Conversation Architecture and Structure

Related topics:

• A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations
  Cora asks whether the user feels hungry, whether they want to eat healthy, and whether they are on a specific diet. Cora also asks whether the user wants to use a specific ingredient for their recipe.…
• A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects
  Proactive dialogue systems, related to a wide range of real-world conversational applications, equip the conversational agent with the capability of leading the conversation direction towards achievin…
• Adding Chit-Chat to Enhance Task-Oriented Dialogues
  In this work, we propose to integrate both types of systems by Adding Chit-Chat to ENhance Task-ORiented dialogues (ACCENTOR), with the goal of making virtual assistant conversations more engaging and…
• Agreement Tracking for Multi-Issue Negotiation Dialogues
  Amogh Mannekote, Bonnie J. Dorr, Kristy Elizabeth Boyer University of Florida “Automated negotiation support systems aim to help human negotiators reach more favorable outcomes in multi-issue negoti…
• Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models
  Previous sequence-to-sequence models are used to tackle documents with only one narrator. However, in dialogs, two speakers have different roles; therefore, their language model distributions are very…
• Are LLMs All You Need for Task-Oriented Dialogue?
  We show that in explicit belief state tracking, LLMs underperform compared to specialized task-specific models. Nevertheless, they show some ability to guide the dialogue to a successful ending throug…
• Ask an Expert: Leveraging Language Models to Improve Strategic Reasoning in Goal-Oriented Dialogue Models
  “Existing dialogue models may encounter scenarios which are not well-represented in the training data, and as a result generate responses that are unnatural, inappropriate, or unhelpful. We propose th…
• Backtracing: Retrieving the Cause of the Query
  While information retrieval (IR) systems may provide answers for such user queries, they do not directly assist content creators—such as lecturers who want to improve their content—identify segments t…
• Cognitive Architectures for Language Agents
  “We introduce such a framework, drawing parallels with two ideas from the history of computing and artificial intelligence (AI): production systems and cognitive architectures. Production systems gene…
• Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations
  In the field of natural language processing, open-domain chatbots have emerged as an important research topic. However, a major limitation of existing open-domain chatbot research is its singular focu…
• Conversation Derailment Forecasting with Graph Convolutional Networks
  “Online conversations are particularly susceptible to derailment, which can manifest itself in the form of toxic communication patterns like disrespectful comments or verbal abuse. Forecasting convers…
• Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation
  To address the challenge of policy learning in open-domain multi-turn conversation, we propose to represent prior information about dialog transitions as a graph and learn a graph grounded dialog poli…
• DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations
  Those models take a contrastive learning approach, where they build binary classifiers to differentiate positive, or coherent examples from negative, or incoherent dialogues. Those classifiers are usu…
• Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models
  Effective interlocutors account for the uncertain goals, beliefs, and emotions of others. But even the best human conversationalist cannot perfectly anticipate the trajectory of a dialogue. How well c…
• Decision-Oriented Dialogue for Human–AI Collaboration
  “All these situations share an underlying structured decision problem in the face of uncertainty, where communicating and collaborating with others is often critical to arrive at the best solution. D…
• Deep Neural Network Approach for the Dialog State Tracking Challenge
  Statistical dialog systems, in maintaining a distribution over multiple hypotheses of the true dialog state, are able to behave in a robust manner when faced with noisy conditions and ambiguity. Such …
• Dialogue State Tracking with a Language Model using Schema-Driven Prompting
  Task-oriented conversational systems often use dialogue state tracking to represent the user’s intentions, which involves filling in values of pre-defined slots. Many approaches have been proposed, of…
• Dialogue Transformers
  Conversational AI assistants promise to help users achieve a task through natural language. Interpreting simple instructions like please turn on the lights is relatively straightforward, but to handle…
• DialogueReason: Rule-Based RL Sparks Dialogue Reasoning in LLMs
  We propose DialogueReason, a reasoning paradigm that uncovers the lost roles in monologue-style reasoning models, aiming to boost diversity and coherency of the reasoning process. Recent advances in R…
• Efficient Streaming Language Models with Attention Sinks
  Deploying Large Language Models (LLMs) in streaming applications such as multi-round dialogue, where long interactions are expected, is urgently needed but poses two major challenges. Firstly, during …
• Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals
  Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular in current practica…
• Enhancing Pipeline-Based Conversational Agents with Large Language Model
  “This paper proposes a hybrid approach that leverages LLMs, in particular GPT-4, to enhance pipeline-based CAs. Using this approach, maintainers of existing CAs can adopt new domains and overcome the …
• HiTKG: Towards Goal-Oriented Conversations via Multi-Hierarchy Learning
  The existing recurrent and graph attention based KG walkers either insufficiently utilize the conversation states or lack global guidance. In our work, a hierarchical model learns goal planning in a h…
• Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems
  In light of this finding, we propose a novel ChatCRS framework to decompose the complex CRS task into several subtasks through the implementation of 1) a knowledge retrieval agent using a tool-augment…
• Insert-expansions For Tool-enabled Conversational Agents
  “This paper delves into an advanced implementation of Chain-of-Thought-Prompting in Large Language Models, focusing on the use of tools (or "plug-ins") within the explicit reasoning paths generated by…
• Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy
  Proactive dialogue system is able to lead the conversation to a goal topic and has advantaged potential in bargain, persuasion and negotiation. Current corpus-based learning manner limits its practica…
• Interaction Dynamics as a Reward Signal for LLMs
