Exploring Neural Models for Parsing Natural Language into First-Order Logic Article Swipe
Hrituraj Singh
,
Milan Aggarwal
,
Balaji Krishnamurthy
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2002.06544
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2002.06544
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2002.06544
- https://arxiv.org/pdf/2002.06544
- OA Status
- green
- Cited By
- 6
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3006228461
All OpenAlex metadata
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https://openalex.org/W3006228461Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2002.06544Digital Object Identifier
- Title
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Exploring Neural Models for Parsing Natural Language into First-Order LogicWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-02-16Full publication date if available
- Authors
-
Hrituraj Singh, Milan Aggarwal, Balaji KrishnamurthyList of authors in order
- Landing page
-
https://arxiv.org/abs/2002.06544Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2002.06544Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2002.06544Direct OA link when available
- Concepts
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Parsing, Computer science, Artificial intelligence, Natural language processing, Order (exchange), Natural language, Natural (archaeology), Programming language, Geography, Business, Archaeology, FinanceTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2024: 1, 2022: 1, 2021: 4Per-year citation counts (last 5 years)
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60Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.variables | 94 |
| abstract_inverted_index.ablations. | 141 |
| abstract_inverted_index.capability | 26 |
| abstract_inverted_index.predicates | 71, 96 |
| abstract_inverted_index.predicting | 107 |
| abstract_inverted_index.First-Order | 20 |
| abstract_inverted_index.consistency | 131 |
| abstract_inverted_index.evaluations | 138 |
| abstract_inverted_index.introducing | 84 |
| abstract_inverted_index.logic-based | 164 |
| abstract_inverted_index.intermediate | 58 |
| abstract_inverted_index.sequentially | 68 |
| abstract_inverted_index.corresponding | 74 |
| abstract_inverted_index.effectiveness | 105 |
| abstract_inverted_index.representation | 18, 59 |
| abstract_inverted_index.encoder-decoder | 81 |
| abstract_inverted_index.representations | 8 |
| abstract_inverted_index.machine-interpretable | 7 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |