为分布式上下文进行联合嵌入和对齐，通过概率归一化的隐变量词汇对齐实现嵌入词，并比较上下文中的词的重叠度。应用于多个语义任务上获得了良好的性能表现。