---
title: 'RPG-MoGe: Relation Prompt-Guided Generative Ensemble'
url: https://www.emergentmind.com/topics/relation-prompt-guided-multi-order-generative-ensemble-rpg-moge
type: topic
---

# RPG-MoGe: Relation Prompt-Guided Generative Ensemble

Searching arXiv for the cited papers to ground the article in current records.
Relation Prompt-Guided Multi-Order Generative Ensemble (RPG-MoGe) is an end-to-end framework for Speech Relation Extraction (SpeechRE), introduced together with the CommonVoice-SpeechRE benchmark in "CommonVoice-SpeechRE and RPG-MoGe: Advancing Speech Relation Extraction with a New Dataset and Multi-Order Generative Framework" [2509.08438]. SpeechRE is defined as the task of directly extracting a set of relational triples from a speech signal, rather than relying on an intermediate manual text annotation step at inference time. In the paper’s formulation, given a speech signal $\boldsymbol{S}$, the model predicts
\[
\varGamma = \{ (h_i, r_i, t_i) \mid h_i, t_i \in \text{E},\; r_i \in \text{R} \},
\]
where $\text{E}$ is the set of entities in the latent speech transcript and $\text{R}$ is a predefined set of relation types. RPG-MoGe addresses three limitations identified in prior SpeechRE work: heavy reliance on synthetic speech benchmarks, rigid single-order generation templates, and weak semantic alignment between speech representations and generated relations [2509.08438].

## 1. Problem formulation and design rationale

RPG-MoGe is motivated by a specific diagnosis of earlier SpeechRE systems. Prior benchmark datasets such as CoNLL04-SpeechRE and ReTACRED-SpeechRE are described as being created mostly by applying TTS to existing text relation extraction corpora, with real human speech being limited and often confined to the test set, and with very few speakers in training, such as 4 or 8. The paper argues that this leads to poor robustness to real-world acoustic variability, including accents, speaking styles, and noise [2509.08438].

A second motivation concerns output representation. Earlier generative SpeechRE models generate triples in one fixed order, typically of the form `\<h> ... \<r> ... \<t> ...`. RPG-MoGe is built on the observation that entities and relations can appear in various linear orders, so constraining the model to a single order wastes data diversity and makes learning template-specific. A third motivation concerns semantic alignment: prior cross-modal alignment methods are described as focusing mainly on low- or mid-level feature similarity and as not injecting higher-level structure such as which relations are likely present in the utterance [2509.08438].

These concerns define the framework’s two principal interventions. First, RPG-MoGe uses a multi-order generation ensemble rather than a single fixed template. Second, it introduces explicit high-level relation guidance from speech through a CNN-based latent relation prediction head, whose outputs are converted into relation prompts fed into the decoder. The result is a jointly trained system combining a speech encoder, a relation prediction head, and a generative decoder on real speech and synthetic speech [2509.08438].

## 2. Core architecture and data flow

The framework contains three core modules: a Whisper speech encoder, a latent relation prediction head (LRPH), and a Whisper text decoder [2509.08438]. The input raw waveform $\boldsymbol{S}$ is first converted into a log-mel spectrogram,
\[
\boldsymbol{X} = \text{LogMel}(\boldsymbol{S}),
\]
which is then encoded as
\[
\boldsymbol{H} = \text{WhisperEncoder}(\boldsymbol{X}) \in \mathbb{R}^{L_H \times d_h}.
\]
The encoder thus produces high-level audio features used by both the classification and generation branches [2509.08438].

The LRPH is a CNN-based multi-label classifier over $\boldsymbol{H}$ that predicts which relation types from $\text{R}$ are likely present in the utterance. Its outputs are turned into relation tokens, denoted $\boldsymbol{T}_{rel}$, which function as relation prompts. The decoder is conditioned jointly on $\boldsymbol{H}$ and these prompts and generates a linearized relation tree encoding one or more triples under a selected order view [2509.08438].

