Even in the era of massive language models, it has been suggested that
character-level representations improve the performance of neural models. The
state-of-the-art neural semantic parser for Discourse Representation Structures
uses character-level representations, improving performance in the four
languages (i.e., English, German, Dutch, and Italian) in the Parallel Meaning
Bank dataset. However, how and why character-level information improves the
parser's performance remains unclear. This study provides an in-depth analysis
of performance changes by order of character sequences. In the experiments, we
compare F1-scores by shuffling the order and randomizing character sequences
after testing the performance of character-level information. Our results
indicate that incorporating character-level information does not improve the
performance in English and German. In addition, we find that the parser is not
sensitive to correct character order in Dutch. Nevertheless, performance
improvements are observed when using character-level information.

研究探讨了采用字符级表示对神经模型性能的影响，通过对英语、德语、荷兰语和意大利语数据集的实验结果发现，字符级信息的引入并不能提高英语和德语语言的性能，但字符级信息可以提高荷兰语和意大利语的性能。

字符级信息是否总是改善基于 DRS 的语义分析？

Does Character-level Information Always Improve DRS-based Semantic  Parsing?

Pre-trained language models (PLMs) have achieved great success in NLP and
have recently been used for tasks in computational semantics. However, these
tasks do not fully benefit from PLMs since meaning representations are not
explicitly included in the pre-training stage. We introduce multilingual
pre-trained language-meaning models based on Discourse Representation
Structures (DRSs), including meaning representations besides natural language
texts in the same model, and design a new strategy to reduce the gap between
the pre-training and fine-tuning objectives. Since DRSs are language neutral,
cross-lingual transfer learning is adopted to further improve the performance
of non-English tasks. Automatic evaluation results show that our approach
achieves the best performance on both the multilingual DRS parsing and
DRS-to-text generation tasks. Correlation analysis between automatic metrics
and human judgements on the generation task further validates the effectiveness
of our model. Human inspection reveals that out-of-vocabulary tokens are the
main cause of erroneous results.

本文介绍了基于话语表示结构的多语言预训练语言含义模型，在预训练阶段包括语义表示，并采用交叉语言迁移学习，以进一步提高非英语任务的性能。实验结果显示，该方法在多语言 DRS 解析和 DRS 到文本生成任务上实现了最佳性能。

多语言解析与生成的预训练语义模型

Pre-Trained Language-Meaning Models for Multilingual Parsing and  Generation

The paper presents the IWCS 2019 shared task on semantic parsing where the
goal is to produce Discourse Representation Structures (DRSs) for English
sentences. DRSs originate from Discourse Representation Theory and represent
scoped meaning representations that capture the semantics of negation, modals,
quantification, and presupposition triggers. Additionally, concepts and
event-participants in DRSs are described with WordNet synsets and the thematic
roles from VerbNet. To measure similarity between two DRSs, they are
represented in a clausal form, i.e. as a set of tuples. Participant systems
were expected to produce DRSs in this clausal form. Taking into account the
rich lexical information, explicit scope marking, a high number of shared
variables among clauses, and highly-constrained format of valid DRSs, all these
makes the DRS parsing a challenging NLP task. The results of the shared task
displayed improvements over the existing state-of-the-art parser.

该论文介绍了 2019 IWCS 分享任务的语义分析，目的是为英语句子生成话语表示结构（DRSs）。

关于语篇表征结构分析的第一次共享任务

The First Shared Task on Discourse Representation Structure Parsing

Neural methods have had several recent successes in semantic parsing, though
they have yet to face the challenge of producing meaning representations based
on formal semantics. We present a sequence-to-sequence neural semantic parser
that is able to produce Discourse Representation Structures (DRSs) for English
sentences with high accuracy, outperforming traditional DRS parsers. To
facilitate the learning of the output, we represent DRSs as a sequence of flat
clauses and introduce a method to verify that produced DRSs are well-formed and
interpretable. We compare models using characters and words as input and see
(somewhat surprisingly) that the former performs better than the latter. We
show that eliminating variable names from the output using De Bruijn-indices
increases parser performance. Adding silver training data boosts performance
even further.

提出了一种基于 Neural 的序列到序列的语义解析器，它能够高准确率地产生英语句子的 DRSs，并通过引入 De Bruijn 索引消除变量名以提高解析器性能，以及加入银标注数据来进一步提高性能。