SINC: Service Information Augmented Open-Domain Conversation
- URL: http://arxiv.org/abs/2206.14000v1
- Date: Tue, 28 Jun 2022 13:41:48 GMT
- Title: SINC: Service Information Augmented Open-Domain Conversation
- Authors: Han Zhou, Xinchao Xu, Wenquan Wu, Zhengyu Niu, Hua Wu, Siqi Bao, Fan
Wang, Haifeng Wang
- Abstract summary: We propose a knowledge-driven dialogue system using dynamic service information.
We release the first open domain Chinese service knowledge dialogue dataset DuSinc.
Both automatic evaluation and human evaluation show that our proposed new method can significantly improve the effect of open-domain conversation.
- Score: 46.912064636311825
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generative open-domain dialogue systems can benefit from external knowledge,
but the lack of external knowledge resources and the difficulty in finding
relevant knowledge limit the development of this technology. To this end, we
propose a knowledge-driven dialogue task using dynamic service information.
Specifically, we use a large number of service APIs that can provide high
coverage and spatiotemporal sensitivity as external knowledge sources. The
dialogue system generates queries to request external services along with user
information, get the relevant knowledge, and generate responses based on this
knowledge. To implement this method, we collect and release the first open
domain Chinese service knowledge dialogue dataset DuSinc. At the same time, we
construct a baseline model PLATO-SINC, which realizes the automatic utilization
of service information for dialogue. Both automatic evaluation and human
evaluation show that our proposed new method can significantly improve the
effect of open-domain conversation, and the session-level overall score in
human evaluation is improved by 59.29% compared with the dialogue pre-training
model PLATO-2. The dataset and benchmark model will be open sourced.
Related papers
- AUGUST: an Automatic Generation Understudy for Synthesizing
Conversational Recommendation Datasets [56.052803235932686]
We propose a novel automatic dataset synthesis approach that can generate both large-scale and high-quality recommendation dialogues.
In doing so, we exploit: (i) rich personalized user profiles from traditional recommendation datasets, (ii) rich external knowledge from knowledge graphs, and (iii) the conversation ability contained in human-to-human conversational recommendation datasets.
arXiv Detail & Related papers (2023-06-16T05:27:14Z) - FCC: Fusing Conversation History and Candidate Provenance for Contextual
Response Ranking in Dialogue Systems [53.89014188309486]
We present a flexible neural framework that can integrate contextual information from multiple channels.
We evaluate our model on the MSDialog dataset widely used for evaluating conversational response ranking tasks.
arXiv Detail & Related papers (2023-03-31T23:58:28Z) - PLATO-K: Internal and External Knowledge Enhanced Dialogue Generation [49.43839526180323]
We introduce PLATO-K based on two-stage dialogic learning to strengthen internal knowledge and external knowledge exploitation.
In the first stage, PLATO-K learns through massive dialogue corpora and memorizes essential knowledge into model parameters.
In the second stage, PLATO-K mimics human beings to search for external information and to leverage the knowledge in response generation.
arXiv Detail & Related papers (2022-11-02T06:23:16Z) - OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking
Experience [87.0233567695073]
Existing studies in conversational AI mostly treat task-oriented dialog (TOD) and question answering (QA) as separate tasks.
We propose a new task, Open-Book TOD (OB-TOD), which combines TOD with QA task and expand external knowledge sources.
We propose a unified model OPERA which can appropriately access explicit and implicit external knowledge to tackle the defined task.
arXiv Detail & Related papers (2022-06-24T18:21:26Z) - 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) - Multi-Sentence Knowledge Selection in Open-Domain Dialogue [11.936691632841388]
We evaluate the existing state of open-domain conversation knowledge selection.
We create an augmented dataset based on the Wizard of Wikipedia (WOW) corpus.
WOW++ averages 8 relevant knowledge sentences per dialogue context.
arXiv Detail & Related papers (2022-03-01T22:07:05Z) - Can I Be of Further Assistance? Using Unstructured Knowledge Access to
Improve Task-oriented Conversational Modeling [39.60614611655266]
This work focuses on responding to these beyond-API-coverage user turns by incorporating external, unstructured knowledge sources.
We introduce novel data augmentation methods for the first two steps and demonstrate that the use of information extracted from dialogue context improves the knowledge selection and end-to-end performances.
arXiv Detail & Related papers (2021-06-16T23:31:42Z) - Learning to Select External Knowledge with Multi-Scale Negative Sampling [31.833572852656008]
The Track-1 of DSTC9 aims to effectively answer user requests or questions during task-oriented dialogues.
By leveraging external knowledge resources, relevant information can be retrieved and encoded into the response generation for these out-of-API-coverage queries.
arXiv Detail & Related papers (2021-02-03T14:59:35Z) - Beyond Domain APIs: Task-oriented Conversational Modeling with
Unstructured Knowledge Access [18.37585134613816]
In this paper, we propose to expand coverage of task-oriented dialogue systems by incorporating external unstructured knowledge sources.
We define three sub-tasks: knowledge-seeking turn detection, knowledge selection, and knowledge-grounded response generation.
We introduce an augmented version of MultiWOZ 2.1, which includes new out-of-API-coverage turns and responses grounded on external knowledge sources.
arXiv Detail & Related papers (2020-06-05T16:12:18Z)
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.