SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services

• URL: http://arxiv.org/abs/2105.08049v1
• Date: Mon, 17 May 2021 17:54:32 GMT
• Title: SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services
• Authors: Yang Zhang, Vahid Noroozi, Evelina Bakhturina, Boris Ginsburg
• Abstract summary: We propose SGD-QA, a model for schema-guided dialogue state tracking based on a question answering approach. The proposed multi-pass model shares a single encoder between the domain information and dialogue utterance. The model improves performance on unseen services by at least 1.6x compared to single-pass baseline models.
• Score: 15.21976869687864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dialogue state tracking is an essential part of goal-oriented dialogue systems, while most of these state tracking models often fail to handle unseen services. In this paper, we propose SGD-QA, a simple and extensible model for schema-guided dialogue state tracking based on a question answering approach. The proposed multi-pass model shares a single encoder between the domain information and dialogue utterance. The domain's description represents the query and the dialogue utterance serves as the context. The model improves performance on unseen services by at least 1.6x compared to single-pass baseline models on the SGD dataset. SGD-QA shows competitive performance compared to state-of-the-art multi-pass models while being significantly more efficient in terms of memory consumption and training performance. We provide a thorough discussion on the model with ablation study and error analysis.

Related papers

• TOD-Flow: Modeling the Structure of Task-Oriented Dialogues [77.15457469745364]
  We propose a novel approach focusing on inferring the TOD-Flow graph from dialogue data annotated with dialog acts. The inferred TOD-Flow graph can be easily integrated with any dialogue model to improve its prediction performance, transparency, and controllability.
  arXiv  Detail & Related papers  (2023-12-07T20:06:23Z)
• Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking [7.176787451868171]
  We introduce SPLAT, a novel architecture which achieves better generalization and efficiency than prior approaches. We demonstrate the effectiveness of our model on theGuided Dialogue (SGD) and MultiWOZ datasets.
  arXiv  Detail & Related papers  (2023-06-15T17:59:31Z)
• History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling [28.27368343751272]
  We propose a History Semantic Graph Enhanced KBQA model (HSGE) that is able to effectively model long-range semantic dependencies in conversation history. We evaluate HSGE on a widely used benchmark dataset for complex sequential question answering.
  arXiv  Detail & Related papers  (2023-06-12T05:10:58Z)
• Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking [57.92608483099916]
  Large pre-trained language models (PLMs) have shown impressive unaided performance across many NLP tasks. For more complex tasks such as dialogue state tracking (DST), designing prompts that reliably convey the desired intent is nontrivial. We introduce a saliency model to limit dialogue text length, allowing us to include more exemplars per query.
  arXiv  Detail & Related papers  (2023-02-12T15:05:10Z)
• DiSTRICT: Dialogue State Tracking with Retriever Driven In-Context Tuning [7.5700317050237365]
  We propose DiSTRICT, a generalizable in-context tuning approach for Dialogue State Tracking (DST) DSTRICT retrieves highly relevant training examples for a given dialogue to fine-tune the model without any hand-crafted templates. Experiments with the MultiWOZ benchmark datasets show that DiSTRICT outperforms existing approaches in various zero-shot and few-shot settings.
  arXiv  Detail & Related papers  (2022-12-06T09:40:15Z)
• A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking [78.2700757742992]
  Task-oriented dialogue systems often employ a Dialogue State Tracker (DST) to successfully complete conversations. Recent state-of-the-art DST implementations rely on schemata of diverse services to improve model robustness. We propose a single multi-task BERT-based model that jointly solves the three DST tasks of intent prediction, requested slot prediction and slot filling.
  arXiv  Detail & Related papers  (2022-07-02T13:27:59Z)
• GODEL: Large-Scale Pre-Training for Goal-Directed Dialog [119.1397031992088]
  We introduce GODEL, a large pre-trained language model for dialog. We show that GODEL outperforms state-of-the-art pre-trained dialog models in few-shot fine-tuning setups. A novel feature of our evaluation methodology is the introduction of a notion of utility that assesses the usefulness of responses.
  arXiv  Detail & Related papers  (2022-06-22T18:19:32Z)
• 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)
• A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset [8.990035371365408]
  We introduce FastSGT, a fast and robust BERT-based model for state tracking in goal-oriented dialogue systems. The proposed model is designed for theGuided Dialogue dataset which contains natural language descriptions. Our model keeps the efficiency in terms of computational and memory consumption while improving the accuracy significantly.
  arXiv  Detail & Related papers  (2020-08-27T18:51:18Z)
• Non-Autoregressive Dialog State Tracking [122.2328875457225]
  We propose a novel framework of Non-Autoregressive Dialog State Tracking (NADST) NADST can factor in potential dependencies among domains and slots to optimize the models towards better prediction of dialogue states as a complete set rather than separate slots. Our results show that our model achieves the state-of-the-art joint accuracy across all domains on the MultiWOZ 2.1 corpus.
  arXiv  Detail & Related papers  (2020-02-19T06:39:26Z)

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.