4,895 research outputs found
Incorporating Structured Commonsense Knowledge in Story Completion
The ability to select an appropriate story ending is the first step towards
perfect narrative comprehension. Story ending prediction requires not only the
explicit clues within the context, but also the implicit knowledge (such as
commonsense) to construct a reasonable and consistent story. However, most
previous approaches do not explicitly use background commonsense knowledge. We
present a neural story ending selection model that integrates three types of
information: narrative sequence, sentiment evolution and commonsense knowledge.
Experiments show that our model outperforms state-of-the-art approaches on a
public dataset, ROCStory Cloze Task , and the performance gain from adding the
additional commonsense knowledge is significant
Open-world Story Generation with Structured Knowledge Enhancement: A Comprehensive Survey
Storytelling and narrative are fundamental to human experience, intertwined
with our social and cultural engagement. As such, researchers have long
attempted to create systems that can generate stories automatically. In recent
years, powered by deep learning and massive data resources, automatic story
generation has shown significant advances. However, considerable challenges,
like the need for global coherence in generated stories, still hamper
generative models from reaching the same storytelling ability as human
narrators. To tackle these challenges, many studies seek to inject structured
knowledge into the generation process, which is referred to as structure
knowledge-enhanced story generation. Incorporating external knowledge can
enhance the logical coherence among story events, achieve better knowledge
grounding, and alleviate over-generalization and repetition problems in
stories. This survey provides the latest and comprehensive review of this
research field: (i) we present a systematical taxonomy regarding how existing
methods integrate structured knowledge into story generation; (ii) we summarize
involved story corpora, structured knowledge datasets, and evaluation metrics;
(iii) we give multidimensional insights into the challenges of
knowledge-enhanced story generation and cast light on promising directions for
future study
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Knowledge Graphs (KGs) play a pivotal role in advancing various AI
applications, with the semantic web community's exploration into multi-modal
dimensions unlocking new avenues for innovation. In this survey, we carefully
review over 300 articles, focusing on KG-aware research in two principal
aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal
tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into
the MMKG realm. We begin by defining KGs and MMKGs, then explore their
construction progress. Our review includes two primary task categories:
KG-aware multi-modal learning tasks, such as Image Classification and Visual
Question Answering, and intrinsic MMKG tasks like Multi-modal Knowledge Graph
Completion and Entity Alignment, highlighting specific research trajectories.
For most of these tasks, we provide definitions, evaluation benchmarks, and
additionally outline essential insights for conducting relevant research.
Finally, we discuss current challenges and identify emerging trends, such as
progress in Large Language Modeling and Multi-modal Pre-training strategies.
This survey aims to serve as a comprehensive reference for researchers already
involved in or considering delving into KG and multi-modal learning research,
offering insights into the evolving landscape of MMKG research and supporting
future work.Comment: Ongoing work; 41 pages (Main Text), 55 pages (Total), 11 Tables, 13
Figures, 619 citations; Paper list is available at
https://github.com/zjukg/KG-MM-Surve
Transferring Procedural Knowledge across Commonsense Tasks
Stories about everyday situations are an essential part of human
communication, motivating the need to develop AI agents that can reliably
understand these stories. Despite the long list of supervised methods for story
completion and procedural understanding, current AI has no mechanisms to
automatically track and explain procedures in unseen stories. To bridge this
gap, we study the ability of AI models to transfer procedural knowledge to
novel narrative tasks in a transparent manner. We design LEAP: a comprehensive
framework that integrates state-of-the-art modeling architectures, training
regimes, and augmentation strategies based on both natural and synthetic
stories. To address the lack of densely annotated training data, we devise a
robust automatic labeler based on few-shot prompting to enhance the augmented
data. Our experiments with in- and out-of-domain tasks reveal insights into the
interplay of different architectures, training regimes, and augmentation
strategies. LEAP's labeler has a clear positive impact on out-of-domain
datasets, while the resulting dense annotation provides native explainability
A Survey on Knowledge-Enhanced Pre-trained Language Models
Natural Language Processing (NLP) has been revolutionized by the use of
Pre-trained Language Models (PLMs) such as BERT. Despite setting new records in
nearly every NLP task, PLMs still face a number of challenges including poor
interpretability, weak reasoning capability, and the need for a lot of
expensive annotated data when applied to downstream tasks. By integrating
external knowledge into PLMs,
\textit{\underline{K}nowledge-\underline{E}nhanced \underline{P}re-trained
\underline{L}anguage \underline{M}odels} (KEPLMs) have the potential to
overcome the above-mentioned limitations. In this paper, we examine KEPLMs
systematically through a series of studies. Specifically, we outline the common
types and different formats of knowledge to be integrated into KEPLMs, detail
the existing methods for building and evaluating KEPLMS, present the
applications of KEPLMs in downstream tasks, and discuss the future research
directions. Researchers will benefit from this survey by gaining a quick and
comprehensive overview of the latest developments in this field.Comment: 19 pages, 12 figures, 192 reference
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves
in the field of natural language processing and artificial intelligence, due to
their emergent ability and generalizability. However, LLMs are black-box
models, which often fall short of capturing and accessing factual knowledge. In
contrast, Knowledge Graphs (KGs), Wikipedia and Huapu for example, are
structured knowledge models that explicitly store rich factual knowledge. KGs
can enhance LLMs by providing external knowledge for inference and
interpretability. Meanwhile, KGs are difficult to construct and evolving by
nature, which challenges the existing methods in KGs to generate new facts and
represent unseen knowledge. Therefore, it is complementary to unify LLMs and
KGs together and simultaneously leverage their advantages. In this article, we
present a forward-looking roadmap for the unification of LLMs and KGs. Our
roadmap consists of three general frameworks, namely, 1) KG-enhanced LLMs,
which incorporate KGs during the pre-training and inference phases of LLMs, or
for the purpose of enhancing understanding of the knowledge learned by LLMs; 2)
LLM-augmented KGs, that leverage LLMs for different KG tasks such as embedding,
completion, construction, graph-to-text generation, and question answering; and
3) Synergized LLMs + KGs, in which LLMs and KGs play equal roles and work in a
mutually beneficial way to enhance both LLMs and KGs for bidirectional
reasoning driven by both data and knowledge. We review and summarize existing
efforts within these three frameworks in our roadmap and pinpoint their future
research directions.Comment: 29 pages, 25 figure
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