Learning Algebraic Recombination for Compositional Generalization (2107.06516v1)

Abstract: Neural sequence models exhibit limited compositional generalization ability in semantic parsing tasks. Compositional generalization requires algebraic recombination, i.e., dynamically recombining structured expressions in a recursive manner. However, most previous studies mainly concentrate on recombining lexical units, which is an important but not sufficient part of algebraic recombination. In this paper, we propose LeAR, an end-to-end neural model to learn algebraic recombination for compositional generalization. The key insight is to model the semantic parsing task as a homomorphism between a latent syntactic algebra and a semantic algebra, thus encouraging algebraic recombination. Specifically, we learn two modules jointly: a Composer for producing latent syntax, and an Interpreter for assigning semantic operations. Experiments on two realistic and comprehensive compositional generalization benchmarks demonstrate the effectiveness of our model. The source code is publicly available at https://github.com/microsoft/ContextualSP.

Learning Algebraic Recombination for Compositional Generalization

The paper presents a novel approach titled LeAR (Learning Algebraic Recombination) aimed at enhancing compositional generalization in neural semantic parsing. Specifically, it addresses limitations in current sequence models that struggle with the algebraic recombination needed to dynamically process structured expressions in recursive manners.

Approach

LeAR frames semantic parsing as a homomorphism between a latent syntactic algebra and a semantic algebra. This framing departs from previous studies that focus primarily on recombining lexical units, recognizing it as insufficient for full compositional generalization. The underlying goal is to learn high-level mappings between latent syntactic operations and semantic operations, rather than directly mapping expressions to meanings.

Model Architecture

The model architecture comprises two key components:

1. Composer: This component is responsible for discovering latent syntactic trees of input expressions. Utilizing a Tree-LSTM structure, it builds syntax trees in a bottom-up manner and assigns nonterminal symbols to nodes for abstraction.
2. Interpreter: This assigns semantic operations to nodes within syntactic trees. It differentiates between lexical nodes, which are assigned semantic primitives, and algebraic nodes, which are assigned semantic operations essential for algebraic recombination.

The paper deploys an end-to-end training mechanism using reinforcement learning through policy gradients, ensuring that the gradual progression of learning weights aligns with the complexity of tasks, a technique akin to curriculum learning.

Experimental Results

LeAR is evaluated on two benchmarks, CFQ and COGS, along with a more traditional dataset, GEO. The results show a marked improvement in accurately parsing complex expressions which involve deep compositional structures, achieving accuracy gains such as CFQ's jump from 67.3% to 90.9% and COGS's advance from 35.0% to 97.7%. These results confirm the model's robustness across varied semantic parsing tasks and compositional challenges.

Key Findings

1. Algebraic Recombination: Focusing on algebraic over lexical recombination results in significantly stronger compositional generalization, supported by the experiment which shows large gaps in performance in previous models tailored to lexical tasks.
2. The Efficacy of Synthesized Semantics: The introduction of a latent syntactic structure, learned via Tree-LSTMs, provides crucial architectural support. This coupled with the explicit semantic operation assignment contributes to the substantial improvement in accuracy.
3. Pragmatic Reward Structures: The effectiveness of a two-tier reward scheme in the reinforcement learning setup, which focuses separately on broader logical structure and smaller primitive alignments, underscores the nuances in aligning human and machine interpretation.

Implications

The implications of this work are vast for AI models dealing with language. Understanding compositional semantics allows for enhanced performance not just in parsing but across language tasks that require understanding or generating new, unseen expressions. Inherent model improvements may lead to advancements in dialogue systems, translation, and other natural language processing areas requiring nuanced comprehension of language structure and meaning.

Future Directions

Potential future directions include extending the model's capabilities to handle broader domains or more diverse semantic formalisms and exploring the minimal requisite syntactic structures needed for varied linguistic architectures. Additionally, refining the abstraction and operation assignment processes may provide insights into further minimizing discrepancies between machine-parsed and human-intended meanings. Continued research is needed to explore structurally varied and syntactically complex domains, providing avenues for even deeper model refinements.

This paper positions itself as a critical pivot towards integrating classical linguistic theory with modern computational models, advocating for a symbiotic relationship between structured syntactic exploration and neural computation.