  The alignment of Large Language Models (LLMs) for multi-turn conversations typically relies on reward signals derived from the content of the text. This approach, however, overlooks a rich, complement…
• KETOD: Knowledge-Enriched Task-Oriented Dialogue
  we create a new dataset, KETOD (Knowledge-Enriched Task-Oriented Dialogue), where we naturally enrich task-oriented dialogues with chit-chat based on relevant entity knowledge. We also propose two new…
• Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations
  Unlike empathetic dialogues, the system in emotional support conversations (ESC) is expected to not only convey empathy for comforting the help-seeker, but also proactively assist in exploring and add…
• Learning to Relate to Previous Turns in Conversational Search
  As in any conversation in natural language, queries in conversational search may involve omissions, references to previous turns, and ambiguities [32]. Thus, a primary challenge for effective conversa…
• Learning to Select the Relevant History Turns in Conversational Question Answering
  “The increasing demand for web-based digital assistants has given a rapid rise in the interest of the Information Retrieval (IR) community towards the field of conversational question answering (ConvQ…
• Multi-Task End-to-End Training Improves Conversational Recommendation
  “The modern recommendation systems found in commercial applications are largely based on implicit preferences, such as a user’s history of web page clicks, item purchases, or media streams, with the r…
• MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMs
  We present MultiChallenge, a pioneering benchmark evaluating large language models (LLMs) on conducting multi-turn conversations with human users, a crucial yet underexamined capability for their appl…
• Neural Approaches to Conversational AI
  Conversational AI is fundamental to natural user interfaces. It is a rapidly growing field, attracting many researchers in the Natural Language Processing (NLP), Information Retrieval (IR) and Machine…
• Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning
  Task-oriented dialog presents a difficult challenge encompassing multiple problems including multi-turn language understanding and generation, knowledge retrieval and reasoning, and action prediction.…
• Neural Conversation Models and How to Rein Them in: A Survey of Failures and Fixes
  “In this paper, we attempt to systematise the literature about the attested problems of neural conversation models (conditional language models realised with neural networks) used as chat-partner simu…
• On the Conversational Basis of Some Presuppositions
  The current literature on presupposition focuses almost exclusively on the projection problem: the question of how and why the presuppositions of atomic clauses are projected to complex sentences whic…
• OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs
  We study a conversational reasoning model that strategically traverses through a largescale common fact knowledge graph (KG) to introduce engaging and contextually diverse entities and attributes. For…
• Overview of DialAM-2024: Argument Mining in Natural Language Dialogues
  Argumentation is the process by which humans rationally elaborate their thoughts and opinions in written (e.g., essays) or spoken (e.g., debates) contexts. Argument Mining research, however, has been …
• Planning Like Human: A Dual-process Framework for Dialogue Planning
  In proactive dialogue, the challenge lies not just in generating responses but in steering conversations toward predetermined goals, a task where Large Language Models (LLMs) typically struggle due to…
• Post-training for Efficient Communication via Convention Formation
  Humans communicate with increasing efficiency in multi-turn interactions, by adapting their language and forming ad-hoc conventions. In contrast, prior work shows that LLMs do not naturally show this …
• Pro-Active Systems and Influenceable Users: Simulating Pro-Activity in Task-oriented Dialogues
  We investigate proactivity, the capacity of a dialogue system to provide relevant information even when not explicitly requested, in the context of task-oriented dialogues. We propose to extend the cu…
• Proactive Conversational Agents with Inner Thoughts
  In this paper, we demonstrate the limitations of such methods and rethink what it means for AI to be proactive in multi-party, human-AI conversations. We propose that just like humans, rather than mer…
• Proactive Human-Machine Conversation with Explicit Conversation Goals
  Though great progress has been made for human-machine conversation, current dialogue system is still in its infancy: it usually converses passively and utters words more as a matter of response, rathe…
• Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration
  Conversational systems based on Large Language Models (LLMs), such as ChatGPT, show exceptional proficiency in context understanding and response generation. However, they still possess limitations, s…
• ProsocialDialog: A Prosocial Backbone for Conversational Agents
  Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce PROSOCIALDIALOG, t…
• Quantitative Introspection in Language Models: Tracking Internal States Across Conversation
  Tracking the internal states of large language models across conversations is important for safety, interpretability, and model welfare, yet current methods are limited. Linear probes and other white-…
• Sequence Organization in Interaction: A Primer in Conversation Analysis
  By “generic orders of organization,” I mean the various organizations of practice that deal with the various generic organizational contingencies of talk-in-interaction without which it cannot proceed…
• Synthetic Dialogue Dataset Generation using LLM Agents
  Linear programming (LP) problems are pervasive in real-life applications. However, despite their apparent simplicity, an untrained user may find it difficult to determine the linear model of their spe…
• Tailored Conversations beyond LLMs: A RL-Based Dialogue Manager
  In this work, we propose a novel framework that integrates large language models (LLMs) with an RL-based dialogue manager for open-ended dialogue with a specific goal. By leveraging hierarchical reinf…
• Towards Conversational Recommendation over Multi-Type Dialogs
  We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recomm…
• WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent Triggerability in Task-Oriented Dialogue
  Task-oriented dialogue systems often face difficulties when user utterances seem semantically complete but lack necessary structural information for appropriate system action. This arises because user…
• “What do others think?”: Task-Oriented Conversational Modeling with Subjective Knowledge
  Task-oriented Dialogue (TOD) Systems aim to build dialogue systems that assist users in accomplishing specific goals, such as booking a hotel or a restaurant. Traditional TODs rely on domain-specific …