Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (1611.00020v4)

Abstract: Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural "programmer", i.e., a sequence-to-sequence model that maps language utterances to programs and utilizes a key-variable memory to handle compositionality (b) a symbolic "computer", i.e., a Lisp interpreter that performs program execution, and helps find good programs by pruning the search space. We apply REINFORCE to directly optimize the task reward of this structured prediction problem. To train with weak supervision and improve the stability of REINFORCE, we augment it with an iterative maximum-likelihood training process. NSM outperforms the state-of-the-art on the WebQuestionsSP dataset when trained from question-answer pairs only, without requiring any feature engineering or domain-specific knowledge.

Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision

The paper presents a novel approach for integrating neural networks with symbolic reasoning, offering a solution to challenges in semantic parsing using weak supervision. This approach, termed Neural Symbolic Machines (NSM), facilitates robust language understanding and execution of discrete non-differentiable operations essential for querying large-scale knowledge bases (KBs) such as Freebase.

NSM proposes a Manager-Programmer-Computer (MPC) framework that combines three main components: a neural "programmer," a symbolic "computer," and a management unit. The neural programmer utilizes a sequence-to-sequence (seq2seq) model to translate language utterances into programs while leveraging a key-variable memory to manage compositionality. The symbolic computer is a Lisp interpreter, tasked with program execution and equipped with capabilities to prune the potential search space, thereby enhancing the process of finding efficient programs.

Critically, NSM applies the REINFORCE algorithm to optimize the task reward of semantic parsing as a structured prediction task. To tackle the instability issues associated with REINFORCE under weak supervision, the training process is augmented with an iterative maximum likelihood procedure, which aids in enhancing stability and convergence. This dual training strategy is instrumental in overcoming the challenges posed by the non-differentiability of the operations employed, allowing for effective end-to-end training without necessitating feature engineering or domain-specific knowledge.

Key technical contributions of the NSM framework include:

1. Augmenting a standard seq2seq model with key-variable memory in order to support and leverage language compositionality, a method analogous to pointer networks.
2. Employing the Lisp interpreter to execute partial programs, thereby facilitating exploratory actions that prune the vast search space for solutions.
3. A hybrid training framework that combines maximum likelihood and REINFORCE to perform robust learning from weak supervision, representing a significant advancement over traditional training methods reliant solely on fully supervised datasets or manual annotations.

Empirical evaluations demonstrate that NSM surpasses the state-of-the-art performance on the WebQuestionsSP dataset, achieving high accuracy and F1 scores through its sophisticated neural-symbolic integration. It effectively bridges a previously reported performance gap between models trained with full versus weak supervision, indicating its potential applicability across a spectrum of tasks requiring semantic parsing from large databases.

The practical implications of this research lie in its potential to improve question-answering systems, semantic search, chatbot interfaces, and other AI applications necessitating understanding and reasoning over large KBs. Theoretically, it represents a strategic advancement in neuro-symbolic computing, paving the way for further exploration into complex reasoning tasks combining machine learning with logical inference.

In conclusion, NSM positions itself as a noteworthy development in the field of AI and natural language processing, providing a framework for effective integration of neural networks with symbolic reasoning mechanisms. Future research could extend this framework to include more complex and diverse semantics or broaden its application to domains beyond semantic parsing, potentially enhancing AI's capability to interact with structured data in a meaningful and computationally efficient manner.