Extensible Semantic Parsing Modules

Updated 16 November 2025

Extensible semantic parsing modules are frameworks that transform natural language utterances into logical forms using modular grammars, rule tables, and neural decoders.
They integrate rule-based, neural, and probabilistic methods to achieve robust cross-domain adaptation and high accuracy in tasks such as KBQA and multi-domain parsing.
Practical extension involves augmenting grammars, updating lexicons, and fine-tuning model components without altering the core inference and learning architecture.

An extensible semantic parsing module is an architectural and algorithmic framework for converting natural language utterances into formal meaning representations (logical forms, code, queries) with design elements that enable adaptation to new domains, meaning representation languages, or linguistic phenomena. Extensibility is achieved by modularizing grammar specification, mapping logic, probabilistic inference, and learning so that new domains, languages, or formalisms require only the addition or modification of a small set of interpretable components—such as production rules, rules tables, or grammar fragments—often without changes to the core learning and inference machinery. Several influential lines of work exemplify extensible modules for semantic parsing, including combinatory lambda-calculus templates over dependency trees, policy-distilled multi-domain sequence models, abstract-syntax transition systems, probabilistic generative grammars with hierarchical Bayesian structure, grammar-constrained neural decoders, and compositional latent tree models.

1. Grammar- and Rule-Based Extensibility

Extensible semantic parsing modules frequently anchor parsing in a domain-general grammar or set of mapping rules that encode the syntax–semantics interface. These grammars may appear as logic templates attached to syntactic structures (e.g., dependency parses) or as abstract syntax description languages (ASDL).

For instance, UDepLambda uses a table $R$ mapping Universal Dependencies (UD) labels to lambda-calculus templates. Each edge label $\ell$ (e.g., nsubj, obj) is associated with two combinators: Head $_\ell$ and Dep $_\ell$ . The semantic construction proceeds with bottom-up composition, attaching each dependent’s lambda-term into its head’s lambda-term via function application or $\beta$ -reduction, yielding a fully compositional logical form. To extend to a new language or linguistic phenomenon, one simply adds or overrides entries in $R$ or introduces new lexical templates $L_v$ for unknown word types or multiword expressions. The rule table is stored in plain-text formats (JSON, Prolog), and overriding entries is supported at load time so that language- or domain-specific logic is injected without core code modification (Reddy et al., 2017).

Transition-based neural modules such as TRANX adopt an abstract syntax description language (ASDL) to specify allowable structures of meaning representations. The ASDL grammar, together with the logic-to-target conversion utility, fully governs the set of legal parser actions. Adding a new meaning representation language or logical operation is accomplished by extending the ASDL grammar with additional constructors and field types. The learning and decoding code remain fixed, and the parser restricts action sequences to those that build valid, well-formed trees according to the new grammar (Yin et al., 2018).

2. Neural and Probabilistic Architectures with Domain Adaptation

Modern extensible modules leverage neural architectures to encode input and build structured outputs, but retain the ability to generalize across domains by design.

In multi-domain settings, unified semantic parsing models use teacher–student policy distillation under weak supervision. Individual domain-specific parsers ("teachers") are first trained on (utterance, denotation) pairs. Their decoding distributions over programs $\pi_{\theta^k}(z|x)$ are then distilled into a single student parser through KL divergence minimization. This decouples domain adaptation from parameter sharing: new domains are handled by training a new teacher and including it in the multi-policy distillation loss. At inference, the student model performs domain-agnostic parsing, determining the correct schema by inspecting output programs without user-provided domain labels (Agrawal et al., 2019).

Probabilistic generative grammar frameworks formalize extensibility at the Bayesian level: the set of productions for nonterminals is modeled as a hierarchical Dirichlet process (HDP) indexed by semantic features of their logical-form arguments. Adding a new domain amounts to integrating new semantic-transformation functions and augmenting the HDP base distributions, while the grammar skeleton and learning algorithm (blocked Gibbs/Metropolis–Hastings) are unchanged. The parser immediately generalizes to new predicates and structures, with fast mixing and robust transfer (Saparov, 2016).

3. Grammar-Constrained Neural Decoding for Knowledge Base QA

Type- and candidate-driven grammar constraints offer a declarative interface for fast extension in large ontology scenarios. In knowledge-base question answering (KBQA), a semantic parsing module may employ a typed, context-free-style grammar $G = (N, \Sigma, R, S)$ where nonterminals are placeholders with explicit types and terminals encode function names, operators, or KB-specific entities. Productions decompose into type-driven and candidate-driven actions, the latter instantiated by extracting entity/relation sets $E(c; k)$ from the target KB and building trie structures for efficient prefix-constrained decoding.

