Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization (N18-1)
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| Challenge: | a proposed model for generating natural language descriptions is too generic and does not exploit task specific characteristics. |
| Approach: | They propose a model which uses a fused bifocal attention mechanism to exploit micro and macro level information and a gated orthogonalization mechanism to ensure that a field is remembered for a few time steps and then forgotten. |
| Outcome: | The proposed model improves on a recently released dataset with two similar datasets for French and German. |
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| 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 . |
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Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills (2022.acl-long)
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| Challenge: | Large pre-trained language models struggle in tasks that require reasoning . recent work shows that they struggle in performing symbolic reasoning operations without substantial amounts of additional data. |
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An efficient method for Natural Language Querying on Structured Data (2023.acl-industry)
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STRUCTSUM Generation for Faster Text Comprehension (2024.acl-long)
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| Challenge: | Current large language models (LLMs) fail to adequately structure and organize information in a way that facilitates comprehension. |
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SKILL: Structured Knowledge Infusion for Large Language Models (2022.naacl-main)
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| Challenge: | Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. |
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Scalable Construction and Reasoning of Massive Knowledge Bases (N18-6)
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| Challenge: | Existing knowledge mining systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. |
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Towards Comprehensive Description Generation from Factual Attribute-value Tables (P19-1)
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Structured Object Language Modeling (SO-LM): Native Structured Objects Generation Conforming to Complex Schemas with Self-Supervised Denoising (2024.emnlp-industry)
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Modeling Context With Linear Attention for Scalable Document-Level Translation (2022.findings-emnlp)
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| Challenge: | Document-level machine translation models lack quadratic complexity in the sequence length due to their attention layers. |
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Advances in Pre-Training Distributed Word Representations (L18-1)
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| Challenge: | Pre-trained word representations are a building block of many Natural Language Processing and Machine Learning applications. |
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