[Paper Review] Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models

Meng Zhao, Lili Mou+1 more|arXiv (Cornell University)|Aug 10, 2017

Topic Modeling17 references3 citations

TL;DR

This paper proposes speaker classification as a surrogate task for neural speaker modeling in multi-party conversations, introducing a large-scale dataset from CNN TV talk show transcripts. It combines content-based and temporal-based modeling via interpolation, showing that hybrid models significantly outperform single-component approaches, with simple post-training interpolation yielding the best performance on macro F1 score.

ABSTRACT

Neural network-based dialog systems are attracting increasing attention in both academia and industry. Recently, researchers have begun to realize the importance of speaker modeling in neural dialog systems, but there lacks established tasks and datasets. In this paper, we propose speaker classification as a surrogate task for general speaker modeling, and collect massive data to facilitate research in this direction. We further investigate temporal-based and content-based models of speakers, and propose several hybrids of them. Experiments show that speaker classification is feasible, and that hybrid models outperform each single component.

Motivation & Objective

• To establish a standardized task for neural speaker modeling in multi-party conversations, addressing the lack of benchmark tasks and datasets.
• To collect and release a large-scale, publicly available dataset of multi-party conversational transcripts from TV talk shows for speaker modeling research.
• To investigate and compare content-based, temporal-based, and hybrid models for speaker classification.
• To evaluate the effectiveness of combining speaker content and temporal order information in neural speaker modeling.
• To provide a foundation for future research in speaker-aware dialog systems and related NLP applications.

Proposed method

• Proposes speaker classification as a surrogate task: predicting the speaker of a given utterance sequence from a candidate set.
• Represents utterances using a bidirectional LSTM to encode contextual content into a dense vector representation.
• Models speakers using either content-based embeddings (from utterances they produced) or temporal-based embeddings (from their relative position in the conversation flow).
• Combines content and temporal representations via interpolation or learnable gating mechanisms, with the gate conditioned on the confidence of content-based predictions.
• Employs a softmax-based classification layer using speaker embeddings as learnable parameters, with cross-entropy loss for training.
• Uses a hybrid training strategy: train content and temporal models separately, then interpolate their predictions using a validated hyperparameter g.

Experimental results

Research questions

• RQ1 Can speaker classification serve as a viable surrogate task for general neural speaker modeling in multi-party conversations?
• RQ2 How effective is combining content-based and temporal-based speaker representations in improving classification performance?
• RQ3 Does simple interpolation of pre-trained models outperform end-to-end hybrid training with gating mechanisms?
• RQ4 How sensitive is the performance to the hyperparameter controlling the balance between content and temporal information?
• RQ5 Can the proposed method generalize to rare or unseen speakers in low-resource settings?

Key findings

• The proposed speaker classification task is feasible and effective, with content-based models significantly outperforming majority-class baselines.
• Hybrid models that combine content and temporal information achieve higher macro F1 scores than either component alone, demonstrating complementary strengths.
• Simple interpolation of separately trained content and temporal models outperforms both end-to-end gating mechanisms and self-adaptive gating, suggesting better optimization stability.
• The optimal interpolation weight g lies in the range (0.6, 0.9), indicating that content information contributes more than temporal information in the best-performing models.
• The self-adaptive gating mechanism shows potential for dynamic balancing but does not surpass the fixed interpolation baseline in this setup.
• The dataset of over 8,000 TV talk show episodes provides a rich, diverse resource for speaker modeling research, supporting future work in dialog systems and speech processing.