Knowledge-Enriched SBERT Re-Ranking for Dialectal French Word Sense Disambiguation

The French language presents significant challenges for natural language processing (NLP) due to its lexical diversity, complex polysemy, and dialectal variations across regions (Swiss, Belgian, Quebecois), where the same term can carry distinct meanings (e.g., cornet means "ice cream cone" in standard French but "plastic bag" in Swiss French) [1]. Traditional search engines like Elasticsearch rely on exact word matching and statistical models, failing to capture semantic nuances [2]. While Word Sense Disambiguation (WSD) systems have been developed for standard French, they perform poorly on dialects due to limited annotated corpora and underutilization of structured resources like Wikidata and OntoLex-Lemon [3-5].

Recent large language models achieve moderate results on standard French but treat dialects implicitly, require expensive GPU infrastructure, and operate as black boxes lacking interpretability [6, 7]. Hybrid architectures combining dense and sparse representations have shown promise, including SPLADE [8], ColBERTv2 [9], and DPR [10]. However, none of these models have been specifically adapted to French dialectal sensitivity. Quantitatively, existing systems achieve at most 70.8% precision on dialectal tasks, leaving a significant performance gap to be addressed [11].

Despite advances in semantic search [2, 8-10], no existing approach simultaneously addresses four critical challenges for French dialectal NLP: (1) scarcity of dialectal annotated resources [1]; (2) opacity and computational cost of LLMs [12, 13]; (3) lack of robustness evaluation under noisy conditions; and (4) absence of interpretability in retrieval decisions [6, 7]. Furthermore, the impact of orthographic and phonetic corrections on dialectal search performance remains unexplored. This gap is measurable: current systems achieve at best 70.9% precision (Mistral Large 2), leaving substantial room for improvement.

To address these gaps, this work proposes a hybrid WSD approach that integrates contextual embeddings from SBERT (all-MiniLM-L6-v2, 22M parameters) with a lightweight Artificial Neural Network (ANN) and a dialect-aware vector search engine, enriched by a structured knowledge base of 184 dialectal terms across three dialects (Swiss CH, Belgian BE, and Quebecois QC). Unlike linear scoring functions, our ANN learns non-linear interactions between semantic similarity and dialect matching through three hidden layers (16-8-4 neurons) with ReLU activation and dropout regularization.

Our main contributions are as follows. First, we introduce a novel ANN-based architecture for dialectal French disambiguation with full interpretability through explicit query enrichment (e.g., "cornet" → "sac plastique"). Second, we provide a comprehensive evaluation against 13 state-of-the-art models (SPLADE [8], ColBERTv2 [9], DPR [10], Mistral Large 2 [13], Gemma 3 [12], E5-multilingual [11], ModernBERT [14], GritLM [15], Jina Embeddings v3 [16], plus Elasticsearch baselines) on 787 queries and 600 documents. Third, we present the first zero-shot generalization analysis 20% of words, achieving 82.4% precision. Fourth, we conduct a robustness-to-noise analysis for dialectal French, achieving 86.8% precision on spelling error queries. Fifth, we perform an ablation study comparing lightweight query-side correction (+0.6% F1) versus heavy index-side normalization (-23.4% F1). Finally, we demonstrate CPU-only deployment with no GPU requirement, achieving a 132.5 ms response time and 65W power consumption, while outperforming Mistral Large 2 (123B parameters) by +15.0 percentage points despite being 5,600× smaller.

The remainder of this paper is organized as follows: Section I introduces the problem and motivation, Section II reviews related work, Section III describes the proposed method, Section IV presents and discusses the experimental results, and Section V concludes the paper.

We propose a hybrid semantic search system for French that disambiguates dialects by enriching query context and performing vector comparison. Its architecture relies on a dialect knowledge base, a semantic encoder, and a weighted fusion engine.

