Creating effective and reliable task-oriented dialog systems (ToDSs) is
challenging, not only because of the complex structure of these systems, but
also due to the scarcity of training data, especially when several modules need
to be trained separately, each one with its own input/output training examples.
Data augmentation (DA), whereby synthetic training examples are added to the
training data, has been successful in other NLP systems, but has not been
explored as extensively in ToDSs. We empirically evaluate the effectiveness of
DA methods in an end-to-end ToDS setting, where a single system is trained to
handle all processing stages, from user inputs to system outputs. We experiment
with two ToDSs (UBAR, GALAXY) on two datasets (MultiWOZ, KVRET). We consider
three types of DA methods (word-level, sentence-level, dialog-level), comparing
eight DA methods that have shown promising results in ToDSs and other NLP
systems. We show that all DA methods considered are beneficial, and we
highlight the best ones, also providing advice to practitioners. We also
introduce a more challenging few-shot cross-domain ToDS setting, reaching
similar conclusions.

在任务导向型对话系统中，通过数据增强方法在端到端设置下进行实证评估，证明了所有考虑的数据增强方法都是有益的，并提供了最佳方法和实践建议，同时引入了一个更具挑战性的少样本跨领域任务导向型对话系统的设置。

比较数据增强方法对于端到端任务导向型对话系统的效果

Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog  Systems

Recent work has shown that commonly available machine reading comprehension
(MRC) datasets can be used to train high-performance neural information
retrieval (IR) systems. However, the evaluation of neural IR has so far been
limited to standard supervised learning settings, where they have outperformed
traditional term matching baselines. We conduct in-domain and out-of-domain
evaluations of neural IR, and seek to improve its robustness across different
scenarios, including zero-shot settings. We show that synthetic training
examples generated using a sequence-to-sequence generator can be effective
towards this goal: in our experiments, pre-training with synthetic examples
improves retrieval performance in both in-domain and out-of-domain evaluation
on five different test sets.

研究表明，机器阅读理解数据集可以用于训练高性能的神经信息检索系统，利用序列到序列生成器生成的合成样本的预训练可以提高神经信息检索系统的鲁棒性和检索表现。

利用合成预训练构建稳健的神经检索模型

Towards Robust Neural Retrieval Models with Synthetic Pre-Training

Models that perform well on a training domain often fail to generalize to
out-of-domain (OOD) examples. Data augmentation is a common method used to
prevent overfitting and improve OOD generalization. However, in natural
language, it is difficult to generate new examples that stay on the underlying
data manifold. We introduce SSMBA, a data augmentation method for generating
synthetic training examples by using a pair of corruption and reconstruction
functions to move randomly on a data manifold. We investigate the use of SSMBA
in the natural language domain, leveraging the manifold assumption to
reconstruct corrupted text with masked language models. In experiments on
robustness benchmarks across 3 tasks and 9 datasets, SSMBA consistently
outperforms existing data augmentation methods and baseline models on both
in-domain and OOD data, achieving gains of 0.8% accuracy on OOD Amazon reviews,
1.8% accuracy on OOD MNLI, and 1.4 BLEU on in-domain IWSLT14 German-English.

使用对数据流形的损坏和重构函数，我们在自然语言领域引入了 SSMBA 数据增强方法，实现了对语料库的数据增强，结果表明在三个任务和九个数据集上，SSMBA 始终优于现有的数据增强方法和基线模型，特别是在机器翻译和情感分析方面，准确率和 BLEU 分数都有很大提升。