Undirected neural sequence models have achieved performance competitive with
the state-of-the-art directed sequence models that generate monotonically from
left to right in machine translation tasks. In this work, we train a policy
that learns the generation order for a pre-trained, undirected translation
model via reinforcement learning. We show that the translations decoded by our
learned orders achieve higher BLEU scores than the outputs decoded from left to
right or decoded by the learned order from Mansimov et al. (2019) on the WMT'14
German-English translation task. On examples with a maximum source and target
length of 30 from De-En, WMT'16 English-Romanian, and WMT'21 English-Chinese
translation tasks, our learned order outperforms all heuristic generation
orders on four out of six tasks. We next carefully analyze the learned order
patterns via qualitative and quantitative analysis. We show that our policy
generally follows an outer-to-inner order, predicting the left-most and
right-most positions first, and then moving toward the middle while skipping
less important words at the beginning. Furthermore, the policy usually predicts
positions for a single syntactic constituent structure in consecutive steps. We
believe our findings could provide more insights on the mechanism of undirected
generation models and encourage further research in this direction. Our code is
publicly available at this https URL

通过强化学习训练预训练的无向翻译模型的生成顺序，从而实现了比从左到右或学习自 Mansimov 等人 (2019) 学习的顺序得到的输出 BLEU 分数更高的翻译结果。

无向序列模型生成顺序的学习和分析

Learning and Analyzing Generation Order for Undirected Sequence Models

This paper explores a variant of automatic headline generation methods, where
a generated headline is required to include a given phrase such as a company or
a product name. Previous methods using Transformer-based models generate a
headline including a given phrase by providing the encoder with additional
information corresponding to the given phrase. However, these methods cannot
always include the phrase in the generated headline. Inspired by previous
RNN-based methods generating token sequences in backward and forward directions
from the given phrase, we propose a simple Transformer-based method that
guarantees to include the given phrase in the high-quality generated headline.
We also consider a new headline generation strategy that takes advantage of the
controllable generation order of Transformer. Our experiments with the Japanese
News Corpus demonstrate that our methods, which are guaranteed to include the
phrase in the generated headline, achieve ROUGE scores comparable to previous
Transformer-based methods. We also show that our generation strategy performs
better than previous strategies.

本文探讨了一种自动生成标题的方法，该方法要求生成的标题必须包含给定的短语，如公司或产品名称。我们提出了一种简单的基于 Transformer 的方法，可以保证在生成的标题中包含给定的短语，并考虑了一种新的标题生成策略，利用 Transformer 的可控制生成顺序。我们的实验表明，我们的方法达到了与先前基于 Transformer 的方法相当的 ROUGE 得分，并且我们的生成策略表现更好。

基于 Transformer 的词汇约束标题生成

Transformer-based Lexically Constrained Headline Generation

In this work, we present an empirical study of generation order for machine
translation. Building on recent advances in insertion-based modeling, we first
introduce a soft order-reward framework that enables us to train models to
follow arbitrary oracle generation policies. We then make use of this framework
to explore a large variety of generation orders, including uninformed orders,
location-based orders, frequency-based orders, content-based orders, and
model-based orders. Curiously, we find that for the WMT'14 English $\to$ German
translation task, order does not have a substantial impact on output quality,
with unintuitive orderings such as alphabetical and shortest-first matching the
performance of a standard Transformer. This demonstrates that traditional
left-to-right generation is not strictly necessary to achieve high performance.
On the other hand, results on the WMT'18 English $\to$ Chinese task tend to
vary more widely, suggesting that translation for less well-aligned language
pairs may be more sensitive to generation order.

本文通过实证研究生成顺序来探讨机器翻译的效果，发现对于英德翻译任务，以字母和最短优先等方式生成的效果相当于标准 Transformer 生成的效果，提出了不必严格遵循从左到右的翻译方式。而对于英中翻译任务，由于语言对齐度低，生成的效果则对生成顺序更加敏感。