Transformer-based encoder-decoder models that generate outputs in a
left-to-right fashion have become standard for sequence-to-sequence tasks. In
this paper, we propose a framework for decoding that produces sequences from
the "outside-in": at each step, the model chooses to generate a token on the
left, on the right, or join the left and right sequences. We argue that this is
more principled than prior bidirectional decoders. Our proposal supports a
variety of model architectures and includes several training methods, such as a
dynamic programming algorithm that marginalizes out the latent ordering
variable. Our model improves considerably over a simple baseline based on
unidirectional transformers on the SIGMORPHON 2023 inflection task and sets
SOTA on the 2022 shared task. The model performs particularly well on long
sequences, can learn the split point of words composed of stem and affix
(without supervision), and performs better relative to the baseline on datasets
that have fewer unique lemmas (but more examples per lemma).

本文中，我们提出了一种从外向内生成序列的解码框架，该框架通过动态规划等多种训练方法，支持多种模型结构，并在推土机 2023 转换任务上取得显著提高。

双向解码框架：以形态变化为例的案例研究

A Framework for Bidirectional Decoding: Case Study in Morphological  Inflection

Although neural sequence-to-sequence models have been successfully applied to
semantic parsing, they fail at compositional generalization, i.e., they are
unable to systematically generalize to unseen compositions of seen components.
Motivated by traditional semantic parsing where compositionality is explicitly
accounted for by symbolic grammars, we propose a new decoding framework that
preserves the expressivity and generality of sequence-to-sequence models while
featuring lexicon-style alignments and disentangled information processing.
Specifically, we decompose decoding into two phases where an input utterance is
first tagged with semantic symbols representing the meaning of individual
words, and then a sequence-to-sequence model is used to predict the final
meaning representation conditioning on the utterance and the predicted tag
sequence. Experimental results on three semantic parsing datasets show that the
proposed approach consistently improves compositional generalization across
model architectures, domains, and semantic formalisms.

在语义解析中，提出了一个新的解码框架，该框架保留了序列到序列模型的表现力和通用性，同时具有类似词典的对齐和解开信息处理，能够提高模型的组合泛化能力。