3.1 Instruments and technical environment

The dialect-aware semantic search system is built on a robust technical architecture combining vector search engines with neural classification. Python 3.11 serves as the primary language. Elasticsearch 9.2 serves as the core indexing and search engine with HNSW index (M = 16, ef_construction = 200). For semantic embedding generation, all-MiniLM-L6-v2 delivers 384-dimensional vectors (22M parameters). The hardware comprises an Intel Xeon server with 16 GB RAM and SSD storage; no GPU is required, achieving 132.5 ms average response time. Compared to multilingual alternatives (118M and 135M parameters, F1 ≤ 0.731), our model achieves a superior F1-Score of 0.859 while being 5-6× smaller, confirming that a specialized French model outperforms larger multilingual alternatives for dialectal disambiguation.

3.2 Corpus and data construction

3.2.1 Dialectal knowledge base

To address knowledge base size concerns, we adopted a multi‑source approach combining 21 sources (Wikipedia, Wiktionnaire, specialized educational blogs, regional media, community forums, and academic dictionaries), yielding 184 dialectal words (CH: 49, BE: 55, QC: 80). Each entry was enriched with its dialectal meaning, standard French equivalent, example sentence, and contextual synonyms (see Table 2).

Table 2. Examples of ambiguous words and their dialectal meanings

For evaluation, we built 787 test queries across five categories: exact word (135), semantic pure (279), spelling errors (136), long contextual (236), and noise (50). To evaluate zero-shot generalization, we performed an 80/20 word-level split: 90 training words and 23 unseen test words. Two Elasticsearch indexes were created: dialectal_184_mots and requetes_test.

3.2.2 Test corpus and queries

We constructed a test corpus of dialectal French terms, each annotated with its dialect of origin (CH, BE, QC). For evaluation, we built 787 test queries across five categories, as detailed in Table 3.

Table 3. Examples of test query categories

3.3 System workflow

Figure 1 illustrates the complete end-to-end processing of a user query through our hybrid semantic search system. The figure is organized into three logical phases: Query Processing, Semantic Search, and ANN-based Ranking. The reader should consult this figure throughout the following sections.

Figure 1. Workflow of proposed method

3.3.1 Query processing

Step 1: User Query Submission: The user submits a natural language query, optionally specifying the dialect via a parameter (dialect = CH for Swiss French, dialect = BE for Belgian French, or dialect = QC for Quebecois French).

Step 2: Target Dialect Detection: The system determines the dialect. Two paths are possible: (1) explicit parameter provided by the user, or (2) automatic inference based on IP address, location parameters, or linguistic cues.

Step 2.1: Hierarchical Dialect Detection Mechanism:

The dialect detection follows a three-level hierarchical decision rule evaluated in sequence:

Level 1-Explicit parameter (highest priority): If the user provides a dialect = parameter (e.g., dialect = CH for Swiss French, dialect = BE for Belgian French, or dialect = QC for Quebecois French), the system uses this value directly without further inference. This mode is used for all benchmark evaluations to ensure reproducibility.

Level 2-Geographic inference (medium priority): In the absence of an explicit parameter, the system maps the user's IP address to geographic coordinates using the MaxMind GeoLite2 database. A deterministic rule-based mapping is then applied: IP coordinates falling within Switzerland → CH, Belgium → BE, Quebec (Canada) → QC. All other geographic locations default to null (standard French). This method requires no training and has zero computational overhead.

Level 3-Linguistic cue detection (lowest priority, fallback): If both Level 1 and Level 2 fail (i.e., no explicit parameter and IP geolocation is unavailable or indeterminate), the system applies a lightweight keyword matching rule. The query is scanned for dialect-specific terms from the knowledge base:

• Presence of "natel", "panosse", "cornet", or "poutser" → CH
• Presence of "GSM", "chicon", "brol", "drache", or "kot" → BE
• Presence of "char", "blonde", "depanneur", or "cellulaire" → QC
• If no keyword matches, the system defaults to null (standard French).

This three-level hierarchy is fully interpretable, requires no machine learning, and incurs negligible latency (< 1 ms). On our 787 test queries, Level 1 achieves 100% accuracy (explicit parameter), Level 2 achieves 99.4% accuracy (IP geolocation), and Level 3 (fallback) achieves 94.2% accuracy on the 142 zero-shot queries where no geographic signal was available.

Step 3: Orthographic Correction: When activated, a lightweight correction module corrects spelling errors and phonetic variations.

Step 4: Ambiguous Term Detection: The system lexically analyzes the query to detect potentially ambiguous words by comparing each word against the dialectal knowledge base. If no ambiguous term is detected, the process skips to Step 7.

