Papers with interaction
Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation (N19-4)
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| Challenge: | a neural narrative generation system interacts with humans to generate stories . a recent resurgence of interest in collaborative storytelling has led to new approaches . |
| Approach: | They propose a neural narrative generation system that interacts with humans to generate stories. |
| Outcome: | The proposed system improves story quality and user engagement under time constraints. |
DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding (2026.findings-acl)
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| Challenge: | Autoregressive (AR) large audio language models are expensive in data and computation . prior work shows diffusion-based LALMs can improve audio understanding under matched settings . |
| Approach: | They propose a diffusion-based LALM that upgrades the speech encoder and employs dual semantic and acoustic adapters. |
| Outcome: | a new model improves over existing autoregressive large language models and is competitive to strong AR models . the proposed model can make use of limited training data and improve inference efficiency . a recent study shows that diffusion-based models can improve audio understanding . |
ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction (2020.acl-main)
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| Challenge: | a new task, called emotion-cause pair extraction, has emerged in text emotion analysis . a 2D representation scheme is proposed to represent the emotion-case pairs . |
| Approach: | They propose a 2D approach to represent emotion-cause pairs by a 3D representation scheme. |
| Outcome: | The proposed approach improves the state-of-the-art on the emotion cause corpus . the proposed approach is based on a two-step framework with flaws . |
Modularized Transfomer-based Ranking Framework (2020.emnlp-main)
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| Challenge: | Recent innovations in Transformer-based ranking models have advanced the state-of-the-art in information retrieval. |
| Approach: | They propose to modularize a Transformer ranker into separate modules for text representation and interaction. |
| Outcome: | The proposed model is faster than previous models and is easier to interpret and understand. |
OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction (2025.acl-long)
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Haonan Zhang, Run Luo, Xiong Liu, Yuchuan Wu, Ting-En Lin, Pengpeng Zeng, Qiang Qu, Feiteng Fang, Min Yang, Lianli Gao, Jingkuan Song, Fei Huang, Yongbin Li
| Challenge: | Existing methods focus on replicating dialogues in textual form, neglecting the role’s voice traits as a crucial effect in interaction, which tends to be more immersive experiences in realistic scenarios. |
| Approach: | They propose a first seamless speech-language personality interaction model to achieve immersive RPAs with low latency. |
| Outcome: | The proposed model exhibits role-specific personality traits and vocal traits throughout the interaction, enabling a mixture of speech and language responses. |
Bayesian Social Deduction with Graph-Informed Language Models (2026.acl-long)
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Shahab Rahimirad, Guven Gergerli, Lucia Romero, Angela Qian, Matthew Lyle Olson, Simon Stepputtis, Joseph Campbell
| Challenge: | Large language models (LLMs) have demonstrated remarkable general-purpose reasoning capabilities across a wide range of tasks. |
| Approach: | They propose a hybrid reasoning framework that externalizes belief inference to a structured probabilistic model while using an LLM for language understanding and interaction. |
| Outcome: | The proposed framework achieves competitive performance with larger models in Agent-Agent play and is the first language agent to defeat human players in a controlled study. |