NatCS: Eliciting Natural Customer Support Dialogues
- URL: http://arxiv.org/abs/2305.03007v1
- Date: Thu, 4 May 2023 17:25:24 GMT
- Title: NatCS: Eliciting Natural Customer Support Dialogues
- Authors: James Gung, Emily Moeng, Wesley Rose, Arshit Gupta, Yi Zhang, Saab
Mansour
- Abstract summary: Existing task-oriented dialogue datasets are not representative of real customer support conversations.
We introduce NatCS, a multi-domain collection of spoken customer service conversations.
- Score: 5.398732055835996
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Despite growing interest in applications based on natural customer support
conversations, there exist remarkably few publicly available datasets that
reflect the expected characteristics of conversations in these settings.
Existing task-oriented dialogue datasets, which were collected to benchmark
dialogue systems mainly in written human-to-bot settings, are not
representative of real customer support conversations and do not provide
realistic benchmarks for systems that are applied to natural data. To address
this gap, we introduce NatCS, a multi-domain collection of spoken customer
service conversations. We describe our process for collecting synthetic
conversations between customers and agents based on natural language phenomena
observed in real conversations. Compared to previous dialogue datasets, the
conversations collected with our approach are more representative of real
human-to-human conversations along multiple metrics. Finally, we demonstrate
potential uses of NatCS, including dialogue act classification and intent
induction from conversations as potential applications, showing that dialogue
act annotations in NatCS provide more effective training data for modeling real
conversations compared to existing synthetic written datasets. We publicly
release NatCS to facilitate research in natural dialog systems
Related papers
- Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems [29.394466123216258]
This study provides a comprehensive overview of the primary characteristics of a dialogue agent, their corresponding open-domain datasets, and the methods used to benchmark these datasets.
We propose UNIT, a UNified dIalogue dataseT constructed from conversations of existing datasets for different dialogue tasks capturing the nuances for each of them.
arXiv Detail & Related papers (2023-07-14T10:05:47Z) - 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) - PLACES: Prompting Language Models for Social Conversation Synthesis [103.94325597273316]
We use a small set of expert-written conversations as in-context examples to synthesize a social conversation dataset using prompting.
We perform several thorough evaluations of our synthetic conversations compared to human-collected conversations.
arXiv Detail & Related papers (2023-02-07T05:48:16Z) - Back to the Future: Bidirectional Information Decoupling Network for
Multi-turn Dialogue Modeling [80.51094098799736]
We propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder.
BiDeN explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks.
Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN.
arXiv Detail & Related papers (2022-04-18T03:51:46Z) - "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) - Topic-Oriented Spoken Dialogue Summarization for Customer Service with
Saliency-Aware Topic Modeling [61.67321200994117]
In a customer service system, dialogue summarization can boost service efficiency by creating summaries for long spoken dialogues.
In this work, we focus on topic-oriented dialogue summarization, which generates highly abstractive summaries.
We propose a novel topic-augmented two-stage dialogue summarizer ( TDS) jointly with a saliency-aware neural topic model (SATM) for topic-oriented summarization of customer service dialogues.
arXiv Detail & Related papers (2020-12-14T07:50:25Z) - Dialogue-Based Relation Extraction [53.2896545819799]
We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE.
We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks.
Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings.
arXiv Detail & Related papers (2020-04-17T03:51:57Z) - Interview: A Large-Scale Open-Source Corpus of Media Dialog [11.28504775964698]
We introduce 'Interview': a large-scale (105K conversations) media dialog dataset collected from news interview transcripts.
Compared to existing large-scale proxies for conversational data, language models trained on our dataset exhibit better zero-shot out-of-domain performance.
'Interview' contains speaker role annotations for each turn, facilitating the development of engaging, responsive dialog systems.
arXiv Detail & Related papers (2020-04-07T02:44:50Z)
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.