Challenge: tutorial aims to explain the basic concepts of translating structured data into natural language . Various solutions for structured data translation will be discussed .
Approach: tutorial aims to cover foundational, methodological, and system development aspects of translating structured data into natural language . Various solutions starting from traditional rule based/heuristic driven and modern data-driven will be discussed .
Outcome: The tutorial aims to convey challenges and nuances in structured data translation, data representation techniques, and domain adaptable solutions for translation of the data into natural language form.

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Challenge: Knowledge graph construction has appealed to the NLP community but has encountered similar issues such as efficiency and robustness.
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Challenge: Adapting existing approaches for converting natural language to SQL encounters hurdles due to distinct nature of GQL compared to SQL.
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Event-Centric Natural Language Processing (2021.acl-tutorials)

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Challenge: This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations.
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Goal-Driven Data Story, Narrations and Explanations (2025.naacl-industry)

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Challenge: Unlike existing tools, our system addresses the ambiguity of vague, multi-line queries, setting a new benchmark in data storytelling by tackling complexities no existing system comprehensively handles.
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Challenge: Existing work on data-to-text generation focused on domain-specific benchmark datasets.
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A Decade of Knowledge Graphs in Natural Language Processing: A Survey (2022.aacl-main)

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Challenge: Knowledge graphs (KGs) are a representation of semantic relations between entities . despite their popularity, there is still no general understanding of what exactly a KG is or for what tasks it is applicable.
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Deep Learning on Graphs for Natural Language Processing (2021.naacl-tutorials)

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Challenge: Graph Neural Networks (GNNs) are powerful tools for non-Euclidean data modeling and are used in many graph-related NLP tasks.
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Graph-based Deep Learning in Natural Language Processing (D19-2)

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From Text to Context: Contextualizing Language with Humans, Groups, and Communities for Socially Aware NLP (2024.naacl-tutorials)

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Challenge: This tutorial will cover the latest techniques and libraries for doing so at each level of analysis.
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An efficient method for Natural Language Querying on Structured Data (2023.acl-industry)

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Challenge: a new approach to NLQ on structured data is based on text-to-SQL type semantic parsing . domain classification, domain classification and domain classification are the main tasks . semantic parsed queries are less common when information is in structured form .
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