Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version) (1612.01197v1)

Abstract: Extending the success of deep neural networks to natural language understanding and symbolic reasoning requires complex operations and external memory. Recent neural program induction approaches have attempted to address this problem, but are typically limited to differentiable memory, and consequently cannot scale beyond small synthetic tasks. In this work, we propose the Manager-Programmer-Computer framework, which integrates neural networks with non-differentiable memory to support abstract, scalable and precise operations through a friendly neural computer interface. Specifically, we introduce a Neural Symbolic Machine, which contains a sequence-to-sequence neural "programmer", and a non-differentiable "computer" that is a Lisp interpreter with code assist. To successfully apply REINFORCE for training, we augment it with approximate gold programs found by an iterative maximum likelihood training process. NSM is able to learn a semantic parser from weak supervision over a large knowledge base. It achieves new state-of-the-art performance on WebQuestionsSP, a challenging semantic parsing dataset, with weak supervision. Compared to previous approaches, NSM is end-to-end, therefore does not rely on feature engineering or domain specific knowledge.

An Overview of "Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision"

The paper "Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision" introduces a novel architecture for semantic parsing that integrates neural networks with non-differentiable memory. This work addresses the challenge of effectively leveraging symbolic reasoning and natural language understanding in a scalable manner. The proposed approach, termed the Manager-Programmer-Computer (MPC) framework, enables efficient program induction by combining neural networks with a traditional programming language, Lisp, to perform precise and scalable operations.

Key Contributions

The cornerstone of this research is the introduction of the Neural Symbolic Machine (NSM), which is framed within the MPC architecture. The NSM consists of a sequence-to-sequence ("seq2seq") neural network model acting as a "programmer" and a Lisp interpreter as a "computer." This setup facilitates abstract and flexible programming operations while maintaining the benefits of efficient memory usage. The model's capability to learn from weak supervision without dependence on extensive feature engineering is a significant step forward in the domain.

1. Manager-Programmer-Computer Framework: The MPC framework consists of three distinct components: a "manager" that provides weak supervision through rewards, a "programmer" that generates programs, and a "computer" which executes these programs using a high-level programming language. This configuration allows for abstract, scalable operations that are not possible with purely differentiable memory.
2. Integration with Lisp: The adoption of a Lisp interpreter as the "computer" extends the functionality of NSM by incorporating operations equivalent to a subset of λ-calculus. This integration allows for the robust execution of complex and abstract symbolic operations on a large knowledge base.
3. Weak Supervision Training: The paper proposes a training regimen using REINFORCE algorithm, augmented with an iterative maximum likelihood process to facilitate learning from weak supervision effectively. This method anchors the learning process with high-reward programs, accelerating training efficiency.
4. Empirical Results: NSM has been applied to the WebQuestionsSP dataset, achieving state-of-the-art results. This demonstrates the capability of the framework to perform competitively on semantic parsing tasks, significantly outperforming previous models, such as STAGG, in terms of precision, recall, and F1-score without relying on hand-crafted features.

Implications and Future Directions

The results presented in the paper indicate the potential of integrating non-differentiable memory with neural network models to advance semantic parsing. By bridging traditional symbolic reasoning with modern neural approaches, NSM represents an important development in the field of knowledge base interaction and natural language processing.

The practical implications of this research extend to any domain where complex query generation and interpretation over large-scale knowledge bases are necessary. Additionally, the capacity to leverage weak supervision efficiently opens up pathways for expansion into other areas, such as automated programming and generalized problem-solving tasks in artificial intelligence.

Further research may explore extending the MPC framework to other domains beyond semantic parsing, improving the reinforcement learning process, and expanding the predefined function set within the Lisp interpreter for broader applicability. There is also an opportunity to refine the memory interface and enhance the ability of the seq2seq model to generalize to unseen operations effectively.

By advancing the integration of symbolic computation within neural networks, this paper contributes to the broader aim of creating intelligent systems capable of sophisticated reasoning and understanding.