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SS-MPC: A Sequence-Structured Multi-Party Conversation System
Computation and Language · Submitted 24 Feb 2025
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Experimental results show that \textbf{SS-MPC} achieves \textbf{15.60\% BLEU-1} and \textbf{12.44\% ROUGE-L} score, outperforming the current state-of-the-art MPC response generation model by \textbf{3.91\%p} in \textbf{BLEU-1} and \textbf{0.62\%p} in \textbf{ROUGE-L}.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
Recent Multi-Party Conversation (MPC) models typically rely on graph-based approaches to capture dialogue structures. However, these methods have limitations, such as information loss during the projection of utterances into structural embeddings and constraints in leveraging pre-trained language models directly. In this paper, we propose \textbf{SS-MPC}, a response generation model for MPC that eliminates the need for explicit graph structures. Unlike existing models that depend on graphs to analyze conversation structures, SS-MPC internally encodes the dialogue structure as a sequential input, enabling direct utilization of pre-trained language models. Experimental results show that \textbf{SS-MPC} achieves \textbf{15.60\% BLEU-1} and \textbf{12.44\% ROUGE-L} score, outperforming the current state-of-the-art MPC response generation model by \textbf{3.91\%p} in \textbf{BLEU-1} and \textbf{0.62\%p} in \textbf{ROUGE-L}. Additionally, human evaluation confirms that SS-MPC generates more fluent and accurate responses compared to existing MPC models.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Recent Multi-Party Conversation (MPC) models typically rely on graph-based approaches to capture dialogue structures. | background 1.00 | 0.03 | 0.02 | asserted 1.00 |
| s1 | However, these methods have limitations, such as information loss during the projection of utterances into structural embeddings and constraints in leveraging pre-trained language models directly. | background 0.56 | 0.06 | 0.02 | asserted 0.99 |
| s2 | In this paper, we propose \textbf{SS-MPC}, a response generation model for MPC that eliminates the need for explicit graph structures. | contribution 0.99 | 0.84 | 0.23 | asserted 1.00 |
| s3 | Unlike existing models that depend on graphs to analyze conversation structures, SS-MPC internally encodes the dialogue structure as a sequential input, enabling direct utilization of pre-trained language models. | method 0.91 | 0.70 | 0.14 | asserted 1.00 |
| s4 | Experimental results show that \textbf{SS-MPC} achieves \textbf{15.60\% BLEU-1} and \textbf{12.44\% ROUGE-L} score, outperforming the current state-of-the-art MPC response generation model by \textbf{3.91\%p} in \textbf{BLEU-1} and \textbf{0.62\%p} in \textbf{ROUGE-L}. | result 1.00 | 0.97 | 0.54 | asserted 1.00 |
| s5 | Additionally, human evaluation confirms that SS-MPC generates more fluent and accurate responses compared to existing MPC models. | result 1.00 | 0.93 | 0.24 | asserted 1.00 |
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The paper
Original abstract
Recent Multi-Party Conversation (MPC) models typically rely on graph-based approaches to capture dialogue structures. However, these methods have limitations, such as information loss during the projection of utterances into structural embeddings and constraints in leveraging pre-trained language models directly. In this paper, we propose \textbf{SS-MPC}, a response generation model for MPC that eliminates the need for explicit graph structures. Unlike existing models that depend on graphs to analyze conversation structures, SS-MPC internally encodes the dialogue structure as a sequential input, enabling direct utilization of pre-trained language models. Experimental results show that \textbf{SS-MPC} achieves \textbf{15.60\% BLEU-1} and \textbf{12.44\% ROUGE-L} score, outperforming the current state-of-the-art MPC response generation model by \textbf{3.91\%p} in \textbf{BLEU-1} and \textbf{0.62\%p} in \textbf{ROUGE-L}. Additionally, human evaluation confirms that SS-MPC generates more fluent and accurate responses compared to existing MPC models.
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How the literature uses it
1 arXiv paper cite this in 5 sentences
Papers citing it · 3 sentences without a confident label
Compares with it 1 paper
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Baselines To verify the effectiveness of DRCR, we compared it with advanced baselines, including GSN [ref] , HeterMPS [ref] , EMMDG [ref] , MADNet [ref] , RL-TRC [ref] , and SS-MPC [ref] .
Discourse Coherence and Response-Guided Context Rewriting for Multi-Party Dialogue Generation · Experimental Settings · 0.99
Cites it as background 1 paper
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Previous work on MDG falls into two paradigms: explicit [ref] and implicit [ref] incorporation of dialogue structure information.
Discourse Coherence and Response-Guided Context Rewriting for Multi-Party Dialogue Generation · Related Work · 0.90
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