Using Neural Networks to Generate Inferential Roles for Natural Language
Neural networks have long been used to study linguistic phenomena spanning the domains of phonology, morphology, syntax, and semantics. Of these domains, semantics is somewhat unique in that there is little clarity concerning what a model needs to be able to do in order to provide an account of how...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-01-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fpsyg.2017.02335/full |
| _version_ | 1819170848743882752 |
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| author | Peter Blouw Chris Eliasmith |
| author_facet | Peter Blouw Chris Eliasmith |
| author_sort | Peter Blouw |
| collection | DOAJ |
| description | Neural networks have long been used to study linguistic phenomena spanning the domains of phonology, morphology, syntax, and semantics. Of these domains, semantics is somewhat unique in that there is little clarity concerning what a model needs to be able to do in order to provide an account of how the meanings of complex linguistic expressions, such as sentences, are understood. We argue that one thing such models need to be able to do is generate predictions about which further sentences are likely to follow from a given sentence; these define the sentence's “inferential role.” We then show that it is possible to train a tree-structured neural network model to generate very simple examples of such inferential roles using the recently released Stanford Natural Language Inference (SNLI) dataset. On an empirical front, we evaluate the performance of this model by reporting entailment prediction accuracies on a set of test sentences not present in the training data. We also report the results of a simple study that compares human plausibility ratings for both human-generated and model-generated entailments for a random selection of sentences in this test set. On a more theoretical front, we argue in favor of a revision to some common assumptions about semantics: understanding a linguistic expression is not only a matter of mapping it onto a representation that somehow constitutes its meaning; rather, understanding a linguistic expression is mainly a matter of being able to draw certain inferences. Inference should accordingly be at the core of any model of semantic cognition. |
| first_indexed | 2024-12-22T19:41:55Z |
| format | Article |
| id | doaj.art-621bb1b777cc45c5b6e5ff8b7c0b0b1e |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-22T19:41:55Z |
| publishDate | 2018-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-621bb1b777cc45c5b6e5ff8b7c0b0b1e2022-12-21T18:14:49ZengFrontiers Media S.A.Frontiers in Psychology1664-10782018-01-01810.3389/fpsyg.2017.02335295741Using Neural Networks to Generate Inferential Roles for Natural LanguagePeter BlouwChris EliasmithNeural networks have long been used to study linguistic phenomena spanning the domains of phonology, morphology, syntax, and semantics. Of these domains, semantics is somewhat unique in that there is little clarity concerning what a model needs to be able to do in order to provide an account of how the meanings of complex linguistic expressions, such as sentences, are understood. We argue that one thing such models need to be able to do is generate predictions about which further sentences are likely to follow from a given sentence; these define the sentence's “inferential role.” We then show that it is possible to train a tree-structured neural network model to generate very simple examples of such inferential roles using the recently released Stanford Natural Language Inference (SNLI) dataset. On an empirical front, we evaluate the performance of this model by reporting entailment prediction accuracies on a set of test sentences not present in the training data. We also report the results of a simple study that compares human plausibility ratings for both human-generated and model-generated entailments for a random selection of sentences in this test set. On a more theoretical front, we argue in favor of a revision to some common assumptions about semantics: understanding a linguistic expression is not only a matter of mapping it onto a representation that somehow constitutes its meaning; rather, understanding a linguistic expression is mainly a matter of being able to draw certain inferences. Inference should accordingly be at the core of any model of semantic cognition.http://journal.frontiersin.org/article/10.3389/fpsyg.2017.02335/fullnatural language inferencerecursive neural networkslanguage comprehensionsemantics |
| spellingShingle | Peter Blouw Chris Eliasmith Using Neural Networks to Generate Inferential Roles for Natural Language Frontiers in Psychology natural language inference recursive neural networks language comprehension semantics |
| title | Using Neural Networks to Generate Inferential Roles for Natural Language |
| title_full | Using Neural Networks to Generate Inferential Roles for Natural Language |
| title_fullStr | Using Neural Networks to Generate Inferential Roles for Natural Language |
| title_full_unstemmed | Using Neural Networks to Generate Inferential Roles for Natural Language |
| title_short | Using Neural Networks to Generate Inferential Roles for Natural Language |
| title_sort | using neural networks to generate inferential roles for natural language |
| topic | natural language inference recursive neural networks language comprehension semantics |
| url | http://journal.frontiersin.org/article/10.3389/fpsyg.2017.02335/full |
| work_keys_str_mv | AT peterblouw usingneuralnetworkstogenerateinferentialrolesfornaturallanguage AT chriseliasmith usingneuralnetworkstogenerateinferentialrolesfornaturallanguage |