Information Extraction and Human-Robot Dialogue towards Real-life Tasks:
A Baseline Study with the MobileCS Dataset
- URL: http://arxiv.org/abs/2209.13464v1
- Date: Tue, 27 Sep 2022 15:30:43 GMT
- Title: Information Extraction and Human-Robot Dialogue towards Real-life Tasks:
A Baseline Study with the MobileCS Dataset
- Authors: Hong Liu, Hao Peng, Zhijian Ou, Juanzi Li, Yi Huang and Junlan Feng
- Abstract summary: 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.
- Score: 52.22314870976088
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, there have merged a class of task-oriented dialogue (TOD) datasets
collected through Wizard-of-Oz simulated games. However, the Wizard-of-Oz data
are in fact simulated data and thus are fundamentally different from real-life
conversations, which are more noisy and casual. Recently, 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, which is crucial for
building the knowledge base for TOD. This paper mainly presents a baseline
study of the two tasks with the MobileCS dataset. We introduce how the two
baselines are constructed, the problems encountered, and the results. We
anticipate that the baselines can facilitate exciting future research to build
human-robot dialogue systems for real-life tasks.
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) - Does Collaborative Human-LM Dialogue Generation Help Information
Extraction from Human Dialogues? [55.28340832822234]
Problem-solving human dialogues in real applications can be much more complex than existing Wizard-of-Oz collections.
We introduce a human-in-the-loop dialogue generation framework capable of synthesizing realistic dialogues.
arXiv Detail & Related papers (2023-07-13T20:02:50Z) - SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented
Dialogue Agents [72.42049370297849]
SpokenWOZ is a large-scale speech-text dataset for spoken TOD.
Cross-turn slot and reasoning slot detection are new challenges for SpokenWOZ.
arXiv Detail & Related papers (2023-05-22T13:47:51Z) - 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) - 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) - HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on
Tabular and Textual Data [87.67278915655712]
We present a new dialogue dataset, HybriDialogue, which consists of crowdsourced natural conversations grounded on both Wikipedia text and tables.
The conversations are created through the decomposition of complex multihop questions into simple, realistic multiturn dialogue interactions.
arXiv Detail & Related papers (2022-04-28T00:52:16Z) - TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue
Modeling on Spoken Conversations [24.245354500835465]
We propose a novel model-agnostic data augmentation paradigm to boost the robustness of task-oriented dialogue modeling on spoken conversations.
Our approach ranked first in both tasks of DSTC10 Track2, a benchmark for task-oriented dialogue modeling on spoken conversations.
arXiv Detail & Related papers (2021-12-23T10:04:25Z) - SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal
Conversations [9.626560177660634]
We present a new corpus for the Situated and Interactive Multimodal Conversations, SIMMC 2.0, aimed at building a successful multimodal assistant agent.
The dataset features 11K task-oriented dialogs (117K utterances) between a user and a virtual assistant on the shopping domain.
arXiv Detail & Related papers (2021-04-18T00:14:29Z) - Conversations with Search Engines: SERP-based Conversational Response
Generation [77.1381159789032]
We create a suitable dataset, the Search as a Conversation (SaaC) dataset, for the development of pipelines for conversations with search engines.
We also develop a state-of-the-art pipeline for conversations with search engines, the Conversations with Search Engines (CaSE) using this dataset.
CaSE enhances the state-of-the-art by introducing a supporting token identification module and aprior-aware pointer generator.
arXiv Detail & Related papers (2020-04-29T13:07:53Z)
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.