A key innovation for extensibility is the mask-caching algorithm: at each decoding step, action validity is determined by type constraints, sub-type inference (including union types), and candidate expression tries. These mask vectors are cached per dynamic type context for constant-time GPU retrieval, even as the set of candidate actions changes with new domain schemas. Extending the module to a new KB or domain only requires updating candidate sets and reconstruction of the trie data structures; the type system and decoder remain static (Nam et al., 1 Oct 2024).

4. Compositional and Span-Based Parsing Schemes

Compositional generalization is a central extensibility criterion—generalizing to new logical structures built from observed components.

Span-based semantic parsers (e.g., SpanBasedSP) induce latent compositional trees where each internal node corresponds to a contiguous span of the input, anchored to a program fragment. Leaves carry predicate labels, while internal nodes represent functional composition through composer rules. The architecture supports plug-and-play extension: new predicate or composition rules are integrated by appending to the predictor grammars and initializing embeddings, with no requirement for gold trees or parse alignments. Training requires only utterance–logical form pairs; the model marginalizes over all latent trees compatible with observed outputs via inside-outside dynamic programming (Herzig et al., 2020).

5. Cross-Formalism and Multi-Task Integration

Several extensible modules support integration across semantic and syntactic formalisms or operate as multi-task systems.

Joint models for syntax and semantics, such as those using shared encoders for span/dependency representations, parameterize both syntactic and predicate–argument structures. Scoring heads for new semantic formalisms (e.g., AMR, FrameNet) can be added with minimal architectural change, leveraging the core shared representation machinery. New languages are supported by replacing embeddings and supplying minimal annotation (Zhou et al., 2019).

Hybrid neural–symbolic modules may combine deep joint representation models (such as compositional embeddings for Q–R pairs) with conditional LLMs for query generation. By decoupling encoder and decoder training, such architectures facilitate rapid transfer to new ontologies or languages given small in-domain parallel corpora (Grefenstette et al., 2014).

6. Evaluation Protocols and Empirical Results

Extensible modules are commonly evaluated on multilingual or multi-domain benchmarks and via measures such as denotation accuracy, F1, and logical form accuracy. For example, UDepLambda yields $F_1$ of 63.5/58.7/60.1 on English/German/Spanish QA datasets with minimal language-specific adaptation (Reddy et al., 2017). Unified policy-distilled parsers obtain a 20-point denotation accuracy improvement on the Overnight data compared to domain-independent baselines (Agrawal et al., 2019). Grammar-constrained decoders with candidate expressions achieve up to 92.8% test accuracy (beam=4) on KQA PRO (Nam et al., 1 Oct 2024). Span-based architectures offer dramatic gains in compositional generalization, e.g., up to 88.9% accuracy on compositional splits (Herzig et al., 2020).

Across architectures, extensibility is achieved by modular rule, grammar, or component specification, efficient adaptation mechanisms, and architecture-level constraints that guarantee grammaticality and semantic validity regardless of the target domain or KB.

7. Practical Steps for Extending Semantic Parsing Modules

The general procedure for extending a semantic parsing module is:

Augment Grammar or Rule Table: Add or override production rules, mapping templates, or constructor definitions, following the extensible grammar formalism (e.g., in JSON, ASDL, or logic templates).
Update Lexicons and Candidate Sets: For KB QA or entity-centric tasks, augment entity/relation lists, retrain or update trie data structures.
Initialize or Fine-tune Model Components: For new types, predicates, composer rules, or word forms, initialize new embeddings or modules and fine-tune end-to-end on domain- or language-specific data.
Retrain or Transfer: Employ policy distillation, transfer learning, or joint inference as suitable for the architecture.
Maintain Core Infrastructure: The core parsing, decoding, and inference algorithms are typically unchanged and operate over the extended grammar/rule inventory.
Empirical Validation: Run standard QA, logical form, or denotation experiments on in-domain and cross-domain/dev/test splits to confirm coverage and generalization.

In summary, extensible semantic parsing modules achieve robustness and cross-domain transfer by formalizing the syntax–semantics interface as a modular, editable grammar or rule database, supported by deterministic or learned mapping operators and decoupled from core learning/inference machinery, thus enabling scalable adaptation to new tasks, languages, or knowledge representations without architectural re-engineering.