While multi-party conversations are often less structured than monologues and
documents, they are implicitly organized by semantic level correlations across
the interactive turns, and dialogue discourse analysis can be applied to
predict the dependency structure and relations between the elementary discourse
units, and provide feature-rich structural information for downstream tasks.
However, the existing corpora with dialogue discourse annotation are collected
from specific domains with limited sample sizes, rendering the performance of
data-driven approaches poor on incoming dialogues without any domain
adaptation. In this paper, we first introduce a Transformer-based parser, and
assess its cross-domain performance. We next adopt three methods to gain domain
integration from both data and language modeling perspectives to improve the
generalization capability. Empirical results show that the neural parser can
benefit from our proposed methods, and performs better on cross-domain dialogue
samples.

本文介绍了一种 Transformer 模型，可以应用于多方对话语篇分析，并结合三种方法，实现跨领域数据泛化，以提高神经网络解析器在跨域对话样本中的表现。

通过领域整合来提高多方对话语篇分析

Improving Multi-Party Dialogue Discourse Parsing via Domain Integration

Recent progress in semantic parsing scarcely considers languages other than
English but professional translation can be prohibitively expensive. We adapt a
semantic parser trained on a single language, such as English, to new languages
and multiple domains with minimal annotation. We query if machine translation
is an adequate substitute for training data, and extend this to investigate
bootstrapping using joint training with English, paraphrasing, and multilingual
pre-trained models. We develop a Transformer-based parser combining paraphrases
by ensembling attention over multiple encoders and present new versions of ATIS
and Overnight in German and Chinese for evaluation. Experimental results
indicate that MT can approximate training data in a new language for accurate
parsing when augmented with paraphrasing through multiple MT engines.
Considering when MT is inadequate, we also find that using our approach
achieves parsing accuracy within 2% of complete translation using only 50% of
training data.

本研究的主要目的是使一个语义解析器模型从英语语言转移到其他多种语言和领域上，通过机器翻译、释义、多语言预训练模型，我们得出一个基于 Transformer 的语义解析器，用于在德语和中文中准确解析。实验表明，机器翻译结合多组 MT 引擎释义可以近似于多种语言的训练数据，同时我们还成功地将训练数据减半，最终准确度仅低于完整翻译 2%。