Multi-Sentence Scoring Tasks

• Multi-sentence scoring tasks are computational methods that evaluate, rank, or assign scores to groups of sentences based on relevance, coherence, or contribution.
• They leverage advanced neural architectures—such as transformers, hierarchical pooling, and attention mechanisms—to capture inter-sentence dependencies.
• Applications include question answering, summarization, dialogue response selection, fact verification, and fine-grained reward estimation for language models.

Multi-sentence scoring tasks refer to the suite of computational methodologies and models aimed at evaluating, ranking, or assigning scalar or structured scores to groups of sentences—either in relation to a specific query, hypothesis, or task objective. Unlike sentence pair scoring (where two sentences are directly compared), multi-sentence scoring involves scenarios where either a sentence must be evaluated against a pool of candidates, several sentences must be compared or aggregated as a unit, or the importance, coherence, or contribution of each sentence in a textual span must be inferred from aggregate feedback. This class of problems is fundamental to question answering, summarization, scoring long-form responses, dialogue response selection, fact verification, and fine-grained reward estimation for LLM alignment.

1. Foundational Methodologies for Multi-Sentence Scoring

At the core of multi-sentence scoring are neural frameworks originally developed for sentence pair scoring, generalized to handle collections of sentences or more complex text structures (Baudiš et al., 2016). The principal architectural paradigm proceeds in two stages:

A. Sentence (or Segment) Embedding: Each sentence $s_i$ is independently or jointly encoded into a vector (or higher-dimensional tensor) representation $E_i$ using a range of models: average word embeddings, Deep Averaging Networks (DAN), RNNs, CNNs, attention-based transformers, or mixed/hierarchical encoders. Mathematical formulations include

$E_i = \text{Model}(\{e_t^{s_i}\})$

where Model can reflect the particular neural architecture (e.g., BiLSTM, transformer).

B. Scoring and Aggregation: The sentence (or candidate) representations are compared via explicit scoring functions, e.g. dot-products, an MLP over $[E_0 \odot E_1; E_0 + E_1]$ (with $\odot$ denoting elementwise multiplication), or task-specific heads.

For evaluating multiple candidates, the system may compute $f_2(s_0, s_i)$ for each $i$ and aggregate or rank the resulting scores. Hierarchical or pooled representations (e.g., attention-weighted sums, $E_\text{context} = \sum_i \alpha_i E_i$, with $\alpha_i = \text{softmax}(w^T E_i)$) enable the model to capture salient inter-sentence dependencies.

Extensible variants include hierarchical pooling using a second-level model, joint attention mechanisms over entire blocks of sentences, or specialized multi-stream encoders for heterogeneous or auxiliary information (Liu et al., 2023).

2. Supervision From Aggregate to Fine-Grained Signal

Traditional reward or scoring models annotate responses at the aggregate (response- or paragraph-) level. Methods such as FRACTAL (Makhija et al., 7 Apr 2024) disaggregate these coarse labels into fine-grained, sentence-level (or instance-level) scores by operating within multiple instance learning (MIL) or learning from label proportions (LLP) frameworks.

Let $B$ be a bag (text response, e.g., a multi-sentence answer), with label $y_B$ assumed to be the result of an aggregation over unknown instance labels (sentence scores)—using functions such as min, max, or average. Under MIL, the bag label is modeled as

$$\tilde{y}_B = \operatorname{probAGG}(M(x) \text{ for all } x \in B)$$

where $M$ is a sentence-level scorer. This enables sentence-level training signals from only bag-level data by minimizing the loss

$$L_\text{totbag}(B, M) = \frac{1}{|B|} \sum_{B \in \mathcal{B}} L_\text{bag}(y_B, \tilde{y}_B)$$

with differentiable surrogates for non-smooth aggregation.

Priors based on domain knowledge (e.g., cosine similarity to query/context or inter-sentence correlation) are injected via extra terms in the loss, e.g.,

$$L_\text{totprior1}(X, M) = \frac{1}{|X|} \sum_{x \in X} L_\text{prior}(p_x, M(x))$$

Pseudo-labeling schemes iteratively bootstrap better instance-level supervision.

