| Challenge: | Referring Expression Generation models typically rely on features such as salience and grammatical function to make decisions about form and content. |
| Approach: | They propose a new approach that makes decisions about form and content in one go . they use a delexicalized version of the WebNLG corpus to test the approach . |
| Outcome: | The proposed approach significantly improves over two strong baselines. |
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Referring to what you know and do not know: Making Referring Expression Generation Models Generalize To Unseen Entities (2020.coling-main)
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| Challenge: | Data-to-text Natural Language Generation (NLG) is a computational process of generating natural language from non-linguistic data. |
| Approach: | They propose two extensions to a state-of-the-art encoder-decoder REG model that generates referring expressions to unseen entities. |
| Outcome: | The proposed model generates more meaningful referring expressions to unseen entities than the original system and related work. |
Referring Expression Generation Using Entity Profiles (D19-1)
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| Challenge: | Existing REG systems rely on entity-specific supervised training, which means they cannot handle entities not seen during training. |
| Approach: | They propose a deep neural network model that encodes both the local context and an external profile of the entity to generate reference realizations. |
| Outcome: | The proposed model outperforms baselines on three different splits of the WebNLG dataset according to automatic and human evaluations. |
Non-neural Models Matter: a Re-evaluation of Neural Referring Expression Generation Systems (2022.acl-long)
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| Challenge: | In recent years, neural models have outperformed rule-based and classic approaches in NLG. |
| Approach: | They evaluate two English datasets and evaluate their performance using automatic and human evaluations. |
| Outcome: | The proposed model outperforms rule-based and classic approaches on two English datasets and is compared with human-based models. |
Intrinsic Task-based Evaluation for Referring Expression Generation (2024.acl-long)
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| Challenge: | Referring Expression Generation (REG) models generate referring expressions that refer to referents at different points in a discourse. |
| Approach: | They propose to use a purely ratings-based human evaluation to evaluate REG models by completing two meta-level tasks. |
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Prompting Vision-Language Models For Aspect-Controlled Generation of Referring Expressions (2024.findings-naacl)
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Danfeng Guo, Sanchit Agarwal, Arpit Gupta, Jiun-Yu Kao, Emre Barut, Tagyoung Chung, Jing Huang, Mohit Bansal
| Challenge: | Referring Expression Generation (REG) is the task of generating a descriptive caption that uniquely identifies a given target in the scene. |
| Approach: | They propose an Aspect-Controlled REG task which requires generating a referring expression conditioned on the input aspect(s) by changing the input input such as color, location, action etc. |
| Outcome: | The proposed model beats all prior works in the CIDEr score and achieves comparable performance to training with 100% of real data. |
Reference production in human-computer interaction: Issues for Corpus-based Referring Expression Generation (L18-1)
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| Challenge: | Referring Expression Generation studies often use web-based data collection tasks without a particular addressee in mind. |
| Approach: | They developed a parallel corpus of monologue and dialogue referring expressions and an annotated corpus to compare instances produced in both modes of communication. |
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A Linguistic Perspective on Reference: Choosing a Feature Set for Generating Referring Expressions in Context (2020.coling-main)
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| Challenge: | Various studies have raised the question of which factors play a role in the choice of referring expressions. |
| Approach: | They propose to use Feature Importance Ranking and Sequential Forward Search to evaluate the “importance” of features in machine learning algorithms for selecting the form of a referring expression in discourse context. |
| Outcome: | The proposed feature set includes 6 features from 4 classes, namely grammatical role, inherent features of the referent, antecedent form and recency. |
Sentence-Level Content Planning and Style Specification for Neural Text Generation (D19-1)
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| Challenge: | Recent advances in text generation systems often produce incoherent and unfaithful outputs . a novel automated text generation system takes into account content selection, text planning, and surface realization. |
| Approach: | They propose an end-to-end trained two-step text generation model that considers sentence-level content planners and language styles. |
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Neural Text Generation in Stories Using Entity Representations as Context (N18-1)
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| Challenge: | Existing models of text generation that explicitly represent entities are based on the use of words and entities. |
| Approach: | They propose a neural model that explicitly represents entities mentioned in the text . they use vectors that are updated as the text proceeds to improve automatic evaluations . |
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Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity (2020.coling-main)
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| Challenge: | End-to-end neural data-totext generation has faced challenges generalizing to new domains and generating semantically consistent text. |
| Approach: | They propose a neural data-to-text generation system that makes minimal assumptions about the data representation and target domain. |
| Outcome: | The proposed system achieves state of the art results on four major D2T datasets with better semantic fidelity than the state-of-the-art methods. |