We propose a simple yet effective strategy to incorporate event knowledge
extracted from event trigger annotations via posterior regularization to
improve the event reasoning capability of mainstream question-answering (QA)
models for event-centric QA. In particular, we define event-related knowledge
constraints based on the event trigger annotations in the QA datasets, and
subsequently use them to regularize the posterior answer output probabilities
from the backbone pre-trained language models used in the QA setting. We
explore two different posterior regularization strategies for extractive and
generative QA separately. For extractive QA, the sentence-level event knowledge
constraint is defined by assessing if a sentence contains an answer event or
not, which is later used to modify the answer span extraction probability. For
generative QA, the token-level event knowledge constraint is defined by
comparing the generated token from the backbone language model with the answer
event in order to introduce a reward or penalty term, which essentially adjusts
the answer generative probability indirectly. We conduct experiments on two
event-centric QA datasets, TORQUE and ESTER. The results show that our proposed
approach can effectively inject event knowledge into existing pre-trained
language models and achieves strong performance compared to existing QA models
in answer evaluation. Code and models can be found:
this https URL

该论文提出了一种简单而有效的策略，通过后验正则化将从事件触发器注释中提取的事件知识纳入主流问答模型中，从而提高面向事件的问答模型的事件推理能力。

事件知识的后验正则化融合在基于事件的问答中

Event Knowledge Incorporation with Posterior Regularization for  Event-Centric Question Answering

Natural language processing applications, such as conversational agents and
their question-answering capabilities, are widely used in the real world.
Despite the wide popularity of large language models (LLMs), few real-world
conversational agents take advantage of LLMs. Extensive resources consumed by
LLMs disable developers from integrating them into end-user applications. In
this study, we leverage an inverted indexing mechanism combined with LLMs to
improve the efficiency of question-answering models for closed-domain
questions. Our experiments show that using the index improves the average
response time by 97.44%. In addition, due to the reduced search scope, the
average BLEU score improved by 0.23 while using the inverted index.

本研究基于倒排索引的机制，结合大型语言模型，以提高封闭领域中的问答模型的效率，实验证明使用倒排索引平均响应时间提高了 97.44％，同时由于减少了搜索范围，平均 BLEU 分数提高了 0.23。

通过倒排索引加速语言模型的问答任务

Speeding Up Question Answering Task of Language Models via Inverted Index

We introduce Mintaka, a complex, natural, and multilingual dataset designed
for experimenting with end-to-end question-answering models. Mintaka is
composed of 20,000 question-answer pairs collected in English, annotated with
Wikidata entities, and translated into Arabic, French, German, Hindi, Italian,
Japanese, Portuguese, and Spanish for a total of 180,000 samples. Mintaka
includes 8 types of complex questions, including superlative, intersection, and
multi-hop questions, which were naturally elicited from crowd workers. We run
baselines over Mintaka, the best of which achieves 38% hits@1 in English and
31% hits@1 multilingually, showing that existing models have room for
improvement. We release Mintaka at this https URL.

介绍了 Mintaka，这是一个设计用于评估端到端问答模型的复杂、自然且多语言的数据集，包含 20,000 个问题 - 答案对，包含 8 种类型的复杂问题，其中包括最高级，交集和多跳问题，并在 9 种不同的语言（英语，阿拉伯语，法语，德语，印地语，意大利语，日语，葡萄牙语和西班牙语）进行了翻译。针对 Mintaka 运行了基线，其中最好的英文命中率 @1 为 38％，多语言命中率 @1 为 31％，表明现有模型仍有改进空间。