The Artificial Intelligence industry regularly develops applications that
mostly rely on Knowledge Bases, a data repository about specific, or general,
domains, usually represented in a graph shape. Similar to other databases, they
face two main challenges: information ingestion and information retrieval. We
approach these challenges by jointly learning graph extraction from text and
text generation from graphs. The proposed solution, a T5 architecture, is
trained in a multi-task semi-supervised environment, with our collected
non-parallel data, following a cycle training regime. Experiments on WebNLG
dataset show that our approach surpasses unsupervised state-of-the-art results
in text-to-graph and graph-to-text. More relevantly, our framework is more
consistent across seen and unseen domains than supervised models. The resulting
model can be easily trained in any new domain with non-parallel data, by simply
adding text and graphs about it, in our cycle framework.

该研究提出了一种基于 T5 架构的多任务半监督环境下联合学习文本图谱提取和图谱生成的解决方案，此方法在 WebNLG 数据集中表现优于无监督最新成果，且相较于有监督模型更具一致性。

一个多任务半监督框架用于 Text2Graph 和 Graph2Text

A multi-task semi-supervised framework for Text2Graph & Graph2Text

Referring Expression Generation (REG) is the task of generating contextually
appropriate references to entities. A limitation of existing REG systems is
that they rely on entity-specific supervised training, which means that they
cannot handle entities not seen during training. In this study, we address this
in two ways. First, we propose task setups in which we specifically test a REG
system's ability to generalize to entities not seen during training. Second, we
propose a profile-based deep neural network model, ProfileREG, which encodes
both the local context and an external profile of the entity to generate
reference realizations. Our model generates tokens by learning to choose
between generating pronouns, generating from a fixed vocabulary, or copying a
word from the profile. We evaluate our model on three different splits of the
WebNLG dataset, and show that it outperforms competitive baselines in all
settings according to automatic and human evaluations.

该研究提出了一种面向上下文的实体参照生成模型，并解决了现有模型依赖于特定实体训练数据的局限性，同时在 WebNLG 数据集上进行的实验表明，该模型在多个方面有着优越的表现。