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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Question-Answering with Grammatically-Interpretable Representations

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Hamid Palangi

Microsoft Research AI


Paul Smolensky

Microsoft Research AI, Johns Hopkins University


Xiaodong He

Microsoft Research AI


Li Deng

Citadel


DOI:

10.1609/aaai.v32i1.12004


Abstract:

We introduce an architecture, the Tensor Product RecurrentNetwork (TPRN). In our application of TPRN, internal representations—learned by end-to-end optimization in a deep neural network performing a textual question-answering(QA) task—can be interpreted using basic concepts from linguistic theory. No performance penalty need be paid for this increased interpretability: the proposed model performs comparably to a state-of-the-art system on the SQuAD QA task.The internal representation which is interpreted is a Tensor Product Representation: for each input word, the model selects a symbol to encode the word, and a role in which to place the symbol, and binds the two together. The selection is via soft attention. The overall interpretation is built from interpretations of the symbols, as recruited by the trained model, and interpretations of the roles as used by the model. We find support for our initial hypothesis that symbols can be interpreted as lexical-semantic word meanings, while roles can be interpreted as approximations of grammatical roles (or categories)such as subject, wh-word, determiner, etc. Fine-grained analysis reveals specific correspondences between the learned roles and parts of speech as assigned by a standard tagger(Toutanova et al. 2003), and finds several discrepancies in the model’s favor. In this sense, the model learns significant aspectsof grammar, after having been exposed solely to linguistically unannotated text, questions, and answers: no prior linguistic knowledge is given to the model. What is given is the means to build representations using symbols and roles, with an inductive bias favoring use of these in an approximately discrete manner.

Topics: AAAI

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HOW TO CITE:

Hamid Palangi||Paul Smolensky||Xiaodong He||Li Deng Question-Answering with Grammatically-Interpretable Representations Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Hamid Palangi||Paul Smolensky||Xiaodong He||Li Deng Question-Answering with Grammatically-Interpretable Representations AAAI 2018, .

Hamid Palangi||Paul Smolensky||Xiaodong He||Li Deng (2018). Question-Answering with Grammatically-Interpretable Representations. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Hamid Palangi||Paul Smolensky||Xiaodong He||Li Deng. Question-Answering with Grammatically-Interpretable Representations. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Hamid Palangi||Paul Smolensky||Xiaodong He||Li Deng. 2018. Question-Answering with Grammatically-Interpretable Representations. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Hamid Palangi||Paul Smolensky||Xiaodong He||Li Deng. (2018) "Question-Answering with Grammatically-Interpretable Representations", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Hamid Palangi||Paul Smolensky||Xiaodong He||Li Deng, "Question-Answering with Grammatically-Interpretable Representations", AAAI, p., 2018.

Hamid Palangi||Paul Smolensky||Xiaodong He||Li Deng. "Question-Answering with Grammatically-Interpretable Representations". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Hamid Palangi||Paul Smolensky||Xiaodong He||Li Deng. "Question-Answering with Grammatically-Interpretable Representations". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Hamid Palangi||Paul Smolensky||Xiaodong He||Li Deng. Question-Answering with Grammatically-Interpretable Representations. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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