Towards Comprehensive Description Generation from Factual Attribute-value Tables (P19-1)
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| Challenge: | Existing models for comprehensive descriptions for factual attribute-value tables might suffer from missing key attributes and groundless information problems. |
| Approach: | They propose a force attention method to encourage the generator to pay more attention to uncovered attributes to avoid potential key attributes missing. |
| Outcome: | The proposed model outperforms the state-of-the-art baselines on automatic and human evaluation. |
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| Challenge: | Knowledge-enriched text generation poses unique challenges in modeling and learning . a roadmap will outline the state-of-the-art methods to tackle these challenges . |
| Approach: | They propose a roadmap to tackle the challenges of knowledge-enriched text generation . they will dive deep into various technical components to illustrate how to represent knowledge . |
| Outcome: | This tutorial outlines the state-of-the-art methods to tackle the problem . it aims to show how to represent knowledge, feed knowledge into a generation model, evaluate results . |
Improving Factual Consistency Between a Response and Persona Facts (2021.eacl-main)
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| Challenge: | Neural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with persona facts. |
| Approach: | They propose to fine-tune these models by reinforcement learning and an efficient reward function that explicitly captures the consistency between a response and persona facts as well as semantic plausibility. |
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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. |
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FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge (2023.emnlp-main)
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| Challenge: | Existing factuality evaluation models are not robust, especially with respect to entity and relation errors in new domains. |
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Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects (D19-1)
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| Challenge: | Existing approaches to generating reviews struggle to generate justifications that are relevant to users’ decision-making process. |
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BioGen: Generating Biography Summary under Table Guidance on Wikipedia (2021.findings-acl)
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| Challenge: | Existing methods for summarizing text have not captured the salient information from an article. |
| Approach: | They propose a table-guided abstractive biography summarization that utilizes factual tables to capture important information and generate a summary of a biography. |
| Outcome: | The proposed method is the first large-scale biography summarization dataset with tables. |
Facts2Story: Controlling Text Generation by Key Facts (2020.coling-main)
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| Challenge: | Existing methods for story generation struggle with staying coherent for long periods of time. |
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Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation (2022.findings-emnlp)
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| Challenge: | Large pre-trained language models have enabled open-ended generation frameworks to tackle a variety of tasks beyond data-to-text generation. |
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Towards Table-to-Text Generation with Numerical Reasoning (2021.acl-long)
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| Challenge: | Recent studies have shown improvement in generating descriptive text from structured data. |
| Approach: | They propose a framework for numerical table-to-text generation based on numerical reasoning . they use a pre-trained model and a copy mechanism to fine-tune the models to produce fluent text . |
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Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints (2020.acl-main)
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| Challenge: | Existing methods for text generation ignore faithfulness between generated text and table . current methods ignore faithfulity, leading to generated information that goes beyond table content . |
| Approach: | They propose a Transformer-based generation framework to enforce faithfulness between generated text and table . they propose metric to evaluate faithfulness and automatic metric for automatic generating . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in automatic evaluations and human evaluations. |