Controlled text perturbation is useful for evaluating and improving model
generalizability. However, current techniques rely on training a model for
every target perturbation, which is expensive and hard to generalize. We
present Tailor, a semantically-controlled text generation system. Tailor builds
on a pretrained seq2seq model and produces textual outputs conditioned on
control codes derived from semantic representations. We craft a set of
operations to modify the control codes, which in turn steer generation towards
targeted attributes. These operations can be further composed into higher-level
ones, allowing for flexible perturbation strategies. We demonstrate the
effectiveness of these perturbations in multiple applications. First, we use
Tailor to automatically create high-quality contrast sets for four distinct
natural language processing (NLP) tasks. These contrast sets contain fewer
spurious artifacts and are complementary to manually annotated ones in their
lexical diversity. Second, we show that Tailor perturbations can improve model
generalization through data augmentation. Perturbing just 2% of training data
leads to a 5.8-point gain on an NLI challenge set measuring reliance on
syntactic heuristics.

Tailor 是一个基于预训练的 seq2seq 模型和语义表示的、能够生成文本输出并且能够在特征属性控制下改变目标文本的生成系统。通过修改控制代码，该系统能够灵活地实现对数据增强的语义控制，能够有效的改善模型泛化能力，并在多个任务中展现优异表现，包括生成高质量对比数据集和在 NLI 挑战集上大幅提升模型性能。

Tailor: 利用语义控制生成和扰动文本

Tailor: Generating and Perturbing Text with Semantic Controls

Accurately learning from user data while providing quantifiable privacy
guarantees provides an opportunity to build better ML models while maintaining
user trust. This paper presents a formal approach to carrying out privacy
preserving text perturbation using the notion of dx-privacy designed to achieve
geo-indistinguishability in location data. Our approach applies carefully
calibrated noise to vector representation of words in a high dimension space as
defined by word embedding models. We present a privacy proof that satisfies
dx-privacy where the privacy parameter epsilon provides guarantees with respect
to a distance metric defined by the word embedding space. We demonstrate how
epsilon can be selected by analyzing plausible deniability statistics backed up
by large scale analysis on GloVe and fastText embeddings. We conduct privacy
audit experiments against 2 baseline models and utility experiments on 3
datasets to demonstrate the tradeoff between privacy and utility for varying
values of epsilon on different task types. Our results demonstrate practical
utility (< 2% utility loss for training binary classifiers) while providing
better privacy guarantees than baseline models.

本文使用 dx-privacy 和 word embedding，提出了一种隐私保护的文本扰动方法，并在大量数据集上进行了实证研究，结果表明在提供更好的隐私保证的同时，实用性有所提高（训练二元分类器的实用性可达到 < 2% 的损失）