For each order view $\psi_i$, the set of gold triples $\mathcal{T}$ is converted into a view-specific tree and then linearized:
\[
\mathcal{G}_{\psi_i} = \text{Treeify}(\mathcal{T}, \psi_i), \quad \psi_i \in \varPsi,\; |\varPsi| = 6,
\]
\[
T_{lin}^{\psi_i} = \text{SeqLin}(\mathcal{G}_{\psi_i}).
\]
The decoder input is the concatenation of relation prompts, order-control tokens, and the linearized tree:
\[
\boldsymbol{T}_{dec} = [\boldsymbol{T}_{rel},\; \boldsymbol{T}_{\psi_i}^{ctrl},\; \boldsymbol{T}_{\psi_i}^{lin}].
\]
The order-control tokens are constructed by permuting the special markers `\<h>`, `\<r>`, and `\<t>` according to the selected permutation [2509.08438].

A recurrent misunderstanding is that RPG-MoGe uses multiple decoders. It does not. The framework uses one shared decoder, and multi-order behavior is realized by multiple passes with different control tokens and training targets [2509.08438].

## 3. Multi-order triplet generation and ensemble voting

The expression “multi-order” refers to the fact that each triple $(h,r,t)$ is considered under all six permutations of its elements:
$(h,r,t)$, $(h,t,r)$, $(r,h,t)$, $(r,t,h)$, $(t,h,r)$, and $(t,r,h)$ [2509.08438]. For each permutation, RPG-MoGe constructs a corresponding relation tree view and trains the same decoder to generate the associated linearized sequence.

During training, each speech sample is expanded into six training targets, one for each order view. The decoder is trained on all six views using the same encoder representation $\boldsymbol{H}$ and the same relation prompts $\boldsymbol{T}_{rel}$, while varying the order-control tokens and target sequences. The decoder loss is standard token-level cross-entropy, aggregated across views; the paper notes that in implementation one sums or averages the six view-specific losses into a single $\mathcal{L}_{dec}$ [2509.08438].

At inference time, the speech is encoded once, relation prompts are predicted once, and the decoder is run six times, once for each order view. Each generated sequence is parsed into a triple set $\mathcal{T}^{(\psi_i)}$. The final output is obtained by vote-based ensembling:
\[
\mathcal{T}^{final} = \{ \tau \mid \text{vote}(\tau) \ge \lambda_{vote} \},
\]
with $\lambda_{vote}=2$ in all experiments [2509.08438]. This criterion accepts a triple if it appears in at least two view-specific outputs.

The significance of this design is methodological rather than merely combinatorial. The paper argues that multi-order training exploits diversity in the linear arrangement of triple elements, while multi-order inference adds a consistency filter through voting. A second common misconception is that the ensemble is a collection of separate models; in fact, it is a single model queried under six different order prompts [2509.08438].

## 4. Relation prompts and relation-aware cross-modal alignment

The latent relation prediction head receives $\boldsymbol{H} \in \mathbb{R}^{L_H \times d_h}$ and applies four 2D CNN layers with ReLU activations, kernel size 3, and progressively increasing channels 16 $\rightarrow$ 32 $\rightarrow$ 64 $\rightarrow$ 128. The resulting feature map is flattened and passed through a linear layer with sigmoid activation to produce multi-label relation scores $\boldsymbol{score}^{(R)} \in \mathbb{R}^{|\text{R}|}$ [2509.08438]. The corresponding loss is binary cross-entropy over the ground-truth relation indicator vector:
\[
\mathcal{L}_{lrp} = -\frac{1}{|\text{R}|} \sum_{i=1}^{|\text{R}|} \Big[ y_i^{(R)} \log(\text{score}_i^{(R)}) + (1 - y_i^{(R)}) \log(1 - \text{score}_i^{(R)}) \Big].
\]

These scores are converted conceptually into relation prompts. The paper states that relation types can be selected by thresholding or by taking the top-$k$ highest-scoring labels, then mapped to special prompt tokens such as `[REL_EMPLOYMENT]` or `[REL_LOCATED_IN]`. The precise tokenization and selection threshold are not fully specified, and this suggests that prompt construction remains an implementation-dependent component of the framework [2509.08438].

