Related papers: Conversational Tree Search: A New Hybrid Dialog Task

Conversational Tree Search: A New Hybrid Dialog Task

URL: http://arxiv.org/abs/2303.10227v1
Date: Fri, 17 Mar 2023 19:50:51 GMT
Title: Conversational Tree Search: A New Hybrid Dialog Task
Authors: Dirk V\"ath, Lindsey Vanderlyn, Ngoc Thang Vu
Abstract summary: We introduce Conversational Tree Search (CTS) as a new task that bridges the gap between FAQ-style information retrieval and task-oriented dialog. Our results show that the new architecture combines the positive aspects of both the FAQ and dialog system used in the baseline and achieves higher goal completion.
Score: 21.697256733634124
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Conversational interfaces provide a flexible and easy way for users to seek information that may otherwise be difficult or inconvenient to obtain. However, existing interfaces generally fall into one of two categories: FAQs, where users must have a concrete question in order to retrieve a general answer, or dialogs, where users must follow a predefined path but may receive a personalized answer. In this paper, we introduce Conversational Tree Search (CTS) as a new task that bridges the gap between FAQ-style information retrieval and task-oriented dialog, allowing domain-experts to define dialog trees which can then be converted to an efficient dialog policy that learns only to ask the questions necessary to navigate a user to their goal. We collect a dataset for the travel reimbursement domain and demonstrate a baseline as well as a novel deep Reinforcement Learning architecture for this task. Our results show that the new architecture combines the positive aspects of both the FAQ and dialog system used in the baseline and achieves higher goal completion while skipping unnecessary questions.

Related papers

Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations [66.16863141262506]
We present a novel approach that focuses on generating internet search queries guided by social commonsense. Our proposed framework addresses passive user interactions by integrating topic tracking, commonsense response generation and instruction-driven query generation.
arXiv Detail & Related papers (2023-10-22T16:14:56Z)
"What do others think?": Task-Oriented Conversational Modeling with Subjective Knowledge [69.46702968979885]
Task-oriented Dialogue (TOD) Systems aim to build dialogue systems that assist users in accomplishing specific goals. Traditional TODs rely on domain-specific APIs/DBs or external factual knowledge to generate responses. We propose a novel task of subjective-knowledge-based TOD (SK-TOD)
arXiv Detail & Related papers (2023-05-20T04:43:26Z)
Manual-Guided Dialogue for Flexible Conversational Agents [84.46598430403886]
How to build and use dialogue data efficiently, and how to deploy models in different domains at scale can be critical issues in building a task-oriented dialogue system. We propose a novel manual-guided dialogue scheme, where the agent learns the tasks from both dialogue and manuals. Our proposed scheme reduces the dependence of dialogue models on fine-grained domain ontology, and makes them more flexible to adapt to various domains.
arXiv Detail & Related papers (2022-08-16T08:21:12Z)
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-stage Clarification in Conversational AI: The case of Question-Answering Dialogue Systems [0.27998963147546135]
Clarification resolution plays an important role in various information retrieval tasks such as interactive question answering and conversational search. We propose a multi-stage clarification mechanism for prompting clarification and query selection in the context of a question answering dialogue system. Our proposed mechanism improves the overall user experience and outperforms competitive baselines with two datasets.
arXiv Detail & Related papers (2021-10-28T15:45:44Z)
Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions [65.60888490988236]
We release a dataset focused on open-domain single- and multi-turn conversations. We benchmark several state-of-the-art neural baselines. We propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues.
arXiv Detail & Related papers (2021-09-13T09:16:14Z)
CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation [62.13413129518165]
CR-Walker is a model that performs tree-structured reasoning on a knowledge graph. It generates informative dialog acts to guide language generation. Automatic and human evaluations show that CR-Walker can arrive at more accurate recommendation.
arXiv Detail & Related papers (2020-10-20T14:53:22Z)
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)
DoQA -- Accessing Domain-Specific FAQs via Conversational QA [25.37327993590628]
We present DoQA, a dataset with 2,437 dialogues and 10,917 QA pairs. The dialogues are collected from three Stack Exchange sites using the Wizard of Oz method with crowdsourcing.
arXiv Detail & Related papers (2020-05-04T08:58:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.