Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema

• URL: http://arxiv.org/abs/2303.16252v1
• Date: Tue, 28 Mar 2023 18:56:31 GMT
• Title: Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema
• Authors: Adib Mosharrof, M.H. Maqbool, A.B. Siddique
• Abstract summary: State-of-the-art approaches in task-oriented dialog systems formulate the problem as a conditional sequence generation task. This requires labeled training data for each new domain or task. We introduce a novel Zero-Shot generalizable end-to-end Task-oriented Dialog system, ZS-ToD.
• Score: 2.7178968279054936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Task-oriented dialog systems empower users to accomplish their goals by facilitating intuitive and expressive natural language interactions. State-of-the-art approaches in task-oriented dialog systems formulate the problem as a conditional sequence generation task and fine-tune pre-trained causal language models in the supervised setting. This requires labeled training data for each new domain or task, and acquiring such data is prohibitively laborious and expensive, thus making it a bottleneck for scaling systems to a wide range of domains. To overcome this challenge, we introduce a novel Zero-Shot generalizable end-to-end Task-oriented Dialog system, ZS-ToD, that leverages domain schemas to allow for robust generalization to unseen domains and exploits effective summarization of the dialog history. We employ GPT-2 as a backbone model and introduce a two-step training process where the goal of the first step is to learn the general structure of the dialog data and the second step optimizes the response generation as well as intermediate outputs, such as dialog state and system actions. As opposed to state-of-the-art systems that are trained to fulfill certain intents in the given domains and memorize task-specific conversational patterns, ZS-ToD learns generic task-completion skills by comprehending domain semantics via domain schemas and generalizing to unseen domains seamlessly. We conduct an extensive experimental evaluation on SGD and SGD-X datasets that span up to 20 unique domains and ZS-ToD outperforms state-of-the-art systems on key metrics, with an improvement of +17% on joint goal accuracy and +5 on inform. Additionally, we present a detailed ablation study to demonstrate the effectiveness of the proposed components and training mechanism

Related papers

• Unified Language-driven Zero-shot Domain Adaptation [55.64088594551629]
  Unified Language-driven Zero-shot Domain Adaptation (ULDA) is a novel task setting. It enables a single model to adapt to diverse target domains without explicit domain-ID knowledge.
  arXiv  Detail & Related papers  (2024-04-10T16:44:11Z)
• PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment [58.46761798403072]
  A model-based automatic dialogue evaluation metric (ADEM) is expected to perform well across multiple domains. Despite significant progress, an ADEM that works well in one domain does not necessarily generalize to another. We propose a Panel of Experts (PoE) network that consists of a shared transformer encoder and a collection of lightweight adapters.
  arXiv  Detail & Related papers  (2022-12-18T02:26:50Z)
• A Simple But Effective Approach to n-shot Task-Oriented Dialogue Augmentation [32.43362825854633]
  We introduce a framework that creates synthetic task-oriented dialogues in a fully automatic manner. Our framework uses the simple idea that each turn-pair in a task-oriented dialogue has a certain function. We observe significant improvements in the fine-tuning scenarios in several domains.
  arXiv  Detail & Related papers  (2021-02-27T18:55:12Z)
• A Hybrid Task-Oriented Dialog System with Domain and Task Adaptive Pretraining [25.674966922466467]
  This paper describes our submission for the End-to-end Multi-domain Task Completion Dialog shared task at the 9th Dialog System Technology Challenge (DSTC-9) Participants in the shared task build an end-to-end task completion dialog system which is evaluated by human evaluation and a user simulator based automatic evaluation.
  arXiv  Detail & Related papers  (2021-02-08T20:02:30Z)
• RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems [75.87418236410296]
  We introduce the RADDLE benchmark, a collection of corpora and tools for evaluating the performance of models across a diverse set of domains. RADDLE is designed to favor and encourage models with a strong generalization ability. We evaluate recent state-of-the-art systems based on pre-training and fine-tuning, and find that grounded pre-training on heterogeneous dialog corpora performs better than training a separate model per domain.
  arXiv  Detail & Related papers  (2020-12-29T08:58:49Z)
• Point or Generate Dialogue State Tracker [0.0]
  We propose the Point-Or-Generate Dialogue State Tracker (POGD) POGD points out explicitly expressed slot values from the user's utterance, and generates implicitly expressed ones based on slot-specific contexts. Experiments show that POGD not only obtains state-of-the-art results on both WoZ 2.0 and MultiWoZ 2.0 datasets but also has good generalization on unseen values and new slots.
  arXiv  Detail & Related papers  (2020-08-08T02:15:25Z)
• UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues [101.96097419995556]
  "UniConv" is a novel unified neural architecture for end-to-end conversational systems in task-oriented dialogues. We conduct comprehensive experiments in dialogue state tracking, context-to-text, and end-to-end settings on the MultiWOZ2.1 benchmark.
  arXiv  Detail & Related papers  (2020-04-29T16:28:22Z)
• Recent Advances and Challenges in Task-oriented Dialog System [63.82055978899631]
  Task-oriented dialog systems are attracting more and more attention in academic and industrial communities. We discuss three critical topics for task-oriented dialog systems: (1) improving data efficiency to facilitate dialog modeling in low-resource settings, (2) modeling multi-turn dynamics for dialog policy learning, and (3) integrating domain knowledge into the dialog model.
  arXiv  Detail & Related papers  (2020-03-17T01:34:56Z)
• Hierarchical Context Enhanced Multi-Domain Dialogue System for Multi-domain Task Completion [17.66372217976539]
  This paper describes our submitted solution, Hierarchical Context Enhanced Dialogue System (HCEDS) The main motivation of our system is to comprehensively explore the potential of hierarchical context for sufficiently understanding complex dialogues. Results listed in the leaderboard show that our system achieves first place in automatic evaluation and the second place in human evaluation.
  arXiv  Detail & Related papers  (2020-03-03T05:10:13Z)
• Few-shot Natural Language Generation for Task-Oriented Dialog [113.07438787659859]
  We present FewShotWoz, the first NLG benchmark to simulate the few-shot learning setting in task-oriented dialog systems. We develop the SC-GPT model, which is pre-trained on a large set of annotated NLG corpus to acquire the controllable generation ability. Experiments on FewShotWoz and the large Multi-Domain-WOZ datasets show that the proposed SC-GPT significantly outperforms existing methods.
  arXiv  Detail & Related papers  (2020-02-27T18:48:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.