3. Hierarchical, Pairwise, and Joint Encoding Strategies

Multi-sentence scoring frameworks can operate in various modes:

• Pairwise Scoring and Aggregation: For a single query versus a sentence pool, the model independently scores each pair, e.g., $f_2(q, s_i)$, and aggregates/ranks the candidates, with metrics such as MAP or MRR (Baudiš et al., 2016).
• Hierarchical Encoding/Pooling: To capture inter-sentence relationships, hierarchical models perform two-stage encoding: first producing $E_i$ (sentence encodings), then pooling these via a secondary model (e.g., RNN, attention) into a context vector $E_\text{context}$. This enables direct modeling of dependencies between sentences, supporting scenarios such as multi-sentence entailment or evidence aggregation (Hanselowski et al., 2018, Liello et al., 2022).
• Unified Scoring for Singleton and Pair Instances: For summarization, both singleton sentences and pairs are jointly scored within a unified vector space, supporting selection strategies that enable both compression (single sentence) and fusion (pairs) (Lebanoff et al., 2019).
• Attention-based Aggregation: Aggregation over multiple sentence-level scores frequently employs (soft-)attention to focus on more relevant units, as in multi-stream persona-guided scoring (Liu et al., 2023) or weighted sentence reward aggregation (Qiu et al., 1 Mar 2025).

4. Learning Objectives and Evaluation Schemes

Multi-sentence scoring tasks deploy objectives that reflect their ranking or generation target:

• Ranking Objectives: For answer selection, next utterance ranking, and retrieval, the system is trained to assign higher scores to correct candidates via pointwise or pairwise rank losses (e.g., bipartite RankNet, hinge loss: $L = \sum \max(0, 1 + s_n - s_p)$) (Baudiš et al., 2016, Hanselowski et al., 2018).
• Regression/Objectives and Correlation Metrics: Semantic similarity and STS tasks often employ regression objectives optimized to maximize correlation coefficients (e.g., Pearson's $r$).
• Contrastive and Joint Losses: In pairwise setups, batch-softmax contrastive loss directly maximizes the contrast between true pairs and in-batch negatives, which is beneficial for tasks requiring tight control over similarity semantics (Chernyavskiy et al., 2021).
• Multi-task Losses: Tasks with multiple sources of supervision (e.g., automated assessment, combining grammatical error detection and essay scoring (Cummins et al., 2018)) build joint objectives

$$E = (1 - \gamma_\text{aes}) (E_\text{ged} + \gamma_\text{lm} E_\text{lm}) + \gamma_\text{aes} E_\text{aes}$$

with hyperparameters to control task contribution.

• Evaluation Metrics: Depending on the task, evaluation may report MAP, MRR, F1 (for classification), Spearman or Pearson’s $r$ (for regression), accuracy, BLEU (for generated sentences), Cohen’s Kappa (for agreement with human labels), and metrics specific to structure (such as dSet for argumentative structure (Putra et al., 2021)).

5. Recent Technical Innovations and Architectures

Recent research has yielded advanced architectures and strategies adapted to the challenges of multi-sentence scoring:

• Poly-Encoders and Multi-Stream Models: Poly-encoders introduce a mediator between bi- and cross-encoders by extracting global context codes from the input and applying candidate-aware attention during scoring, leading to enhanced accuracy–efficiency trade-offs for candidate ranking (Humeau et al., 2019). Persona-Coded Poly-Encoders extend this with dedicated streams for auxiliary (persona) information and sophisticated post-fusion layers (Liu et al., 2023).
• Sliding LLMing (SLM): Transcormer introduces SLM with a triple-stream self-attention mechanism that recovers bidirectional context in a single inference pass—achieving efficiency gains over masked LLMing and performance improvements for reranking and linguistic evaluation (Song et al., 2022).
• Fine-Grained Reward and Feedback Models: FRACTAL applies MIL/LLP to derive sentence-level supervision from bag-level reward, combining differentiable approximations of aggregation functions and domain priors. Further, attention-based aggregation of sentence-level reward signals has demonstrated clear performance gains in RLHF scenarios for LLM alignment (Makhija et al., 7 Apr 2024, Qiu et al., 1 Mar 2025).
• Reference Diversification and Multi-Tasking: Sentence fusion and generation tasks benefit from reference augmentation via curated equivalence classes (allowing for multiple valid outputs under different connectives) and auxiliary discourse tasks, which together improve both learning and evaluation (Ben-David et al., 2020).
• Data Augmentation and Sampling: Effective bi-encoder training utilizes silver-labeled data (produced by cross-encoders) and informed sampling strategies (e.g., BM25, KDE) to optimize support for similarity distributions in the training set and improve both in-domain and domain-adaptation performance (Thakur et al., 2020).

