In Grammatical Error Correction (GEC), sequence labeling models enjoy fast
inference compared to sequence-to-sequence models; however, inference in
sequence labeling GEC models is an iterative process, as sentences are passed
to the model for multiple rounds of correction, which exposes the model to
sentences with progressively fewer errors at each round. Traditional GEC models
learn from sentences with fixed error rates. Coupling this with the iterative
correction process causes a mismatch between training and inference that
affects final performance. In order to address this mismatch, we propose a
GAN-like sequence labeling model, which consists of a grammatical error
detector as a discriminator and a grammatical error labeler with Gumbel-Softmax
sampling as a generator. By sampling from real error distributions, our errors
are more genuine compared to traditional synthesized GEC errors, thus
alleviating the aforementioned mismatch and allowing for better training. Our
results on several evaluation benchmarks demonstrate that our proposed approach
is effective and improves the previous state-of-the-art baseline.

本研究提出了一种 GAN 类似的序列标注模型，采用 Gumbel-Softmax 采样和真实的错误分布来进行语法纠错，以解决传统模型训练与推理不匹配的问题，实验结果表明该方法有效，并改进了之前的最先进基准。

将语法错误纠正作为类 GAN 的序列标注

Grammatical Error Correction as GAN-like Sequence Labeling

The task of graph-to-text generation aims at producing sentences that
preserve the meaning of input graphs. As a crucial defect, the current
state-of-the-art models may mess up or even drop the core structural
information of input graphs when generating outputs. We propose to tackle this
problem by leveraging richer training signals that can guide our model for
preserving input information. In particular, we introduce two types of
autoencoding losses, each individually focusing on different aspects (a.k.a.
views) of input graphs. The losses are then back-propagated to better calibrate
our model via multi-task training. Experiments on two benchmarks for
graph-to-text generation show the effectiveness of our approach over a
state-of-the-art baseline. Our code is available at
http://github.com/Soistesimmer/AMR-multiview.

该研究提出通过使用自动编码损失的多任务训练的方法，从而在图到文本生成领域中更好地保存输入信息并提高性能，实验证明其优于现有基准模型。

图到文本生成中的结构信息保留

Structural Information Preserving for Graph-to-Text Generation

Existing entity alignment methods mainly vary on the choices of encoding the
knowledge graph, but they typically use the same decoding method, which
independently chooses the local optimal match for each source entity. This
decoding method may not only cause the "many-to-one" problem but also neglect
the coordinated nature of this task, that is, each alignment decision may
highly correlate to the other decisions. In this paper, we introduce two
coordinated reasoning methods, i.e., the Easy-to-Hard decoding strategy and
joint entity alignment algorithm. Specifically, the Easy-to-Hard strategy first
retrieves the model-confident alignments from the predicted results and then
incorporates them as additional knowledge to resolve the remaining
model-uncertain alignments. To achieve this, we further propose an enhanced
alignment model that is built on the current state-of-the-art baseline. In
addition, to address the many-to-one problem, we propose to jointly predict
entity alignments so that the one-to-one constraint can be naturally
incorporated into the alignment prediction. Experimental results show that our
model achieves the state-of-the-art performance and our reasoning methods can
also significantly improve existing baselines.

本文提出了两种协调推理方法，即 Easy-to-Hard 解码策略和共同实体对齐算法，以解决实体对齐过程中的 “一对多” 问题，并通过扩展当前基线的对齐模型来实现。实验结果表明，该模型达到了最先进的性能水平，提出的推理方法也可以显著改进现有的基线。