MonoTODia: Translating Monologue Requests to Task-Oriented Dialogues

• URL: http://arxiv.org/abs/2502.17268v1
• Date: Mon, 24 Feb 2025 15:51:42 GMT
• Title: MonoTODia: Translating Monologue Requests to Task-Oriented Dialogues
• Authors: Sebastian Steindl, Ulrich Schäfer, Bernd Ludwig,
• Abstract summary: This study investigates a novel approach to sourcing annotated dialogues from existing German monologue material.<n>We fine-tune state-of-the-art Large Language Models for the task of rewriting e-mails as dialogues and annotating them.<n>Our evaluation shows that the dialogues and annotations are of high quality and can serve as a valuable starting point for training TOD systems.
• Score: 0.27309692684728604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data scarcity is one of the main problems when it comes to real-world applications of transformer-based models. This is especially evident for task-oriented dialogue (TOD) systems, which require specialized datasets, that are usually not readily available. This can hinder companies from adding TOD systems to their services. This study therefore investigates a novel approach to sourcing annotated dialogues from existing German monologue material. Focusing on a real-world example, we investigate whether these monologues can be transformed into dialogue formats suitable for training TOD systems. We show the approach with the concrete example of a company specializing in travel bookings via e-mail. We fine-tune state-of-the-art Large Language Models for the task of rewriting e-mails as dialogues and annotating them. To ensure the quality and validity of the generated data, we employ crowd workers to evaluate the dialogues across multiple criteria and to provide gold-standard annotations for the test dataset. We further evaluate the usefulness of the dialogues for training TOD systems. Our evaluation shows that the dialogues and annotations are of high quality and can serve as a valuable starting point for training TOD systems. Finally, we make the annotated dataset publicly available to foster future research.

Related papers

• WavChat: A Survey of Spoken Dialogue Models [66.82775211793547]
  Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain. These advanced spoken dialogue models not only comprehend audio, music, and other speech-related features, but also capture stylistic and timbral characteristics in speech. Despite the progress in spoken dialogue systems, there is a lack of comprehensive surveys that systematically organize and analyze these systems.
  arXiv  Detail & Related papers  (2024-11-15T04:16:45Z)
• Training Zero-Shot Generalizable End-to-End Task-Oriented Dialog System Without Turn-level Dialog Annotations [2.757798192967912]
  This work employs multi-task instruction fine-tuning to create more efficient and scalable task-oriented dialogue systems. Our approach outperforms both state-of-the-art models trained on annotated data and billion-scale parameter off-the-shelf ChatGPT models.
  arXiv  Detail & Related papers  (2024-07-21T04:52:38Z)
• Are LLMs Robust for Spoken Dialogues? [10.855403629160921]
  Large Pre-Trained Language Models have demonstrated state-of-the-art performance in different downstream tasks. Most of the publicly available datasets and benchmarks on task-oriented dialogues focus on written conversations. We have evaluated the performance of LLMs for spoken task-oriented dialogues on the DSTC11 test sets.
  arXiv  Detail & Related papers  (2024-01-04T14:36:38Z)
• SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation [55.82577086422923]
  We provide a feasible definition of dialogue segmentation points with the help of document-grounded dialogues. We release a large-scale supervised dataset called SuperDialseg, containing 9,478 dialogues. We also provide a benchmark including 18 models across five categories for the dialogue segmentation task.
  arXiv  Detail & Related papers  (2023-05-15T06:08:01Z)
• DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization [127.714919036388]
  DIONYSUS is a pre-trained encoder-decoder model for summarizing dialogues in any new domain. Our experiments show that DIONYSUS outperforms existing methods on six datasets.
  arXiv  Detail & Related papers  (2022-12-20T06:21:21Z)
• Controllable Dialogue Simulation with In-Context Learning [39.04491297557292]
  textscDialogic is a dialogue simulation method based on large language model in-context learning. Our method can rapidly expand a small set of dialogue data with minimum or zero human involvement. Our simulated dialogues have near-human fluency and annotation accuracy.
  arXiv  Detail & Related papers  (2022-10-09T06:32:58Z)
• 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)
• 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)
• Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization [29.887562761942114]
  We show how DialoGPT, a pre-trained model for conversational response generation, can be developed as an unsupervised dialogue annotator. We apply DialoGPT to label three types of features on two dialogue summarization datasets, SAMSum and AMI, and employ pre-trained and non pre-trained models as our summarizes.
  arXiv  Detail & Related papers  (2021-05-26T13:50:13Z)

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.