A Review of Dialogue Systems: From Trained Monkeys to Stochastic Parrots

• URL: http://arxiv.org/abs/2111.01414v1
• Date: Tue, 2 Nov 2021 08:07:55 GMT
• Title: A Review of Dialogue Systems: From Trained Monkeys to Stochastic Parrots
• Authors: Atharv Singh Patlan, Shiven Tripathi, Shubham Korde
• Abstract summary: We aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. We present a broad overview of methods developed to build dialogue systems over the years.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In spoken dialogue systems, we aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. Dialogue systems are increasingly being designed to move beyond just imitating conversation and also improve from such interactions over time. In this survey, we present a broad overview of methods developed to build dialogue systems over the years. Different use cases for dialogue systems ranging from task-based systems to open domain chatbots motivate and necessitate specific systems. Starting from simple rule-based systems, research has progressed towards increasingly complex architectures trained on a massive corpus of datasets, like deep learning systems. Motivated with the intuition of resembling human dialogues, progress has been made towards incorporating emotions into the natural language generator, using reinforcement learning. While we see a trend of highly marginal improvement on some metrics, we find that limited justification exists for the metrics, and evaluation practices are not uniform. To conclude, we flag these concerns and highlight possible research directions.

Related papers

• WavChat: A Survey of Spoken Dialogue Models [66.82775211793547]
  Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain. These advanced spoken dialogue models not only comprehend audio, music, and other speech-related features, but also capture stylistic and timbral characteristics in speech. Despite the progress in spoken dialogue systems, there is a lack of comprehensive surveys that systematically organize and analyze these systems.
  arXiv  Detail & Related papers  (2024-11-15T04:16:45Z)
• PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model [79.64376762489164]
  PK-Chat is a Pointer network guided generative dialogue model, incorporating a unified pretrained language model and a pointer network over knowledge graphs. The words generated by PK-Chat in the dialogue are derived from the prediction of word lists and the direct prediction of the external knowledge graph knowledge. Based on the PK-Chat, a dialogue system is built for academic scenarios in the case of geosciences.
  arXiv  Detail & Related papers  (2023-04-02T18:23:13Z)
• From Knowledge Augmentation to Multi-tasking: Towards Human-like Dialogue Systems [0.0]
  The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers. In this thesis, we focus on methods that address the numerous issues that have been imposing the gap between artificial conversational agents and human-level interlocutors.
  arXiv  Detail & Related papers  (2022-11-14T17:27:07Z)
• Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review [40.49926141538684]
  Visual Context Augmented Dialogue System (VAD) has the potential to communicate with humans by perceiving and understanding multimodal information. VAD possesses the potential to generate engaging and context-aware responses.
  arXiv  Detail & Related papers  (2022-07-02T09:31:37Z)
• Building a Personalized Dialogue System with Prompt-Tuning [5.942602139622984]
  We build a dialogue system that responds based on a given character setting (persona) We propose an approach that uses prompt-tuning, which has low learning costs, on pre-trained large-scale language models.
  arXiv  Detail & Related papers  (2022-06-11T02:21:11Z)
• UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues [59.499965460525694]
  We propose a unified dialogue system (UniDS) with the two aforementioned skills. We design a unified dialogue data schema, compatible for both chit-chat and task-oriented dialogues. We train UniDS with mixed dialogue data from a pretrained chit-chat dialogue model.
  arXiv  Detail & Related papers  (2021-10-15T11:56:47Z)
• Advances in Multi-turn Dialogue Comprehension: A Survey [51.215629336320305]
  Training machines to understand natural language and interact with humans is an elusive and essential task of artificial intelligence. This paper reviews the previous methods from the technical perspective of dialogue modeling for the dialogue comprehension task. In addition, we categorize dialogue-related pre-training techniques which are employed to enhance PrLMs in dialogue scenarios.
  arXiv  Detail & Related papers  (2021-10-11T03:52:37Z)
• Recent Advances in Deep Learning-based Dialogue Systems [12.798560005546262]
  We mainly focus on the deep learning-based dialogue systems. This survey is the most comprehensive and upto-date one at present in the area of dialogue systems and dialogue-related tasks.
  arXiv  Detail & Related papers  (2021-05-10T14:07:49Z)
• Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems [21.98135285833616]
  Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. We present a new approach for building goal-oriented dialogue systems that is scalable, as well as data efficient.
  arXiv  Detail & Related papers  (2021-04-19T07:09:27Z)
• Advances in Multi-turn Dialogue Comprehension: A Survey [51.215629336320305]
  We review the previous methods from the perspective of dialogue modeling. We discuss three typical patterns of dialogue modeling that are widely-used in dialogue comprehension tasks.
  arXiv  Detail & Related papers  (2021-03-04T15:50:17Z)
• Recent Advances and Challenges in Task-oriented Dialog System [63.82055978899631]
  Task-oriented dialog systems are attracting more and more attention in academic and industrial communities. We discuss three critical topics for task-oriented dialog systems: (1) improving data efficiency to facilitate dialog modeling in low-resource settings, (2) modeling multi-turn dynamics for dialog policy learning, and (3) integrating domain knowledge into the dialog model.
  arXiv  Detail & Related papers  (2020-03-17T01:34:56Z)

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.