Papers with Human communication
Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment (2023.acl-short)
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Roni Rabin, Alexandre Djerbetian, Roee Engelberg, Lidan Hackmon, Gal Elidan, Reut Tsarfaty, Amir Globerson
| Challenge: | Human communication often involves information gaps between the interlocutors. |
| Approach: | They propose a model that generates such gap-focused questions automatically . they propose an evaluation by human annotators of the generated questions . |
| Outcome: | The proposed model outperforms human generated questions in a competitive environment. |
MTAG: Modal-Temporal Attention Graph for Unaligned Human Multimodal Language Sequences (2021.naacl-main)
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Jianing Yang, Yongxin Wang, Ruitao Yi, Yuying Zhu, Azaan Rehman, Amir Zadeh, Soujanya Poria, Louis-Philippe Morency
| Challenge: | a novel graph-based neural model for multimodal sequential data is proposed . fusion is the process of blending information from multiple modalities, usually preceded by alignment . |
| Approach: | They propose a graph-based neural model that converts unaligned data into a modal-temporal graph . they use a dynamic pruning and read-out technique to efficiently process the graph fusion operation . |
| Outcome: | The proposed model performs state-of-the-art on multimodal sentiment analysis and emotion recognition benchmarks while utilizing significantly fewer model parameters. |
Aff2Vec: Affect–Enriched Distributional Word Representations (C18-1)
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| Challenge: | Affective word distributions are not well understood in literature. |
| Approach: | They propose a model that embeds affective word interpretations into enriched word embeddings. |
| Outcome: | The proposed model outperforms the state-of-the-art in word-similarity tasks and in emotion analysis, personality detection, and frustration prediction tasks. |
Learning to Mediate Disparities Towards Pragmatic Communication (2022.acl-long)
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| Challenge: | Recent work explores pragmatic reasoning based on Rational Speech Act (RSA) and Theory of Mind in communication (Zhu et al., 2021). |
| Approach: | They propose a framework where the speaker attempts to learn the speaker-listener disparity and adjust the speech accordingly by adding a light-weighted disparity adjustment layer into working memory on top of speaker’s long-term memory system. |
| Outcome: | The proposed framework can learn and adapt to different types of listeners by adding a light-weighted disparity adjustment layer into working memory on top of speaker’s long-term memory system. |
ExpressivityBench: Can LLMs Communicate Implicitly? (2026.findings-eacl)
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Joshua Tint, Som Sagar, Aditya Taparia, Kelly Raines, Bimsara Pathiraja, Caleb Liu, Ransalu Senanayake
| Challenge: | a new study evaluates the expressivity of large language models for communicating implicitly . authors: models can express tone, identity, and intent beyond literal meanings . phrasing and tone of a message can convey a number of topics beyond literal contexts - authors . |
| Approach: | They propose a framework to evaluate the expressivity of large language models . they use a social-linguistic grader to validate their models against human judgments . |
| Outcome: | The proposed framework quantifies how well LLM-generated text communicates target properties without explicit mention across nine tasks spanning emotion, identity, and tone. |
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality (2022.emnlp-main)
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| Challenge: | Currently, human communication models fail to explicitly model common ground (CG) . less than half of the responses in current data is rated as high quality . |
| Approach: | They propose a dataset that annotates dialogues with explicit CG and solicits 9k diverse responses each following one common ground. |
| Outcome: | The proposed dataset annotates dialogues with explicit CG and solicits 9k diverse responses each following one common ground. |