In this paper, we propose a novel method for joint entity and relation
extraction from unstructured text by framing it as a conditional sequence
generation problem. In contrast to conventional generative information
extraction models that are left-to-right token-level generators, our approach
is \textit{span-based}. It generates a linearized graph where nodes represent
text spans and edges represent relation triplets. Our method employs a
transformer encoder-decoder architecture with pointing mechanism on a dynamic
vocabulary of spans and relation types. Our model can capture the structural
characteristics and boundaries of entities and relations through span
representations while simultaneously grounding the generated output in the
original text thanks to the pointing mechanism. Evaluation on benchmark
datasets validates the effectiveness of our approach, demonstrating competitive
results. Code is available at this https URL

我们提出了一种新的方法，将无结构文本中的实体和关系提取作为条件序列生成问题，采用基于跨度的方法生成线性化图，其中节点表示文本跨度，边表示关系三元组。通过跨度表示，我们的模型可以捕捉实体和关系的结构特征和边界，并通过指向机制将生成的输出与原始文本进行关联。基准数据集上的评估验证了我们方法的有效性，展示了竞争性的结果。

一种自回归的文本到图形框架用于联合实体和关系抽取

An Autoregressive Text-to-Graph Framework for Joint Entity and Relation  Extraction

Mixed reality applications require tracking the user's full-body motion to
enable an immersive experience. However, typical head-mounted devices can only
track head and hand movements, leading to a limited reconstruction of full-body
motion due to variability in lower body configurations. We propose BoDiffusion
-- a generative diffusion model for motion synthesis to tackle this
under-constrained reconstruction problem. We present a time and space
conditioning scheme that allows BoDiffusion to leverage sparse tracking inputs
while generating smooth and realistic full-body motion sequences. To the best
of our knowledge, this is the first approach that uses the reverse diffusion
process to model full-body tracking as a conditional sequence generation task.
We conduct experiments on the large-scale motion-capture dataset AMASS and show
that our approach outperforms the state-of-the-art approaches by a significant
margin in terms of full-body motion realism and joint reconstruction error.

本研究提出了一种名为 BoDiffusion 的生成扩散模型，用于解决头戴式设备只能追踪头部和手部运动的局限，通过条件序列生成任务模拟全身运动，实验表明该模型在运动合成方面的性能优于现有模型。

BoDiffusion：用于全身人体运动合成的稀疏观测扩散

BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion  Synthesis

Despite significant progress of generative models in the natural sciences,
their controllability remains challenging. One fundamentally missing aspect of
molecular or protein generative models is an inductive bias that can reflect
continuous properties of interest. To that end, we propose the Regression
Transformer (RT), a novel method that abstracts regression as a conditional
sequence modeling problem. This introduces a new paradigm of multitask language
models which seamlessly bridge sequence regression and conditional sequence
generation.
We thoroughly demonstrate that, despite using a nominal-scale training
objective, the RT matches or surpasses the performance of conventional
regression models in property prediction tasks of small molecules, proteins and
chemical reactions. Critically, priming the same model with continuous
properties yields a highly competitive conditional generative model that
outperforms specialized approaches in a substructure-constrained,
property-driven molecule generation benchmark. Our dichotomous approach is
facilitated by a novel, alternating training scheme that enables the model to
decorate seed sequences by desired properties, e.g., to optimize reaction
yield.
In sum, the RT is the first report of a multitask model that concurrently
excels at predictive and generative tasks in biochemistry. This finds
particular application in property-driven, local exploration of the chemical or
protein space and could pave the road toward foundation models in material
design.
The code to reproduce all experiments of the paper is available at:
this https URL

本文提出 Regression Transformer（RT）方法，它是一种基于序列模型的回归模型，可用于小分子、蛋白质和化学反应的属性预测任务及其生成。该方法通过新颖的交替训练方案，使其在化学或蛋白质空间的属性驱动、局部探索中具有特殊的应用和潜在的基础模型设计方向。