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

10/31/2016
by   Chen Liang, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/04/2016

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

Extending the success of deep neural networks to natural language unders...
research
10/04/2018

Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

We marry two powerful ideas: deep representation learning for visual rec...
research
11/28/2016

Learning a Natural Language Interface with Neural Programmer

Learning a natural language interface for database tables is a challengi...
research
12/08/2016

Coupling Distributed and Symbolic Execution for Natural Language Queries

Building neural networks to query a knowledge base (a table) with natura...
research
12/05/2018

Photo-Realistic Blocksworld Dataset

In this report, we introduce an artificial dataset generator for Photo-r...
research
02/19/2022

Do Transformers use variable binding?

Increasing the explainability of deep neural networks (DNNs) requires ev...
research
10/29/2020

Less is More: Data-Efficient Complex Question Answering over Knowledge Bases

Question answering is an effective method for obtaining information from...

Please sign up or login with your details

Forgot password? Click here to reset