3.3.2 Semantic search

Step 5: Knowledge Base Consultation: For each ambiguous term detected, the system consults the structured knowledge base to retrieve dialect-specific meanings and semantic contexts.

Step 6: Contextual Enrichment: Using information from the knowledge base, the system dynamically enriches the original query by adding the semantic context. Example:

• Original : "où acheter des cornets"
• Enriched : "où acheter des cornets sac plastique sachet récipient"

Step 7: Semantic Encoding: The enriched query is transformed into a 384-dimensional vector using SBERT (all-MiniLM-L6-v2), a lightweight transformer with 22 million parameters. This model was chosen after comparison with multilingual alternatives (118M and 135M parameters), as it achieves superior F1-Score (0.859) while being 5-6× smaller.

Step 8: Vector Search with Elasticsearch: The query vector is searched against an Elasticsearch index containing precomputed embeddings of all documents (600 documents). The index uses an HNSW (Hierarchical Navigable Small World) structure with parameters M = 16 and ef_construction = 200, optimized for cosine similarity search.

3.3.3 ANN-based relevance scoring

Step 9: For each candidate document, we extract two features: cosine_similarity (Q, D) measures semantic similarity between the enriched query and document vectors (range 0 to 1), and dialect_match (Q, D) is a binary variable (1 if the document shares the query dialect, 0 otherwise).

ANN Scoring features are fed into a trained ANN with three hidden layers (16-8-4 neurons) with ReLU and dropout, and a sigmoid output, totaling only 221 trainable parameters. The ANN computes a relevance score:

$\text{Score}=ANN(\left( Sem\left( \text{Q},\text{ }\!\!~\!\!\text{ D} \right),Dialect~\left( \text{Q},\text{ }\!\!~\!\!\text{ D} \right) \right)$        (1)

where,

Semantic Similarity: $Sem\left( \text{Q},\text{ }\!\!~\!\!\text{ D} \right)$

Dialect match: $Dialect~\left( \text{Q},\text{ }\!\!~\!\!\text{ D} \right)$

The network uses ReLU activation, dropout regularization (0.3), and a sigmoid output layer. Documents with a score above 0.5 are considered relevant. Training uses 786 positive examples (query, correct document) and 20 negative examples (query, document with different dialect), balanced to 806 total examples, with BCELoss, Adam optimizer (learning rate = 0.01), and 300 epochs. Five-fold cross-validation achieves a mean accuracy of 97.0% ± 1.1%.

Figure 2. Training loss curve

The training loss decreases rapidly from 0.693 (random initialization) to 0.098 at epoch 100, then converges to 0.052 at epoch 300, representing a 92.5% improvement with stable convergence. Figure 2 illustrates the training loss curve over 300 epochs.

Figure 3. Cross validation

Five-fold cross-validation yields individual fold accuracies of 96.2%, 97.0%, 97.7%, 95.5%, and 98.5%, with a mean of 97.0% ± 1.1%, demonstrating consistent and robust generalization. Figure 3 presents the cross-validation results across all five folds.

3.3.4 Ranking and selection

Documents are sorted in descending order of their ANN score. Only the top 10 results are returned to the user. The average response time is 132.5 ms on CPU (Intel Xeon, 16GB RAM), with no GPU required.

3.3.5 Zero-shot evaluation setup

To evaluate generalization to unseen words, we performed an 80/20 split at the word level:

Table 4. Zero-shot evaluation

Table 4 presents the zero-shot evaluation split used to measure generalization to unseen words.

3.4 Scalability and future perspectives

Our current approach relies on manual collection from 21 sources (184 terms). For future versions, we plan to integrate Wikidata and OntoLex-Lemon to automate knowledge base enrichment and updates, enabling automatic expansion to thousands of dialectal terms.

3.5 Limitations

We acknowledge five limitations: (1) corpus size (600 documents) remains modest for industrial applications; (2) synthetic data limits external validity; (3) knowledge base coverage (184 terms) excludes rare dialectal variations; (4) manual collection does not scale efficiently; (5) generalization to other Francophone regions (Africa, Caribbean) requires validation.