Knowledge base population seeks to expand knowledge graphs with facts that
are typically extracted from a text corpus. Recently, language models
pretrained on large corpora have been shown to contain factual knowledge that
can be retrieved using cloze-style strategies. Such approach enables zero-shot
recall of facts, showing competitive results in object prediction compared to
supervised baselines. However, prompt-based fact retrieval can be brittle and
heavily depend on the prompts and context used, which may produce results that
are unintended or hallucinatory.We propose to use textual entailment to
validate facts extracted from language models through cloze statements. Our
results show that triple validation based on textual entailment improves
language model predictions in different training regimes. Furthermore, we show
that entailment-based triple validation is also effective to validate candidate
facts extracted from other sources including existing knowledge graphs and text
passages where named entities are recognized.

使用文本蕴涵来验证从语言模型通过填充式语句中提取的事实，结果表明基于文本蕴涵的三元验证改进了语言模型的预测能力。

在对象预测中进行有效三元验证的文本蕴涵

Textual Entailment for Effective Triple Validation in Object Prediction

The output structure of database-like tables, consisting of values structured
in horizontal rows and vertical columns identifiable by name, can cover a wide
range of NLP tasks. Following this constatation, we propose a framework for
text-to-table neural models applicable to problems such as extraction of line
items, joint entity and relation extraction, or knowledge base population. The
permutation-based decoder of our proposal is a generalized sequential method
that comprehends information from all cells in the table. The training
maximizes the expected log-likelihood for a table's content across all random
permutations of the factorization order. During the content inference, we
exploit the model's ability to generate cells in any order by searching over
possible orderings to maximize the model's confidence and avoid substantial
error accumulation, which other sequential models are prone to. Experiments
demonstrate a high practical value of the framework, which establishes
state-of-the-art results on several challenging datasets, outperforming
previous solutions by up to 15%.

提出了一种面向文本到表格的神经模型框架，包括排列基础的解码器，可应用于提取行项、合并实体和关系、或者知识库填充等 NLP 任务中，并在多个挑战性数据集上实现了超越以往解决方法高达 15％的最优效果。

基于编码器 - 解码器模型的表格生成框架

STable: Table Generation Framework for Encoder-Decoder Models

The study of linguistic typology is rooted in the implications we find
between linguistic features, such as the fact that languages with object-verb
word ordering tend to have post-positions. Uncovering such implications
typically amounts to time-consuming manual processing by trained and
experienced linguists, which potentially leaves key linguistic universals
unexplored. In this paper, we present a computational model which successfully
identifies known universals, including Greenberg universals, but also uncovers
new ones, worthy of further linguistic investigation. Our approach outperforms
baselines previously used for this problem, as well as a strong baseline from
knowledge base population.

本文介绍了一种使用计算模型发现语言基础规律的方法，其表现优于以往的基线模型，成功识别了已知的 Greenberg 大一统规律，同时还发现了有待进一步研究的新规律。

探索类型学知识库中的概率推论

Uncovering Probabilistic Implications in Typological Knowledge Bases

A crucial aspect of a knowledge base population system that extracts new
facts from text corpora, is the generation of training data for its relation
extractors. In this paper, we present a method that maximizes the effectiveness
of newly trained relation extractors at a minimal annotation cost. Manual
labeling can be significantly reduced by Distant Supervision, which is a method
to construct training data automatically by aligning a large text corpus with
an existing knowledge base of known facts. For example, all sentences
mentioning both 'Barack Obama' and 'US' may serve as positive training
instances for the relation born_in(subject,object). However, distant
supervision typically results in a highly noisy training set: many training
sentences do not really express the intended relation. We propose to combine
distant supervision with minimal manual supervision in a technique called
feature labeling, to eliminate noise from the large and noisy initial training
set, resulting in a significant increase of precision. We further improve on
this approach by introducing the Semantic Label Propagation method, which uses
the similarity between low-dimensional representations of candidate training
instances, to extend the training set in order to increase recall while
maintaining high precision. Our proposed strategy for generating training data
is studied and evaluated on an established test collection designed for
knowledge base population tasks. The experimental results show that the
Semantic Label Propagation strategy leads to substantial performance gains when
compared to existing approaches, while requiring an almost negligible manual
annotation effort.

本研究讨论了使用远程监督和特征标注相结合的方法解决知识库填充问题，提出了一种名为语义标签传播的方法，并在实验中显示出了显著的性能提升。