6. Applications and Research Implications

Multi-sentence scoring forms the backbone for many complex NLP systems:

• Answer Sentence Selection & Fact Verification: Systems identify the most relevant supporting (or contradicting) sentences among large pools, as in the FEVER claim verification task with entity-linking based retrieval, ESIM-based scoring, and attention-based multi-sentence entailment aggregation (Hanselowski et al., 2018).
• Summarization and Sentence Fusion: Joint selection and scoring of singleton and paired sentences enables extractive, compressive, and abstractive summarization; technical advances allow ranking by informativeness and compatibility, and support end-to-end summarizer pipelines (Lebanoff et al., 2019, Ben-David et al., 2020).
• Educational Assessment: Multi-sentence scoring underpins advanced essay and free-response grading, leveraging multi-task and fine-tuned LLM approaches for detailed feedback, holistic scoring, and rubric-aligned assessment—from grammatical error annotation to domain-specific science response classification (Cummins et al., 2018, Latif et al., 2023, Wu et al., 2023).
• Dialogue and Conversational AI: Multi-stream architectures enhance candidate retrieval and response ranking, leveraging auxiliary persona or behavioral information, with empirical improvements in BLEU and HR@1 (Liu et al., 2023).
• Reinforcement Learning from Human Feedback (RLHF) in LLMs: Fine-grained, sentence-level reward models close the gap between sparse response-level signals and noisy token-level attributions, enabling finer alignment and improved human preference generalization (Makhija et al., 7 Apr 2024, Qiu et al., 1 Mar 2025).
• Interpretability and Targeted Optimization: Fine-grained scoring models support actionable interpretability by surfacing which sentence(s) are responsible for a positive or negative global assessment, facilitating targeted content optimization or correction.

7. Challenges, Limitations, and Future Directions

Despite significant progress, several challenges remain:

• Supervision Granularity and Cost: Fine-grained human annotation (sentence-level labels) is expensive; current research focuses on disaggregating aggregate labels using MIL, LLP, and prior-informed objectives to mitigate annotation bottlenecks (Makhija et al., 7 Apr 2024).
• Architectural Complexity vs. Efficiency: Many state-of-the-art models balance the trade-off between scoring accuracy (cross-encoders, hierarchical attention) and computational efficiency (bi-encoders, poly-encoders, SLM), with ongoing research into scalable aggregation, candidate caching, and stream fusion (Humeau et al., 2019, Song et al., 2022, Liu et al., 2023).
• Robust Evaluation and Domain Adaptation: Variance in candidate pool size and composition, shifting domain distributions, and multi-modal or behaviorally rich inputs all challenge robust scoring. Empirical findings advocate for evaluation with statistically sound confidence intervals, careful sampling, and robust transfer approaches (Baudiš et al., 2016, Thakur et al., 2020, Putra et al., 2021).
• Discourse and Structure Modeling: Effective scoring in argumentation and complex discourse requires structural understanding (e.g., tree-based linking, node depth prediction), incorporation of auxiliary signals, and careful domain adaptation (Putra et al., 2021).
• Personalization and Multi-Modal Integration: Next-generation systems aim to leverage multi-modal signals (demographics, interaction patterns, behavioral/medical data) for personalized scoring and adaptive response selection, requiring the design of flexible and privacy-safe encoding and fusion architectures (Liu et al., 2023).

Future research is expected to focus on unified frameworks that generalize scoring across tasks and modalities, efficient learning from weak or aggregate supervision, adaptive architectures for large-scale real-time retrieval, and the development of interpretable metrics and tools for analyzing and auditing sentence-level rewards and feedback in human-aligned language systems.