A Knowledge-Injected Curriculum Pretraining Framework for Question Answering

Основні поняття

Proposing a comprehensive framework for knowledge learning and complex reasoning in question answering tasks.

Анотація

The paper introduces the Knowledge-Injected Curriculum Pretraining framework (KICP) to enhance knowledge-based question answering by injecting knowledge from KGs into language models. The framework consists of three key components: knowledge injection, knowledge adaptation, and curriculum reasoning. KICP aims to improve language understanding with KGs and enable complex human-like reasoning in QA tasks. By generating KG-centered pretraining corpus, adapting LM with a trainable adapter, and following a curriculum approach for training LM from easy to hard reasoning tasks, KICP achieves higher performance and generalization ability on real-world datasets.

Статистика

KBQA is a key task in NLP research [33].
Incorporating pretrained LMs with KGs improves performance [21].
KICP outperforms other methods on four real-world datasets.

Цитати

Ключові висновки, отримані з

A Knowledge-Injected Curriculum Pretraining Framework for Question Answering

by Xin Lin,Tian... о arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.09712.pdf

Глибші Запити

How does the KICP framework address the limitations of existing methods?

The KICP framework addresses the limitations of existing methods in several ways:

Generalization: Existing methods for converting KGs into corpus often rely on specific techniques and resources, limiting their flexibility and applicability. The KICP framework provides a general approach that can work with different detailed implementations, allowing for more versatile applications.

Knowledge Injection: By directly converting KG triples into sentences to construct the pretraining corpus, KICP ensures comprehensive knowledge learning by injecting knowledge into the LM. This overcomes the limitation of using KGs as supplementary data and covers all information from the KG.

Knowledge Adaptation: The KA module in KICP reduces negative impacts caused by differences between generated and natural corpora by keeping the NLU ability of LM intact while learning knowledge from the generated corpus with a trainable adapter.

Curriculum Reasoning: The CR module in KICP follows human reasoning patterns to construct corpora requiring complex reasoning skills. By training LM from easy to hard in a curriculum manner, it promotes model learning and enables complex reasoning abilities beyond simple language understanding tasks.

What are the potential applications of the KICP framework beyond question answering tasks?

The potential applications of the Knowledge-Injected Curriculum Pretraining (KICP) framework extend beyond question answering tasks to various areas within natural language processing (NLP) research:

Text Generation: The structured approach of injecting knowledge from external sources like KGs can enhance text generation models' performance by providing contextually relevant information during generation processes.

Information Retrieval: Incorporating external knowledge sources through pretraining frameworks like KICP can improve information retrieval systems' accuracy and relevance by leveraging domain-specific facts during search queries.

Sentiment Analysis: Utilizing curated knowledge graphs within sentiment analysis models could lead to more nuanced sentiment classification based on contextual understanding derived from external sources.

Language Translation: Enhancing machine translation models with additional background information encoded through pretraining frameworks like KICP may result in more accurate translations that consider cultural nuances or domain-specific terminology.

How can the concept of curriculum reasoning be applied to other areas of natural language processing?

The concept of curriculum reasoning introduced in frameworks like KICP can be applied across various domains within natural language processing (NLP) research:

Summarization Tasks:

Gradually increasing complexity levels when training summarization models could help them learn how to distill essential information effectively.

Named Entity Recognition:

Introducing progressively challenging entity recognition tasks during training could improve models' ability to identify entities accurately under varying contexts.

Semantic Parsing:

Structuring semantic parsing training data sets with incremental difficulty levels could train models better at interpreting user intents expressed through varied linguistic forms.

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