A Career Interview Dialogue System using Large Language Model-based Dynamic Slot Generation

• URL: http://arxiv.org/abs/2412.16943v1
• Date: Sun, 22 Dec 2024 09:25:02 GMT
• Title: A Career Interview Dialogue System using Large Language Model-based Dynamic Slot Generation
• Authors: Ekai Hashimoto, Mikio Nakano, Takayoshi Sakurai, Shun Shiramatsu, Toshitake Komazaki, Shiho Tsuchiya,
• Abstract summary: This study aims to improve the efficiency and quality of career interviews conducted by nursing managers. We have been developing a slot-filling dialogue system that engages in pre-interviews to collect information on staff careers.
• Score: 0.6597195879147557
• License:
• Abstract: This study aims to improve the efficiency and quality of career interviews conducted by nursing managers. To this end, we have been developing a slot-filling dialogue system that engages in pre-interviews to collect information on staff careers as a preparatory step before the actual interviews. Conventional slot-filling-based interview dialogue systems have limitations in the flexibility of information collection because the dialogue progresses based on predefined slot sets. We therefore propose a method that leverages large language models (LLMs) to dynamically generate new slots according to the flow of the dialogue, achieving more natural conversations. Furthermore, we incorporate abduction into the slot generation process to enable more appropriate and effective slot generation. To validate the effectiveness of the proposed method, we conducted experiments using a user simulator. The results suggest that the proposed method using abduction is effective in enhancing both information-collecting capabilities and the naturalness of the dialogue.

Related papers

• Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-Resource User Groups [1.7725414095035827]
  This study addresses the interaction challenges encountered by spoken dialogue systems (SDSs) when engaging with users who exhibit distinct conversational behaviors. We propose a novel data augmentation framework to enhance SDS performance for user groups with limited resources.
  arXiv  Detail & Related papers  (2024-08-20T03:33:04Z)
• Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals [76.69419538047813]
  ProToD approach anticipates the future dialogue actions and incorporates the goal-oriented reward signal to enhance ToD systems. We present a novel evaluation method that assesses ToD systems based on goal-driven dialogue simulations. Empirical experiments conducted on the MultiWoZ 2.1 dataset demonstrate that our model can achieve superior performance using only 10% of the data.
  arXiv  Detail & Related papers  (2023-09-16T10:56:00Z)
• FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue [20.79359173822053]
  We propose a novel dialogue pre-training model, FutureTOD, which distills future knowledge to the representation of the previous dialogue context. Our intuition is that a good dialogue representation both learns local context information and predicts future information.
  arXiv  Detail & Related papers  (2023-06-17T10:40:07Z)
• KETOD: Knowledge-Enriched Task-Oriented Dialogue [77.59814785157877]
  Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. We investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model.
  arXiv  Detail & Related papers  (2022-05-11T16:01:03Z)
• Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems [109.16553492049441]
  We propose a novel method to incorporate the knowledge reasoning capability into dialogue systems in a more scalable and generalizable manner. To the best of our knowledge, this is the first work to have transformer models generate responses by reasoning over differentiable knowledge graphs.
  arXiv  Detail & Related papers  (2022-03-20T17:51:49Z)
• Precognition in Task-oriented Dialogue Understanding: Posterior Regularization by Future Context [8.59600111891194]
  We propose to jointly model historical and future information through the posterior regularization method. We optimize the KL distance between these to regularize our model during training. Experiments on two dialogue datasets validate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2022-03-07T09:58:50Z)
• Response Generation with Context-Aware Prompt Learning [19.340498579331555]
  We present a novel approach for pre-trained dialogue modeling that casts the dialogue generation problem as a prompt-learning task. Instead of fine-tuning on limited dialogue data, our approach, DialogPrompt, learns continuous prompt embeddings optimized for dialogue contexts. Our approach significantly outperforms the fine-tuning baseline and the generic prompt-learning methods.
  arXiv  Detail & Related papers  (2021-11-04T05:40:13Z)
• "How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations [87.95711406978157]
  This work presents a new benchmark on spoken task-oriented conversations. We study multi-domain dialogue state tracking and knowledge-grounded dialogue modeling. Our data set enables speech-based benchmarking of task-oriented dialogue systems.
  arXiv  Detail & Related papers  (2021-09-28T04:51:04Z)
• Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters [52.725200145600624]
  We propose KnowExpert to bypass the retrieval process by injecting prior knowledge into the pre-trained language models with lightweight adapters. Experimental results show that KnowExpert performs comparably with the retrieval-based baselines.
  arXiv  Detail & Related papers  (2021-05-13T12:33:23Z)
• TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue [113.45485470103762]
  In this work, we unify nine human-human and multi-turn task-oriented dialogue datasets for language modeling. To better model dialogue behavior during pre-training, we incorporate user and system tokens into the masked language modeling.
  arXiv  Detail & Related papers  (2020-04-15T04:09:05Z)

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.