Papers with DICE
Standardizing Distress Analysis: Emotion-Driven Distress Identification and Cause Extraction (DICE) in Multimodal Online Posts (2023.emnlp-main)
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| Challenge: | Existing methods for identifying hate speech have been limited to analyzing textual content. |
| Approach: | They propose a method for distress identification and cause extraction from social media posts using emotional information. |
| Outcome: | The proposed method improves F1 and ROS scores by 1.95% and 3% relative to the best-performing baseline. |
DICE: Structured Reasoning in LLMs through SLM-Guided Chain-of-Thought Correction (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) often prioritize reasoning over adherence to detailed instructions due to high computational costs and limited parameter access. |
| Approach: | They propose a lightweight framework that guides small language models to refine LLMs’ outputs through chain-of-thought correction. |
| Outcome: | The proposed framework improves the average format accuracy and content correctness of LLM outputs by 35.4% and 29.4%, respectively, achieving state-of-the-art (SOTA) performance over other competitive baselines. |
Rolling the DICE on Idiomaticity: How LLMs Fail to Grasp Context (2025.acl-long)
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| Challenge: | Existing models fail to resolve idiomaticity when it depends on contextual understanding . idiom frequency influences performance but does not guarantee accurate interpretation. |
| Approach: | They propose a novel contrastive dataset to assess whether large language models can effectively leverage context to disambiguate idiomatic meanings. |
| Outcome: | The proposed model performs better on sentences deemed more likely by the model . collocational frequency and sentence probability influence performance but not accuracy . |
Methods for Numeracy-Preserving Word Embeddings (2020.emnlp-main)
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Dhanasekar Sundararaman, Shijing Si, Vivek Subramanian, Guoyin Wang, Devamanyu Hazarika, Lawrence Carin
| Challenge: | Word embedding models capture semantic relationships between words but fail to capture numerical properties associated with numbers. |
| Approach: | They propose a method to assign and learn embeddings for numbers using word embedders. |
| Outcome: | The proposed model outperforms pre-trained word embedding models across multiple examples of two tasks. |
Inter-sentence Context Modeling and Structure-aware Representation Enhancement for Conversational Sentiment Quadruple Extraction (2025.emnlp-main)
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| Challenge: | Existing studies struggle to capture complete dialogue semantics due to inadequate inter-utterance modeling and the underutilization of dialogue structure. |
| Approach: | They propose a model to extract dialogue aspect sentiment quadruples from dialogues using a sentence-by-sentence encoding module. |
| Outcome: | The proposed model extracts quadruples of target-aspect-opinion-sentiment from dialogues. |
DICE: Data-Efficient Clinical Event Extraction with Generative Models (2023.acl-long)
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| Challenge: | EE tasks target specific domains with vague entity boundaries, resulting in a lack of training data. |
| Approach: | They propose a robust and data-efficient generative model for clinical event extraction . they frame event extraction as a conditional generation problem and introduce a contrastive learning objective to decide the boundaries of biomedical mentions. |
| Outcome: | The proposed model is robust and data-efficient for clinical event extraction . it trains an auxiliary mention identification task and event extraction tasks to better identify entity mention boundaries . |
ToMMeR - Efficient Entity Mention Detection from Large Language Models (2026.acl-long)
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| Challenge: | Existing methods to detect text spans that refer to entities are often conflated with entity typing in a single joint task. |
| Approach: | They propose a lightweight model that probes mention detection capabilities from early LLM layers. |
| Outcome: | The proposed model achieves 93% recall zero-shot with 90% precision under human-calibrated LLM-judge protocol . |