We consider automatically identifying the defined term within a mathematical
definition from the text of an academic article. Inspired by the development of
transformer-based natural language processing applications, we pose the problem
as (a) a token-level classification task using fine-tuned pre-trained
transformers; and (b) a question-answering task using a generalist large
language model (GPT). We also propose a rule-based approach to build a labeled
dataset from the LATEX source of papers. Experimental results show that it is
possible to reach high levels of precision and recall using either recent (and
expensive) GPT 4 or simpler pre-trained models fine-tuned on our task.

我们考虑从学术论文的文本中自动识别数学定义中的明确术语。我们将问题作为（a）使用微调的预训练变压器进行令牌级分类任务，以及（b）使用通用大型语言模型（GPT）进行问答任务来解决。我们还提出了一种基于规则的方法，通过从论文的 LATEX 源代码构建一个带标签的数据集。实验结果表明，使用最近（和昂贵的）GPT 4 或在我们的任务上进行微调的较简单的预训练模型均可以达到高精度和召回率的水平。

利用 Transformer 提取数学学术文章中的定义

Extracting Definienda in Mathematical Scholarly Articles with  Transformers

Open attribute value extraction for emerging entities is an important but
challenging task. A lot of previous works formulate the problem as a
\textit{question-answering} (QA) task. While the collections of articles from
web corpus provide updated information about the emerging entities, the
retrieved texts can be noisy, irrelevant, thus leading to inaccurate answers.
Effectively filtering out noisy articles as well as bad answers is the key to
improving extraction accuracy. Knowledge graph (KG), which contains rich, well
organized information about entities, provides a good resource to address the
challenge. In this work, we propose a knowledge-guided reinforcement learning
(RL) framework for open attribute value extraction. Informed by relevant
knowledge in KG, we trained a deep Q-network to sequentially compare extracted
answers to improve extraction accuracy. The proposed framework is applicable to
different information extraction system. Our experimental results show that our
method outperforms the baselines by 16.5 - 27.8\%.

利用知识图谱引导的强化学习框架，对开放属性值提取任务进行了研究，结果表明该方法在不同的信息提取系统中都优于基准线 16.5 - 27.8％。

基于知识引导的强化学习开放属性值提取

Knowledge-guided Open Attribute Value Extraction with Reinforcement  Learning

Answering questions from university admission exams (Gaokao in Chinese) is a
challenging AI task since it requires effective representation to capture
complicated semantic relations between questions and answers. In this work, we
propose a hybrid neural model for deep question-answering task from history
examinations. Our model employs a cooperative gated neural network to retrieve
answers with the assistance of extra labels given by a neural turing machine
labeler. Empirical study shows that the labeler works well with only a small
training dataset and the gated mechanism is good at fetching the semantic
representation of lengthy answers. Experiments on question answering
demonstrate the proposed model obtains substantial performance gains over
various neural model baselines in terms of multiple evaluation metrics.

本研究提出了一种基于深度学习的问题 - 答案模型，采用协作门控神经网络协助神经图灵机标注器检索答案，并在历史考试的数据集上取得了较好的成果，相比各种神经模型基准，在多项评估指标上实现了实质性的性能提升。