The decoder consumes these prompt tokens before generation. In the paper’s account, this induces an implicit but structured form of cross-modal alignment. The encoder is trained to produce speech features useful both for multi-label relation prediction and for generative triple decoding, while the prompt tokens derived from $\boldsymbol{H}$ shape the decoder’s output distribution toward relation types supported by the speech signal. The result is described as “relation-aware” alignment, because higher-level structure is injected into the generation process instead of relying only on lower-level feature matching [2509.08438].

This mechanism places RPG-MoGe in a broader family of prompt-conditioned generative relation extraction systems. "Generative Prompt Tuning for Relation Classification" reformulates text relation classification as a text-infilling problem and likewise uses prompt-based generation to encode relation semantics [2210.12435]. In document-level extraction, Graph-DPEP combines type-space decomposition with an ensemble-play stage driven by graph-of-thoughts reasoning [2411.02864]. These systems operate in different settings, but all treat prompts as a mechanism for structuring relation prediction.

## 5. Training objective, tokenization, and implementation

RPG-MoGe uses Whisper as both speech backbone and generative backbone. Two backbone sizes are reported: `whisper-small` with approximately 244M parameters and a 12-layer encoder/decoder configuration, and `whisper-medium` with approximately 769M parameters and a 24-layer encoder/decoder configuration. The encoder is fine-tuned end-to-end together with LRPH and the decoder on the SpeechRE datasets, without requiring an external alignment corpus, in contrast to earlier Wave2vec+BART setups [2509.08438].

The triplet representation relies on three special tokens: `\<h>` for the head entity, `\<r>` for the relation type, and `\<t>` for the tail entity. The multi-view relation tree is organized as a four-layer tree consisting of root, first element of the chosen order, second element, and third element. Linearization under a given view yields sequences such as `\<h> Barack Obama \<r> born_in \<t> Honolulu` or, under another view, `\<r> born_in \<h> Barack Obama \<t> Honolulu`. Multiple triples from a single utterance are concatenated in this structured format [2509.08438].

The total objective is the unweighted sum
\[
\mathcal{L}_{total} = \mathcal{L}_{lrp} + \mathcal{L}_{dec}.
\]
No explicit weighting coefficient is introduced in the reported experiments. The decoder branch is trained by token-level cross-entropy, and the multi-order nature of the objective arises from averaging or summing the six view-specific decoding losses for each sample [2509.08438].

Several training details are explicitly reported. LRPH uses dropout with $p=0.5$. During training, 0–50% of positive relations predicted by LRPH are randomly masked before conversion to prompt tokens; this masking is disabled at inference time. The paper states that this is intended to reduce dependence on perfect relation prompts and to narrow the train–test gap by simulating prediction noise. Optimization uses Adam with learning rate $1\text{e-}5$, batch size 12, and 50, 20, and 10 epochs on CoNLL04-SpeechRE, ReTACRED-SpeechRE, and CommonVoice-SpeechRE, respectively [2509.08438].

The inference procedure is correspondingly simple but compute-aware. There is one forward pass through the encoder and LRPH, followed by six decoder passes, one per order view. The paper notes that these decoder passes can be parallelized if resources permit [2509.08438].

## 6. Empirical performance and interaction with CommonVoice-SpeechRE

RPG-MoGe is introduced together with CommonVoice-SpeechRE, a real-human-speech SpeechRE benchmark based on the Common Voice 17.0 English subset [2509.08438]. The dataset contains approximately 20,000 manually annotated sentences, with train/dev/test splits of 14,557 / 2,495 / 2,494, 45 relation types, nearly 20,000 distinct speakers, and an average audio length of approximately 11.6 seconds. The paper presents this dataset as a response to the scarcity of diverse real-speech resources in prior SpeechRE evaluation [2509.08438].

