Papers with SPICE
Adding SPICE to Life: Speaker Profiling in Multiparty Conversations (2024.lrec-main)
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| Challenge: | Prior studies assumed the speaker’s persona’s immediate availability, a premise not universally applicable. |
| Approach: | They propose to synthesize persona attributes for each dialogue participant by combining three core tasks: persona discovery, persona-type identification, and persona value extraction. |
| Outcome: | The proposed task synthesizes persona attributes for each dialogue participant . the resulting model is compared against a baseline model and the proposed model is robust. |
On the Evaluation of Vision-and-Language Navigation Instructions (2021.eacl-main)
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| Challenge: | Existing instruction generators have not been evaluated using human wayfinders . BLEU, ROUGE, METEOR and CIDEr are ineffective for evaluating grounded navigation instructions. |
| Approach: | They propose an instruction-trajectory compatibility model that operates without reference instructions to improve wayfinding performance. |
| Outcome: | The proposed model shows the highest correlation with human wayfinding outcomes when scoring individual instructions. |
KFCNet: Knowledge Filtering and Contrastive Learning for Generative Commonsense Reasoning (2021.findings-emnlp)
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| Challenge: | Pre-trained language models have led to substantial gains over a broad range of NLP tasks, but have limitations for high-quality tasks such as commonsense generation and ad keyword generation. |
| Approach: | They propose a Knowledge Filtering and Contrastive learning Network which references external knowledge and achieves better generation performance. |
| Outcome: | The proposed model outperforms the current state of the art on the CommonGen benchmark by a large margin. |
DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement (2025.emnlp-main)
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| Challenge: | Current approaches typically merge sentence-level parsing outputs for discourse input, resulting in fragmented graphs and degraded downstream performance. |
| Approach: | They propose a task for discourse-level text scene graph parsing that merges sentence-level outputs for discourse input and propose 'DiscoSG' a dataset of 400 expert-annotated and 8,430 synthesised multi-sentence caption-graph pairs is used to test the new task. |
| Outcome: | The proposed task improves SPICE by 30% over the baseline while achieving 86 faster inference than existing models. |
Multimodal Contextualized Semantic Parsing from Speech (2024.acl-long)
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| Challenge: | Towards this goal, we introduce Semantic Parsing in Contextual Environments (SPICE) task designed to enhance artificial agents’ contextual awareness by integrating multimodal inputs with prior contexts. |
| Approach: | They introduce a task designed to enhance artificial agents’ contextual awareness by integrating multimodal inputs with prior contexts. |
| Outcome: | The proposed task is based on the VG-SPICE dataset and the Audio-Vision Dialogue Scene Parser (AViD-SP) it allows agents to maintain their contextual state within a structured, dense information framework that is scalable and interpretable . |
CLIPScore: A Reference-free Evaluation Metric for Image Captioning (2021.emnlp-main)
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| Challenge: | Image captioning relies on reference-based automatic evaluations, but references are expensive to collect and comparing against multiple human-authored captions is insufficient. |
| Approach: | They propose a reference-free metric that can be used for automatic caption evaluation without references. |
| Outcome: | The proposed model outperforms existing metrics on image-text compatibility and a reference-augmented version achieves even higher correlation with human judgements. |
CLAIR: Evaluating Image Captions with Large Language Models (2023.emnlp-main)
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| Challenge: | Existing measures for image caption evaluation fail to capture dimensions of similarity . a novel method that leverages the zero-shot language modeling capabilities of large language models (LLMs) demonstrates a stronger correlation with human judgments of caption quality compared to existing measures. |
| Approach: | They propose a method that leverages the zero-shot language modeling capabilities of large language models to evaluate captions. |
| Outcome: | The proposed method shows a stronger correlation with human judgments of caption quality compared to other measures. |
Divergent Thinking: Escape the Homogeneity Trap in Generative Commonsense Reasoning (2026.findings-acl)
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| Challenge: | Generative commonsense reasoning requires models to synthesize coherent narratives that satisfy lexical constraints and commonsensical logic. |
| Approach: | They propose a framework that allows for deep semantic diversity rather than surface-level lexical variation. |
| Outcome: | The proposed framework achieves over 10% improvement in overall accuracy on NoRa and SPICE score on CommonGen-Lite. |