Towards Building a Spoken Dialogue System for Argument Exploration (2022.lrec-1)
Copied to clipboard
| Challenge: | Argumentative dialogue systems lack a robust natural language understanding framework for complex tasks . drop-down menus hinder the application of natural language learning approaches . |
| Approach: | They propose to integrate a natural language understanding framework into an argumentative dialogue system. |
| Outcome: | The proposed system is compared to a baseline system using a drop-down menu . the drop- down menu convinces, but the willingness to use it is significantly higher . |
Similar Papers
Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue (2022.aacl-main)
Copied to clipboard
| Challenge: | Persuasive dialogue systems are designed for chatbots to communicate with and influence users with specific goals. |
| Approach: | They propose a modular dialogue system framework that integrates factual information and social content into persuasive dialogues. |
| Outcome: | The proposed framework is generalizable to any dialogue tasks that have mixed social and task contents. |
Combining Argumentation Structure and Language Model for Generating Natural Argumentative Dialogue (2022.aacl-short)
Copied to clipboard
| Challenge: | Argumentative dialogue is important process where speakers discuss a specific theme for consensus building or decision making. |
| Approach: | They propose a method to generate argumentative dialogues by combining argumentation structure and language model. |
| Outcome: | The proposed method significantly improves the naturalness of arguments without losing consistency. |
NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue (2022.findings-naacl)
Copied to clipboard
| Challenge: | NLU++ provides a more challenging evaluation environment for dialogue NLU models . Typical ToD systems still rely on a modular design . |
| Approach: | They propose to use NLU++ to provide a more challenging evaluation environment for dialogue NLU models. |
| Outcome: | The proposed dataset improves existing datasets and provides a much more challenging evaluation environment for dialogue NLU models. |
Evaluation of Argument Search Approaches in the Context of Argumentative Dialogue Systems (2020.lrec-1)
Copied to clipboard
| Challenge: | Argumentative dialogue systems and chat bots require a database of arguments that matches their requirements. |
| Approach: | They propose a dialogue system that presents arguments by virtual avatar and synthetic speech to users and allows them to rate the presented content in four different categories. |
| Outcome: | The proposed system evaluates arguments retrieved by two state-of-the-art argument search engines and a system based on traditional web search. |
Would You Like to Make a Donation? A Dialogue System to Persuade You to Donate (2024.lrec-main)
Copied to clipboard
| Challenge: | Persuasive automated dialogue systems are a popular way to influence people's behavior and decision making. |
| Approach: | They propose to use a context-aware persuasion strategy selection module to persult users . they also propose a persuasiveness prediction model to automatically evaluate the persuasiveness of generated text. |
| Outcome: | The proposed system can achieve better performance on several automated evaluation metrics than baseline models. |
Argument Summarization and its Evaluation in the Era of Large Language Models (2025.emnlp-main)
Copied to clipboard
Moritz Altemeyer, Steffen Eger, Johannes Daxenberger, Yanran Chen, Tim Altendorf, Philipp Cimiano, Benjamin Schiller
| Challenge: | Large Language Models (LLMs) have revolutionized various Natural Language Generation tasks, including Argument Summarization (ArgSum). |
| Approach: | They propose a prompt-based evaluation scheme and validate it through a human benchmark dataset. |
| Outcome: | The proposed evaluation scheme outperforms existing methods and is validated by a human benchmark dataset. |
Natural Language Reasoning in Large Language Models: Analysis and Evaluation (2025.findings-acl)
Copied to clipboard
| Challenge: | Argumentative reasoning presents unique challenges due to its reliance on context, implicit assumptions, and value judgments. |
| Approach: | They propose a large-scale evaluation of LLMs' unconstrained natural language reasoning capabilities . they formalise a new strategy designed to evaluate argumentative reasoning in LLM . |
| Outcome: | The proposed model performs better on a range of reasoning tasks than other models. |
Opening up Minds with Argumentative Dialogues (2022.findings-emnlp)
Copied to clipboard
Youmna Farag, Charlotte Brand, Jacopo Amidei, Paul Piwek, Tom Stafford, Svetlana Stoyanchev, Andreas Vlachos
| Challenge: | Recent research on argumentative dialogues has focused on persuading people to take some action, changing their stance on the topic of discussion, or winning debates. |
| Approach: | They present a dataset of 183 argumentative dialogues about veganism, Brexit and COVID-19 vaccination. |
| Outcome: | The proposed model is significantly better on other dialogue properties such as engagement and clarity. |
Towards an Automatic Assessment of Crowdsourced Data for NLU (L18-1)
Copied to clipboard
| Challenge: | Recent development of spoken dialog systems aims at allowing a natural input style. |
| Approach: | They investigate how crowdsourced data can be assessed with respect to its naturalness and usefulness by using a word based language model to identify valid data. |
| Outcome: | The proposed methods show that valid data can be identified with the help of a word based language model. |
ARGSBASE: A Multi-Agent Interface for Structured Human–AI Deliberation (2026.eacl-demo)
Copied to clipboard
| Challenge: | a new deliberation interface enables users to engage with multiple large language models (LLMs) ArgsBase exemplifies hybrid argumentation and supports epistemically responsible human–AI collaboration. |
| Approach: | They propose a deliberation interface that enables users to engage with multiple large language models coordinated by a moderator agent. |
| Outcome: | The proposed system exemplifies hybrid argumentation and aligns with recent calls for "reasonable parrots" the user study shows that the tool is easy to use, perspective-enhancing, and promising for research . |