Athena: Constructing Dialogues Dynamically with Discourse Constraints
- URL: http://arxiv.org/abs/2011.10683v1
- Date: Sat, 21 Nov 2020 00:28:34 GMT
- Title: Athena: Constructing Dialogues Dynamically with Discourse Constraints
- Authors: Vrindavan Harrison, Juraj Juraska, Wen Cui, Lena Reed, Kevin K.
Bowden, Jiaqi Wu, Brian Schwarzmann, Abteen Ebrahimi, Rishi Rajasekaran,
Nikhil Varghese, Max Wechsler-Azen, Steve Whittaker, Jeffrey Flanigan, and
Marilyn Walker
- Abstract summary: This report describes Athena, a dialogue system for spoken conversation on popular topics and current events.
We develop a flexible topic-agnostic approach to dialogue management that dynamically configures dialogue based on general principles of entity and topic coherence.
After describing the dialogue system architecture, we perform an analysis of conversations that Athena participated in during the 2019 Alexa Prize Competition.
- Score: 11.008755264048522
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This report describes Athena, a dialogue system for spoken conversation on
popular topics and current events. We develop a flexible topic-agnostic
approach to dialogue management that dynamically configures dialogue based on
general principles of entity and topic coherence. Athena's dialogue manager
uses a contract-based method where discourse constraints are dispatched to
clusters of response generators. This allows Athena to procure responses from
dynamic sources, such as knowledge graph traversals and feature-based
on-the-fly response retrieval methods. After describing the dialogue system
architecture, we perform an analysis of conversations that Athena participated
in during the 2019 Alexa Prize Competition. We conclude with a report on
several user studies we carried out to better understand how individual user
characteristics affect system ratings.
Related papers
- Dialog-to-Actions: Building Task-Oriented Dialogue System via
Action-Level Generation [7.110201160927713]
We propose a task-oriented dialogue system via action-level generation.
Specifically, we first construct dialogue actions from large-scale dialogues and represent each natural language (NL) response as a sequence of dialogue actions.
We train a Sequence-to-Sequence model which takes the dialogue history as input and outputs sequence of dialogue actions.
arXiv Detail & Related papers (2023-04-03T11:09:20Z) - 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) - Let's Get Personal: Personal Questions Improve SocialBot Performance in
the Alexa Prize [0.0]
There has been an increased focus on creating conversational open-domain dialogue systems in the spoken dialogue community.
Unlike traditional dialogue systems, these conversational systems cannot assume any specific information need or domain restrictions.
We developed a robust open-domain conversational system, Athena, that real Amazon Echo users access and evaluate at scale.
arXiv Detail & Related papers (2023-03-09T00:10:29Z) - A Transformer-based Response Evaluator for Open-Domain Spoken
Conversation [1.0474108328884806]
We study response selection in the Athena system, an Alexa Prize SocialBot.
We compare several off-the-shelf response ranking methods for open-domain dialogue.
We find that Athena-RR with a Recall@1 of 70.79% outperforms Athena-Heuristic and all of the off-the-shelf rankers by a large margin.
arXiv Detail & Related papers (2023-02-09T03:38:07Z) - 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) - User Satisfaction Estimation with Sequential Dialogue Act Modeling in
Goal-oriented Conversational Systems [65.88679683468143]
We propose a novel framework, namely USDA, to incorporate the sequential dynamics of dialogue acts for predicting user satisfaction.
USDA incorporates the sequential transitions of both content and act features in the dialogue to predict the user satisfaction.
Experimental results on four benchmark goal-oriented dialogue datasets show that the proposed method substantially and consistently outperforms existing methods on USE.
arXiv Detail & Related papers (2022-02-07T02:50:07Z) - Saying No is An Art: Contextualized Fallback Responses for Unanswerable
Dialogue Queries [3.593955557310285]
Most dialogue systems rely on hybrid approaches for generating a set of ranked responses.
We design a neural approach which generates responses which are contextually aware with the user query.
Our simple approach makes use of rules over dependency parses and a text-to-text transformer fine-tuned on synthetic data of question-response pairs.
arXiv Detail & Related papers (2020-12-03T12:34:22Z) - Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph [0.9236074230806579]
We present the third version of the open-domain dialogue system Alquist developed within the Alexa Prize 2020 competition.
The main novel contribution is the introduction of a system based on a conversational knowledge graph and adjacency pairs.
We discuss and describe Alquist's pipeline, data acquisition and processing, dialogue manager, NLG, knowledge aggregation, and a hierarchy of adjacency pairs.
arXiv Detail & Related papers (2020-11-06T10:10:02Z) - Is this Dialogue Coherent? Learning from Dialogue Acts and Entities [82.44143808977209]
We create the Switchboard Coherence (SWBD-Coh) corpus, a dataset of human-human spoken dialogues annotated with turn coherence ratings.
Our statistical analysis of the corpus indicates how turn coherence perception is affected by patterns of distribution of entities.
We find that models combining both DA and entity information yield the best performances both for response selection and turn coherence rating.
arXiv Detail & Related papers (2020-06-17T21:02:40Z) - Rethinking Dialogue State Tracking with Reasoning [76.0991910623001]
This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data.
Empirical results demonstrate that our method significantly outperforms the state-of-the-art methods by 38.6% in terms of joint belief accuracy for MultiWOZ 2.1.
arXiv Detail & Related papers (2020-05-27T02:05:33Z) - 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)
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.