The reported results focus on micro-F1 for entity, relation, and triplet extraction across CoNLL04-SpeechRE, ReTACRED-SpeechRE, and CommonVoice-SpeechRE. For the Whisper-based end-to-end comparison against MCAM, the triplet-F1 numbers are as follows [2509.08438]:

| Dataset | MCAM(770M)\(_{whi}\) | RPG-MoGe(770M)\(_{whi}\) |
|---|---:|---:|
| CoNLL04-SpeechRE | 22.71 | 24.67 |
| ReTACRED-SpeechRE | 8.21 | 9.18 |
| CommonVoice-SpeechRE | 15.71 | 18.29 |

These results correspond to gains of +1.96 on CoNLL04-SpeechRE, +0.97 on ReTACRED-SpeechRE, and +2.58 on CommonVoice-SpeechRE. The largest improvement among the three listed datasets is therefore on the real-human-speech benchmark. The paper also states that RPG-MoGe with a smaller 250M backbone matches or exceeds MCAM(520M) in many metrics, which it presents as evidence of parameter efficiency [2509.08438].

The narrative interpretation offered by the paper is that the improvements derive from two sources: better exploitation of data diversity through the multi-order generative ensemble and better relation-aware alignment through LRPH and relation prompts. The paper does not provide a full ablation table, but it reports that replacing Wave2vec+BART with Whisper already improves LNA-ED and MCAM, and that RPG-MoGe with Whisper improves further. This suggests that both the backbone choice and the framework-specific additions contribute to the observed gains [2509.08438].

## 7. Related frameworks, limitations, and prospective extensions

RPG-MoGe belongs to a wider class of generative relation extraction methods, but its formulation is specific to direct speech-to-triplet extraction. In text relation classification, GenPT shows that prompt-based generative modeling can exploit rich label semantics by treating relation prediction as text infilling and by using entity-guided decoding and discriminative relation scoring [2210.12435]. In few-shot document relation extraction, Graph-DPEP uses graph-style triplets, type-space decomposition, a verifier, and an ensemble-play stage that repairs missing predictions through graph-of-thoughts reasoning [2411.02864]. These related systems share prompt-guided generation as a design pattern, but RPG-MoGe distinguishes itself by coupling speech encoding with latent relation prompting and by using multi-order triplet generation over all six permutations [2509.08438].

The paper’s explicit limitation discussion is brief, but several constraints are clearly implied by the reported design. Multi-order inference requires decoding under all six order views, so inference time and compute are higher than in a single-order model, even though the encoder and LRPH are executed only once [2509.08438]. The latent relation prediction head depends on a predefined supervised relation inventory $\text{R}$, which implies that adaptation to new domains or relation types requires retraining or at least reconfiguration of both LRPH and decoder prompts [2509.08438]. Errors in LRPH may also misguide generation, although training-time prompt masking is intended to mitigate this failure mode [2509.08438].

The paper also notes that the current dataset and experiments are English-only, despite Whisper’s multilingual capability. This suggests a natural direction toward multilingual SpeechRE, provided that annotated speech and an appropriate relation ontology are available [2509.08438]. Additional extensions explicitly suggested in the paper include transferring the same multi-order generation and latent relation prompting ideas to other audio information extraction tasks, to text-only relation extraction, and to interactive or controllable extraction settings in which desired relations are specified as prompts [2509.08438].

In this sense, RPG-MoGe can be understood as a structured generative architecture that combines three distinct ideas in a single SpeechRE pipeline: prompt-guided decoding, permutation-based output diversification, and relation-aware cross-modal supervision. Its contribution is therefore not only a new benchmark and a new model, but a specific claim about how relation structure should be injected into end-to-end speech extraction systems [2509.08438].

Source: https://www.emergentmind.com/topics/relation-prompt-guided-multi-order-generative-ensemble-rpg-moge