Sentence semantic matching is a research hotspot in natural language
processing, which is considerably significant in various key scenarios, such as
community question answering, searching, chatbot, and recommendation. Since
most of the advanced models directly model the semantic relevance among words
between two sentences while neglecting the \textit{keywords} and
\textit{intents} concepts of them, DC-Match is proposed to disentangle keywords
from intents and utilizes them to optimize the matching performance. Although
DC-Match is a simple yet effective method for semantic matching, it highly
depends on the external NER techniques to identify the keywords of sentences,
which limits the performance of semantic matching for minor languages since
satisfactory NER tools are usually hard to obtain. In this paper, we propose to
generally and flexibly resolve the text into multi concepts for multilingual
semantic matching to liberate the model from the reliance on NER models. To
this end, we devise a \underline{M}ulti-\underline{C}oncept \underline{P}arsed
\underline{S}emantic \underline{M}atching framework based on the pre-trained
language models, abbreviated as \textbf{MCP-SM}, to extract various concepts
and infuse them into the classification tokens. We conduct comprehensive
experiments on English datasets QQP and MRPC, and Chinese dataset Medical-SM.
Besides, we experiment on Arabic datasets MQ2Q and XNLI, the outstanding
performance further prove MCP-SM's applicability in low-resource languages.

MCP-SM 提出了一个多概念解析的语义匹配框架，用于从句子中提取各种概念，并将其融入分类标记，以解决语义匹配中对命名实体识别模型的依赖问题。

一种通用且灵活的多概念解析框架用于多语言语义匹配

A General and Flexible Multi-concept Parsing Framework for Multilingual  Semantic Matching

Using generated data to improve the performance of downstream discriminative
models has recently gained popularity due to the great development of
pre-trained language models. In most previous studies, generative models and
discriminative models are trained separately and thus could not adapt to any
changes in each other. As a result, the generated samples can easily deviate
from the real data distribution, while the improvement of the discriminative
model quickly reaches saturation. Generative adversarial networks (GANs) train
generative models via an adversarial process with discriminative models to
achieve joint training. However, the training of standard GANs is notoriously
unstable and often falls short of convergence. In this paper, to address these
issues, we propose a $\textit{self-consistent learning}$ framework, in which a
discriminator and a generator are cooperatively trained in a closed-loop form.
The discriminator and the generator enhance each other during multiple rounds
of alternating training until a scoring consensus is reached. This framework
proves to be easy to train and free from instabilities such as mode collapse
and non-convergence. Extensive experiments on sentence semantic matching
demonstrate the effectiveness of the proposed framework: the discriminator
achieves 10+ AP of improvement on the zero-shot setting and new
state-of-the-art performance on the full-data setting.

本文提出了一种自洽学习的框架，利用生成对抗网络的生成模型和判别模型的博弈优化，使两者协同训练，避免了 GAN 训练不稳定的问题，并在句子语义匹配问题上实现了较好的性能。