The paper describes a system that uses large language model (LLM) technology
to support the automatic learning of new entries in an intelligent agent's
semantic lexicon. The process is bootstrapped by an existing non-toy lexicon
and a natural language generator that converts formal, ontologically-grounded
representations of meaning into natural language sentences. The learning method
involves a sequence of LLM requests and includes an automatic quality control
step. To date, this learning method has been applied to learning multiword
expressions whose meanings are equivalent to those of transitive verbs in the
agent's lexicon. The experiment demonstrates the benefits of a hybrid learning
architecture that integrates knowledge-based methods and resources with both
traditional data analytics and LLMs.

该论文介绍了一种利用大型语言模型技术支持智能代理语义词典中新条目的自动学习的系统。这种学习方法通过现有的非玩具词典和自然语言生成器引导启动，将意义的表达按基本本体建立转换为自然语言句子。此学习方法已应用于学习多词表达式，其含义与智能代理语义词典中的及物动词等效。实验展示了一种融合基于知识的方法、资源、传统数据分析和大型语言模型的混合学习架构的优势。

利用 LLMs 实现内容为中心的认知代理的知识获取自动化

Automating Knowledge Acquisition for Content-Centric Cognitive Agents  Using LLMs

Our goal is to answer questions about paragraphs describing processes (e.g.,
photosynthesis). Texts of this genre are challenging because the effects of
actions are often implicit (unstated), requiring background knowledge and
inference to reason about the changing world states. To supply this knowledge,
we leverage VerbNet to build a rulebase (called the Semantic Lexicon) of the
preconditions and effects of actions, and use it along with commonsense
knowledge of persistence to answer questions about change. Our evaluation shows
that our system, ProComp, significantly outperforms two strong reading
comprehension (RC) baselines. Our contributions are two-fold: the Semantic
Lexicon rulebase itself, and a demonstration of how a simulation-based approach
to machine reading can outperform RC methods that rely on surface cues alone.
Since this work was performed, we have developed neural systems that
outperform ProComp, described elsewhere (Dalvi et al., NAACL'18). However, the
Semantic Lexicon remains a novel and potentially useful resource, and its
integration with neural systems remains a currently unexplored opportunity for
further improvements in machine reading about processes.

使用语义词汇表 (Semantic Lexicon) 构建规则库并结合常识知识，采用基于模拟的方法进行机器阅读的研究。实验结果表明，该方法在描述过程方面的任务上优于两个强基线阅读理解方法 (read comprehension baselines)。

利用 VerbNet 预测程序文本中动作的影响

What Happened? Leveraging VerbNet to Predict the Effects of Actions in  Procedural Text

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted
Examples), that acquires a semantic lexicon from a corpus of sentences paired
with semantic representations. The lexicon learned consists of phrases paired
with meaning representations. WOLFIE is part of an integrated system that
learns to transform sentences into representations such as logical database
queries. Experimental results are presented demonstrating WOLFIE's ability to
learn useful lexicons for a database interface in four different natural
languages. The usefulness of the lexicons learned by WOLFIE are compared to
those acquired by a similar system, with results favorable to WOLFIE. A second
set of experiments demonstrates WOLFIE's ability to scale to larger and more
difficult, albeit artificially generated, corpora. In natural language
acquisition, it is difficult to gather the annotated data needed for supervised
learning; however, unannotated data is fairly plentiful. Active learning
methods attempt to select for annotation and training only the most informative
examples, and therefore are potentially very useful in natural language
applications. However, most results to date for active learning have only
considered standard classification tasks. To reduce annotation effort while
maintaining accuracy, we apply active learning to semantic lexicons. We show
that active learning can significantly reduce the number of annotated examples
required to achieve a given level of performance.

该论文研究了一个名为 WOLFIE 的系统，该系统从配对有语义表示的句子语料库中获取语义词汇表，该词汇表由短语和意义表示配对组成，并且可以缩放到更大和更困难的语料库中，同时还表明了活跃学习具有在自然语言任务中大量降低注释所需数量的能力。