Exploring Neural Models for Parsing Natural Language into First-Order
Logic
- URL: http://arxiv.org/abs/2002.06544v1
- Date: Sun, 16 Feb 2020 09:22:32 GMT
- Title: Exploring Neural Models for Parsing Natural Language into First-Order
Logic
- Authors: Hrituraj Singh, Milan Aggrawal, Balaji Krishnamurthy
- Abstract summary: We study the capability of neural models in parsing English sentences to First-Order Logic (FOL)
We model FOL parsing as a sequence to sequence mapping task where given a natural language sentence, it is encoded into an intermediate representation using an LSTM followed by a decoder which sequentially generates the predicates in the corresponding FOL formula.
- Score: 10.62143644603835
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Semantic parsing is the task of obtaining machine-interpretable
representations from natural language text. We consider one such formal
representation - First-Order Logic (FOL) and explore the capability of neural
models in parsing English sentences to FOL. We model FOL parsing as a sequence
to sequence mapping task where given a natural language sentence, it is encoded
into an intermediate representation using an LSTM followed by a decoder which
sequentially generates the predicates in the corresponding FOL formula. We
improve the standard encoder-decoder model by introducing a variable alignment
mechanism that enables it to align variables across predicates in the predicted
FOL. We further show the effectiveness of predicting the category of FOL entity
- Unary, Binary, Variables and Scoped Entities, at each decoder step as an
auxiliary task on improving the consistency of generated FOL. We perform
rigorous evaluations and extensive ablations. We also aim to release our code
as well as large scale FOL dataset along with models to aid further research in
logic-based parsing and inference in NLP.
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