mlsanityData to 7 Oct 2026

Speaker Profiling in Multiparty Conversations

arXiv 2304.08801 ↗First posted 18 Apr 2023cs.CL cs.AIcited by 0

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Abstract (arXiv)

In conversational settings, individuals exhibit unique behaviors, rendering a one-size-fits-all approach insufficient for generating responses by dialogue agents. Although past studies have aimed to create personalized dialogue agents using speaker persona information, they have relied on the assumption that the speaker's persona is already provided. However, this assumption is not always valid, especially when it comes to chatbots utilized in industries like banking, hotel reservations, and airline bookings. This research paper aims to fill this gap by exploring the task of Speaker Profiling in Conversations (SPC). The primary objective of SPC is to produce a summary of persona characteristics for each individual speaker present in a dialogue. To accomplish this, we have divided the task into three subtasks: persona discovery, persona-type identification, and persona-value extraction. Given a dialogue, the first subtask aims to identify all utterances that contain persona information. Subsequently, the second task evaluates these utterances to identify the type of persona information they contain, while the third subtask identifies the specific persona values for each identified type. To address the task of SPC, we have curated a new dataset named SPICE, which comes with specific labels. We have evaluated various baselines on this dataset and benchmarked it with a new neural model, SPOT, which we introduce in this paper. Furthermore, we present a comprehensive analysis of SPOT, examining the limitations of individual modules both quantitatively and qualitatively.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 51 references that are in the map. First posted on arXiv 18 Apr 2023.

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References · 28

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  6. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  7. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  8. 2018Personalizing Dialogue Agents: I have a dog, do you have pets too?cited by 820
  9. 2018MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversationscited by 636
  10. 2020Recipes for building an open-domain chatbotcited by 593
  11. 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
  12. 2016A Persona-Based Neural Conversation Modelcited by 221
16 more
  1. 2017Deep Learning for Hate Speech Detection in Tweetscited by 180
  2. 2018Dialogue Natural Language Inferencecited by 135
  3. 2019Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advancescited by 118
  4. 2018Retrieve and Refine: Improved Sequence Generation Models For Dialoguecited by 82
  5. 2020DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognitioncited by 78
  6. 2020Deep Open Intent Classification with Adaptive Decision Boundarycited by 56
  7. 2019Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Networkcited by 38
  8. 2020Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classificationcited by 29
  9. 2017Personalization in Goal-Oriented Dialogcited by 28
  10. 2019Hierarchical Reinforcement Learning for Open-Domain Dialogcited by 27
  11. 2019Getting To Know You: User Attribute Extraction from Dialoguescited by 19
  12. 2019Listening between the Lines: Learning Personal Attributes from Conversationscited by 15
  13. 2018Learning Personas from Dialogue with Attentive Memory Networkscited by 12
  14. 2021Detecting Speaker Personas from Conversational Textscited by 8
  15. 2020Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategycited by 5
  16. 2020Listener's Social Identity Matters in Personalised Response Generationcited by 4

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