Turning Conversations into Workflows: A Framework to Extract and Evaluate Dialog Workflows for Service AI Agents (2025.findings-acl)
Copied to clipboard
Prafulla Kumar Choubey, Xiangyu Peng, Shilpa Bhagavath, Caiming Xiong, Shiva Kumar Pentyala, Chien-Sheng Wu
| Challenge: | Existing workflow extraction methods for service agents are time-consuming and outdated, causing inconsistent and inconsistent results. |
| Approach: | They propose a framework for extracting and evaluating dialog workflows from historical interactions. |
| Outcome: | The proposed framework improves workflow extraction by 12.16% over baseline. |
Similar Papers
DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog (2023.findings-emnlp)
Copied to clipboard
Xin Zheng, Tianyu Liu, Haoran Meng, Xu Wang, Yufan Jiang, Mengliang Rao, Binghuai Lin, Yunbo Cao, Zhifang Sui
| Challenge: | Existing work on question-answer extraction fails to integrate incomplete utterances from dialog context for composite QA retrieval. |
| Approach: | They propose a task where questions and corresponding answers might be separated across different utterances. |
| Outcome: | The proposed methods perform well on 5 customer service datasets and set a benchmark for N-to-N DialogQAE with utterance and session level evaluation metrics. |
Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing goal-oriented dialogue datasets focus on identifying slots and values, but in reality, customer service agents follow multi-step procedures derived from explicit company policies. |
| Approach: | They propose to use a fully-labeled dataset to study customer service dialogue systems in real-world scenarios. |
| Outcome: | The proposed dataset outperforms existing models but still lacks 50.8% absolute accuracy to reach human-level performance on the dataset. |
Automatic Dialog Flow Extraction and Guidance (2023.eacl-srw)
Copied to clipboard
| Challenge: | This PhD thesis aims to increase the efficiency of the human's job without losing human contact. |
| Approach: | They propose to extract information from Portuguese dialogs and use it to describe interactions to create a pipeline for guiding human actions. |
| Outcome: | The proposed solution will be applied to task-oriented dialog transcriptions (e.g. call center) it will use three components: Extraction, for processing dialogs and using the information to describe interactions; Representation, for discovering the most frequent dialog flows; Guidance, for helping the agent during a new dialog. |
Improving Knowledge Production Efficiency With Question Answering on Conversation (2023.acl-industry)
Copied to clipboard
| Challenge: | Existing researches on conversation-based QA focus on document-based tasks . current researche focuses on document based tasks, but there is a lack of researche on conversation based qa . |
| Approach: | They propose a multi-span extraction model on conversation-based QA and introduce continual pre-training and multi-task learning schemes to further improve model performance. |
| Outcome: | The proposed model outperforms baseline on two Chinese datasets and will be released for research purposes. |
ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration (2025.findings-acl)
Copied to clipboard
Xinyi Jiang, Tianyi Hu, Yuheng Qin, Guoming Wang, Zhou Huan, Kehan Chen, Gang Huang, Rongxing Lu, Siliang Tang
| Challenge: | Existing methods for enhancing dialogue performance rely on summarizing behavior . e-commerce chatbots need to align their dialogue strategies with human behavior to achieve coherent, human-like conversations with customers. |
| Approach: | They propose a method to extract core patterns from dialogue data and integrate them into models by mining service thought processes using a multi-agent aPproach. |
| Outcome: | The proposed method outperforms manual methods and outperfies baselines on Taobao in China. |
Are the Tools up to the Task? an Evaluation of Commercial Dialog Tools in Developing Conversational Enterprise-grade Dialog Systems (N19-2)
Copied to clipboard
| Challenge: | Existing toolsets are incomplete in meeting the goal of building effective dialog systems, authors say . |
| Approach: | They compare dialog tools available from a number of companies to determine their strengths and weaknesses . they provide quantitative and qualitative results in three main areas: natural language understanding, dialog, and text generation . |
| Outcome: | The toolsets are incomplete, but they are compared to other tools to determine their strengths and weaknesses. |
A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs (2025.acl-industry)
Copied to clipboard
Chiwan Park, Wonjun Jang, Daeryong Kim, Aelim Ahn, Kichang Yang, Woosung Hwang, Jihyeon Roh, Hyerin Park, Hyosun Wang, Min Seok Kim, Jihoon Kang
| Challenge: | Large Language Models (LLMs) have led to significant improvements in various service domains, including search, recommendation, and chatbot applications. |
| Approach: | They propose a framework for developing scalable, controllable, and reliable AI-driven agents that can be applied to real-world applications. |
| Outcome: | The proposed framework bridges the gap between academic research and real-world application, and enables scalable, controllable, and reliable AI-driven agents. |
Interview Evaluation: A Novel Approach for Automatic Evaluation of Conversational Question Answering Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing evaluation methods for CQA use pre-collected human-human conversations . previous methods use model-predicted dialogue history instead of ground truth . |
| Approach: | They propose an automatic evaluation approach that uses the model's dialogue history to evaluate models. |
| Outcome: | The proposed method improves on existing models and their evaluations on QuAC and CoQA. |
Mirage: A Diagnostic Framework for Evaluating the Realism of Synthetic Contact Center Dialogue Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | capturing the full realism of real agent–customer interactions remains a challenge . privacy constraints and data scarcity limit the availability of real conversations . |
| Approach: | They propose a diagnostic evaluation framework for synthetic dialogue generation . they benchmarked strategies guided by structured supervision on call attributes . |
| Outcome: | The proposed framework outperforms synthetic transcripts on quality assurance tasks . it shows that current generation strategies lack sentiment fidelity, disfluency modeling, behavioral variation and conversational realism . |
Ditch the Gold Standard: Re-evaluating Conversational Question Answering (2022.acl-long)
Copied to clipboard
| Challenge: | Existing conversational question answering systems provide natural-language answers to users in information-seeking conversations. |
| Approach: | They conduct the first large-scale human evaluation of state-of-the-art conversational question answering systems . they propose a question rewriting mechanism based on predicted history which better correlates with human judgments . |
| Outcome: | The proposed question rewriting mechanism better correlates with human judgments. |