| Challenge: | Using deep learning to build robust and scalable spoken dialogue systems is still a challenging task. |
| Approach: | tutorial focuses on an overview of dialogue system development . goal-oriented spoken dialogue systems are most prominent component in virtual personal assistants . |
| Outcome: | This tutorial focuses on an overview of dialogue system development while summarizing the challenges. |
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| Challenge: | Spoken Dialogue Systems (SDS) have great commercial potential . the advent of deep learning has led to significant advances in this area of NLP research . |
| Approach: | This tutorial will introduce researchers to the pipeline framework for modelling goal-oriented dialogue systems. |
| Outcome: | This tutorial will familiarise researchers with the latest advances in spoken dialogue systems . the aim of the course is to encourage dialogue research in the NLP community . |
Deep Chit-Chat: Deep Learning for ChatBots (D18-3)
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| Challenge: | tutorial focuses on building conversational models with deep learning approaches for chatbots. |
| Approach: | This tutorial focuses on building conversational models with deep learning approaches for chatbots. |
| Outcome: | The tutorial summarizes the fundamental challenges in modeling open domain dialogues . it also covers some new trends of research of chatbots - such as how to "control" conversations with specific information . |
Knowledge-enhanced Response Generation in Dialogue Systems: Current Advancements and Emerging Horizons (2024.lrec-tutorials)
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| Challenge: | Knowledge-enhanced Dialogue Systems (KEDS) are a new approach to enhancing human-machine interaction through natural language. |
| Approach: | This tutorial provides an in-depth exploration of Knowledge-enhanced Dialogue Systems (KEDS) it aims to elucidate their significance, highlight advances made using deep learning, and pinpoint the current challenges. |
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Natural Language Processing for Multilingual Task-Oriented Dialogue (2022.acl-tutorials)
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| Challenge: | a tutorial will examine the challenges and gaps in multilingual ToD research . multilingual systems are difficult to build, and are limited to English and other languages . |
| Approach: | This tutorial will discuss the importance of multilingual task-oriented dialogue systems . it will provide an overview of current research gaps, challenges and initiatives related to multilingual ToD systems - with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP. |
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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. |
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| Challenge: | In recent years, neural-network based models have been used for a wide range of tasks, including slot filling and intent classification. |
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Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations (2024.lrec-main)
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| Challenge: | Task-oriented dialogue systems have been surveyed in the medical community from a non-technical perspective, but a systematic review from . a rigorous computational perspective has to date remained noticeably absent. |
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Beyond Candidates : Adaptive Dialogue Agent Utilizing Persona and Knowledge (2023.findings-emnlp)
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| Challenge: | a previous study suggested that human dialogue systems ground persona and knowledge but they require incomplete candidate sets. |
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DialogSum: A Real-Life Scenario Dialogue Summarization Dataset (2021.findings-acl)
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| Challenge: | Experimental results show unique challenges in dialogue summarization such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense. |
| Approach: | They propose a large-scale labeled dialogue summarization dataset . they use state-of-the-art neural models to analyze spoken dialogue summaries . |
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