Papers with REG
Prompting Vision-Language Models For Aspect-Controlled Generation of Referring Expressions (2024.findings-naacl)
Copied to clipboard
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. |
NeuralREG: An end-to-end approach to referring expression generation (P18-1)
Copied to clipboard
| 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. |
Referring to what you know and do not know: Making Referring Expression Generation Models Generalize To Unseen Entities (2020.coling-main)
Copied to clipboard
| 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)
Copied to clipboard
| 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)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
| Outcome: | The proposed evaluation makes the models more reliable and discriminable, and improves the quality of the REs. |
Towards Unifying Reference Expression Generation and Comprehension (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for REG and REC have distinct inputs and connections between them . a new model for REg and reprehension is needed to solve these problems . |
| Approach: | They propose a unified model for REG and REC that fuses image, region and text . they propose Vision-conditioned Masked Language Modeling and Text-Conditioned Region Prediction . |
| Outcome: | The proposed model outperforms existing models on REG and REC tasks. |
Reference production in human-computer interaction: Issues for Corpus-based Referring Expression Generation (L18-1)
Copied to clipboard
| 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. |
| Outcome: | The results suggest that human reference production may be affected by the presence of a second (specific) human participant as the receiver of the communication in a number of ways. |
Definite Description Lexical Choice: taking Speaker’s Personality into account (L18-1)
Copied to clipboard
| Challenge: | Referring Expression Generation (REG) lexical choice is the subtask that provides words to express an input meaning representation. |
| Approach: | They propose a personality-dependent lexical choice model for Referring Expression Generation (REG) that provides words to express a given input meaning representation. |
| Outcome: | The proposed model outperforms a standard lexicalisation model based on meaning-to-text mappings and personality information. |
Referring Expression Generation in time-constrained communication (L18-1)
Copied to clipboard
| Challenge: | In game-like applications, an underlying Natural Language Generation system may have to express urgency or other dynamic aspects of a fast-evolving situation as text. |
| Approach: | They propose to use a task of Referring Expression Generation (REG) to generate natural language text from a visual context provided by a video game application. |
| Outcome: | The proposed algorithm outperforms standard approaches to REG in time-constrained communication. |
REG: Retrieval via Emotion Similarity for Guiding Empathetic Dialogue Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Empathy relies on the cognitive capacity to relate to similar past experiences. Existing methods prioritize semantic similarity over emotion characteristics, leading to unempathetic responses. |
| Approach: | They propose a framework that integrates four Emotion Attributes into the retrieval process to ensure explicit emotional alignment. |
| Outcome: | Empirical results show that REG significantly outperforms baselines, offering a robust solution for empathetic generation. |