Intent-driven In-context Learning for Few-shot Dialogue State Tracking

• URL: http://arxiv.org/abs/2412.03270v1
• Date: Wed, 04 Dec 2024 12:25:41 GMT
• Title: Intent-driven In-context Learning for Few-shot Dialogue State Tracking
• Authors: Zihao Yi, Zhe Xu, Ying Shen,
• Abstract summary: Dialogue state tracking (DST) plays an essential role in task-oriented dialogue systems. IDIC-DST achieves state-of-the-art performance in few-shot settings on MultiWOZ 2.1 and MultiWOZ 2.4 datasets.
• Score: 14.866241060137714
• License:
• Abstract: Dialogue state tracking (DST) plays an essential role in task-oriented dialogue systems. However, user's input may contain implicit information, posing significant challenges for DST tasks. Additionally, DST data includes complex information, which not only contains a large amount of noise unrelated to the current turn, but also makes constructing DST datasets expensive. To address these challenges, we introduce Intent-driven In-context Learning for Few-shot DST (IDIC-DST). By extracting user's intent, we propose an Intent-driven Dialogue Information Augmentation module to augment the dialogue information, which can track dialogue states more effectively. Moreover, we mask noisy information from DST data and rewrite user's input in the Intent-driven Examples Retrieval module, where we retrieve similar examples. We then utilize a pre-trained large language model to update the dialogue state using the augmented dialogue information and examples. Experimental results demonstrate that IDIC-DST achieves state-of-the-art performance in few-shot settings on MultiWOZ 2.1 and MultiWOZ 2.4 datasets.

Related papers

• Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation [12.93942316816741]
  GPT-4 is used to simulate the user and agent interaction, generating thousands of annotated dialogues with DST labels. A two-stage fine-tuning on LLaMA 2 is performed on the generated data and the real data for the DST prediction. Our approach is also capable of adapting to the dynamic demands in real-world scenarios, generating dialogues in new domains swiftly.
  arXiv  Detail & Related papers  (2024-05-17T07:00:05Z)
• Dialogue State Distillation Network with Inter-Slot Contrastive Learning for Dialogue State Tracking [25.722458066685046]
  Dialogue State Tracking (DST) aims to extract users' intentions from the dialogue history. Currently, most existing approaches suffer from error propagation and are unable to dynamically select relevant information. We propose a Dialogue State Distillation Network (DSDN) to utilize relevant information of previous dialogue states.
  arXiv  Detail & Related papers  (2023-02-16T11:05:24Z)
• KILDST: Effective Knowledge-Integrated Learning for Dialogue State Tracking using Gazetteer and Speaker Information [3.342637296393915]
  Dialogue State Tracking (DST) is core research in dialogue systems and has received much attention. It is necessary to define a new problem that can deal with dialogue between users as a step toward the conversational AI that extracts and recommends information from the dialogue between users. We introduce a new task - DST from dialogue between users about scheduling an event (DST-S) The DST-S task is much more challenging since it requires the model to understand and track dialogue in the dialogue between users and to understand who suggested the schedule and who agreed to the proposed schedule.
  arXiv  Detail & Related papers  (2023-01-18T07:11:56Z)
• CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation [75.60156479374416]
  CGoDial is a new challenging and comprehensive Chinese benchmark for Goal-oriented Dialog evaluation. It contains 96,763 dialog sessions and 574,949 dialog turns totally, covering three datasets with different knowledge sources. To bridge the gap between academic benchmarks and spoken dialog scenarios, we either collect data from real conversations or add spoken features to existing datasets via crowd-sourcing.
  arXiv  Detail & Related papers  (2022-11-21T16:21:41Z)
• Information Extraction and Human-Robot Dialogue towards Real-life Tasks: A Baseline Study with the MobileCS Dataset [52.22314870976088]
  The SereTOD challenge is organized and releases the MobileCS dataset, which consists of real-world dialog transcripts between real users and customer-service staffs from China Mobile. Based on the MobileCS dataset, the SereTOD challenge has two tasks, not only evaluating the construction of the dialogue system itself, but also examining information extraction from dialog transcripts. This paper mainly presents a baseline study of the two tasks with the MobileCS dataset.
  arXiv  Detail & Related papers  (2022-09-27T15:30:43Z)
• OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue [40.62090743056549]
  This paper presents an ontology-aware pretrained language model (OPAL) for end-to-end task-oriented dialogue (TOD) Unlike chit-chat dialogue models, task-oriented dialogue models fulfill at least two task-specific modules: dialogue state tracker (DST) and response generator (RG)
  arXiv  Detail & Related papers  (2022-09-10T04:38:27Z)
• In-Context Learning for Few-Shot Dialogue State Tracking [55.91832381893181]
  We propose an in-context (IC) learning framework for few-shot dialogue state tracking (DST) A large pre-trained language model (LM) takes a test instance and a few annotated examples as input, and directly decodes the dialogue states without any parameter updates. This makes the LM more flexible and scalable compared to prior few-shot DST work when adapting to new domains and scenarios.
  arXiv  Detail & Related papers  (2022-03-16T11:58:24Z)
• Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation [70.81596088969378]
  Cross-lingual Outline-based Dialogue dataset (termed COD) enables natural language understanding. COD enables dialogue state tracking, and end-to-end dialogue modelling and evaluation in 4 diverse languages.
  arXiv  Detail & Related papers  (2022-01-31T18:11:21Z)
• Prompt Learning for Few-Shot Dialogue State Tracking [75.50701890035154]
  This paper focuses on how to learn a dialogue state tracking (DST) model efficiently with limited labeled data. We design a prompt learning framework for few-shot DST, which consists of two main components: value-based prompt and inverse prompt mechanism. Experiments show that our model can generate unseen slots and outperforms existing state-of-the-art few-shot methods.
  arXiv  Detail & Related papers  (2022-01-15T07:37:33Z)
• Improving Longer-range Dialogue State Tracking [22.606650177804966]
  Dialogue state tracking (DST) is a pivotal component in task-oriented dialogue systems. In this paper, we aim to improve the overall performance of DST with a special focus on handling longer dialogues.
  arXiv  Detail & Related papers  (2021-02-27T02:44:28Z)
• Improving Limited Labeled Dialogue State Tracking with Self-Supervision [91.68515201803986]
  Existing dialogue state tracking (DST) models require plenty of labeled data. We present and investigate two self-supervised objectives: preserving latent consistency and modeling conversational behavior. Our proposed self-supervised signals can improve joint goal accuracy by 8.95% when only 1% labeled data is used.
  arXiv  Detail & Related papers  (2020-10-26T21:57:42Z)

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.