Developing Production-Level Conversational Interfaces with Shallow Semantic Parsing (D18-2)
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| Challenge: | In this demo, we demonstrate an end-to-end approach for building conversational interfaces from prototype to production. |
| Approach: | They propose an end-to-end approach for building conversational interfaces from prototype to production that leverages shallow semantic parsing. |
| Outcome: | The proposed approach has proven to work well for a number of applications across diverse verticals. |
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| Challenge: | This tutorial focuses on the evolution of voice-native LLMs . it reviews the adaptation of text LLM to audio, cross-modal alignment, and joint speech–text training . |
| Approach: | This tutorial examines the evolution of voice-native LLMs in conversational agents . it compares cascaded and voice-based LLM systems to end-to-end retrieval-and vision-grounded systems . |
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What, When, and How to Ground: Designing User Persona-Aware Conversational Agents for Engaging Dialogue (2023.acl-industry)
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| Challenge: | a personalized dialogue system can generate user-customized responses based on long-term memory about the user's persona. |
| Approach: | They propose a method for building a personalized open-domain dialogue system . they combine weighted dataset blending and negative persona information augmentation methods . |
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Conversational Semantic Parsing for Dialog State Tracking (2020.emnlp-main)
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Jianpeng Cheng, Devang Agrawal, Héctor Martínez Alonso, Shruti Bhargava, Joris Driesen, Federico Flego, Dain Kaplan, Dimitri Kartsaklis, Lin Li, Dhivya Piraviperumal, Jason D. Williams, Hong Yu, Diarmuid Ó Séaghdha, Anders Johannsen
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Value-Agnostic Conversational Semantic Parsing (2021.acl-long)
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Emmanouil Antonios Platanios, Adam Pauls, Subhro Roy, Yuchen Zhang, Alexander Kyte, Alan Guo, Sam Thomson, Jayant Krishnamurthy, Jason Wolfe, Jacob Andreas, Dan Klein
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Controllable Conversation Generation with Conversation Structures via Diffusion Models (2023.findings-acl)
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| Challenge: | Current generation models fail to effectively utilize rich linguistic and world knowledge to generate coherent long text. |
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Lightweight Transformers for Conversational AI (2022.naacl-industry)
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| Challenge: | Commercial dialogue systems typically require a small footprint and fast execution time, but recent trends are in the other direction, resulting in difficulties in model deployment. |
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| Challenge: | Existing toolsets are incomplete in meeting the goal of building effective dialog systems, authors say . |
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When Speed Meets Intelligence: Scalable Conversational NER in an Ever-evolving World (2026.eacl-industry)
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| Challenge: | Large Language Models excel at understanding conversational semantics, but lack of data makes them impractical for production deployment. |
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It’s Not under the Lamppost: Expanding the Reach of Conversational AI (2024.lrec-main)
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| Challenge: | Focused probes into the capabilities of language-based assistants easily reveal significant areas of brittleness that demonstrate large gaps in their coverage. |
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Data Collection and End-to-End Learning for Conversational AI (D19-2)
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| Challenge: | tutorial aims to familiarise research community with recent advances in statistical dialogue systems . focus of tutorial is on learning end-to-end from data and their relation to more common modular systems. |
| Approach: | This tutorial aims to familiarise the research community with the latest advances in statistical dialogue systems . the focus of the tutorial is on recently introduced end-to-end learning for dialogue systems and their relation to more common modular systems. |
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