Addressing Domain Changes in Task-oriented Conversational Agents through Dialogue Adaptation (2023.eacl-srw)
Copied to clipboard
| Challenge: | Recent task-oriented dialogue systems are trained on annotated dialogues, but when domain knowledge changes, the initial model may become obsolete. |
| Approach: | They propose to use an annotated dialogue dataset to train a dialogue model for domain changes . they propose to fine-tune a generative language model on domain changes to reduce performance . |
| Outcome: | The proposed approach reduces performance by 55% by fine-tuning a generative language model on domain changes. |
Similar Papers
Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems (D19-56)
Copied to clipboard
| Challenge: | Statistical conversational systems are complex, timeintensive, expensive, and not easily transferable due to data scarcity. |
| Approach: | They propose a task-oriented dialogue model that operates on text input . they validate it on multi-domain task-orientated dialogues from a multi-word dataset . |
| Outcome: | The proposed model bypasses explicit policy and language generation modules on multi-domain task-oriented dialogues from the MultiWOZ dataset. |
Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Dialogue policy learning for task-oriented dialogue systems has enjoyed great progress through using reinforcement learning methods. |
| Approach: | They propose a dialogue action decoder and a simulator-free adversarial learning method to improve dialogue agent performance without using reinforcement learning. |
| Outcome: | The proposed methods achieve more stable and higher performance with fewer efforts, such as the domain knowledge required to design a user simulator and the intractable parameter tuning in reinforcement learning. |
Unsupervised End-to-End Task-Oriented Dialogue with LLMs: The Power of the Noisy Channel (2024.emnlp-main)
Copied to clipboard
| Challenge: | a task-oriented dialogue system requires turn-level annotations for interacting with their APIs. |
| Approach: | They propose an unsupervised approach that infers turn-level annotations as latent variables using a noisy channel model to build an end-to-end dialogue agent. |
| Outcome: | The proposed method doubles the success rate of a strong GPT-3.5 benchmark. |
Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation (C18-1)
Copied to clipboard
| Challenge: | Existing pipeline models for task-oriented dialogue system require explicit modeling of dialogue states and hand-crafted action spaces to query domain-specific knowledge base. |
| Approach: | They propose a framework that leverages the advantages of classic pipeline and sequence-to-sequence models. |
| Outcome: | The proposed framework outperforms baseline models on automatic and human evaluation on a Stanford Multi-turn Multi-domain task-oriented dialogue dataset. |
UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to training dialogue agents separately are not optimized for multi-domain task-oriented dialogues. |
| Approach: | They propose a unified neural architecture for end-to-end conversational systems in multi-domain task-oriented dialogues that jointly trains a bi-level state tracker and a joint dialogue act and response generator. |
| Outcome: | The proposed system outperforms existing systems on the MultiWOZ2.1 benchmark in dialogue state tracking, context-to-text, and end-to end settings. |
Training Neural Response Selection for Task-Oriented Dialogue Systems (P19-1)
Copied to clipboard
Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su
| Challenge: | Despite their popularity, retrieval-based models have had modest impact on task-oriented dialogue systems . main obstacle to their application is the low-data regime of most task-orientated dialogue tasks . e-commerce, banking, and other domains are applications of retrieval models . |
| Approach: | They propose a method which pretrains a retrieval-based model on large general-domain conversational corpora and fine-tunes it for the target dialogue domain. |
| Outcome: | The proposed method is evaluated on five diverse domains, ranging from e-commerce to banking. |
A Unifying View On Task-oriented Dialogue Annotation (2022.lrec-1)
Copied to clipboard
| Challenge: | Recent research attention in task-oriented dialogue systems focuses on end-to-end neural models. |
| Approach: | They present a dataset that combines annotated corpora from four domains to provide a unified ontology and annotation schema for task-oriented dialogues. |
| Outcome: | The proposed dataset improves language, information content and performance in dialogues with two recent models. |
Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing methods for domain adaptation of abstractive dialogue summarization lack generalization ability on new domains. |
| Approach: | They propose a domain-oriented prefix-tuning model that uses a prefix module to alleviate domain entanglement and discrete prompts to guide the model to focus on key contents of dialogues. |
| Outcome: | The proposed model can be generalized to two multi-domain dialogue summarization datasets. |
An Improved, Strong Baseline for Pre-Trained Large Language Models as Task-Oriented Dialogue Systems (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have shown that Large Language Models perform insufficiently as TOD systems. |
| Approach: | They propose a self-checking mechanism to improve LLM performance as TOD systems. |
| Outcome: | The proposed model outperforms existing models and improves their performance. |
Towards Efficient Dialogue Processing in the Emergency Response Domain (2023.acl-srw)
Copied to clipboard
| Challenge: | Adapters perform dialogue act classification and domain-specific slot tagging in the emergency response domain. |
| Approach: | They propose to build a system that performs dialogue act classification and domain-specific slot tagging while being efficient, flexible and robust. |
| Outcome: | The proposed model performs well in the emergency response domain while being efficient, flexible and robust. |