Patent application EP20170851A (EP), filed 2020-04-22
Publication number: EP3739499A1
Title: GRAMMAR TRANSFER USING ONE OR MORE NEURAL NETWORKS
Abstract: [0001]    Apparatuses, systems, and techniques to transfer grammar between sentences. In at least one embodiment, one or more first sentences are translated into one or more second sentences having different grammar using one or more neural networks.
IPC codes: G06F  40/    30            A I
G06N  20/    00            A I
G06N   3/    02            A I
Claims: number: 1
text: A processor, comprising:
one or more arithmetic logic units, ALUs, to be configured using one or more neural networks to translate one or more first sentences into one or more second sentences having different grammar from the one or more first sentences.
number: 2
text: The processor of claim 1, wherein the one or more ALUs are further to be configured to:
determine, using at least one of the one or more neural networks, a content code for each of the one or more first sentences, the content code providing a style-independent expression of content of the respective first sentence.
number: 3
text: The processor of claim 2, wherein the one or more ALUs are further to be configured to:
determine, using at least one of the one or more neural networks, a style code to be used for the one or more second sentences, the style codes being inferred from respective sentences.
number: 4
text: The processor of claim 3, wherein the one or more ALUs are further to be configured to:
number: 5
text: The processor of any of the preceding claims, wherein the different grammar corresponds to a different expression of content of the one or more first sentences, the different expression differing in at least one of style, sentiment, structure, or type.
number: 6
text: The processor of any of the preceding claims, wherein at least one of the one or more first sentences is received as audio data and converted into text data, or at least one of the one or more second sentences is provided as audio data generated using a digital voice corresponding to a respective style code.
number: 7
text: The processor of any of the preceding claims, wherein the one or more ALUs are further to be configured to:
minimize cycle-consistency loss during training of at least a subset of the one or more neural networks.
number: 8
text: A system comprising:
number: 9
text: The system of claim 8, wherein the one or more processors are further to be configured as the one or more arithmetic logic units of a processor as mentioned in claims 2 to 5 or 7.
number: 10
text: The system of claim 8 or 9, wherein the one or more processors are configured as a processor as mentioned in any of claims 5 to 7.
number: 11
text: A processor comprising:
one or more arithmetic logic units ,ALUs, to train one or more neural networks to translate one or more first sentences into one or more second sentences having different grammar from the one or more first sentences.
number: 12
text: The processor of claim 11, wherein the one or more ALUs are further to be configured to:
determine, using at least one of the one or more neural networks, a content code for each of the one or more first sentences, the content code providing a style-independent expression of content of the respective first sentence.
number: 13
text: The processor of claim 12, wherein the one or more ALUs are further to be configured to:
determine, using at least one of the one or more neural networks, a style code to be used for the one or more second sentences, the style codes being inferred from respective sentences.
number: 14
text: The processor of claim 13, wherein the one or more ALUs are further to be configured to:
number: 15
text: The processor of any of claims 11 to 14, wherein the different grammar corresponds to a different expression of content of the one or more first sentences, the different expression differing in at least one of style, sentiment, structure, or type.
number: 16
text: The processor of any of claims 11 to 15, wherein at least one of the one or more first sentences is received as audio data and converted into text data, or at least one of the one or more second sentences is provided as audio data generated using a digital voice corresponding to a respective style code.
number: 17
text: The processor of any of claims 11 to 16, wherein the one or more ALUs are further to be configured to:
minimize cycle-consistency loss during training of at least a subset of the one or more neural networks.
number: 18
text: A system, comprising:
number: 19
text: The system of claim 18, wherein the one or more processors are further to be configured to at least one of the following:
number: 20
text: The system of claim 18 or 19, wherein the different grammar corresponds to a different expression of content of the one or more first sentences, the different expression differing in at least one of style, sentiment, structure, or type.
number: 21
text: The system of any of claims 18 to 20, wherein at least one of the one or more first sentences is received as audio data and converted into text data, or at least one of the one or more second sentences is provided as audio data generated using a digital voice corresponding to a respective style code.
number: 22
text: The system of any of claims 18 to 21, wherein the one or more processors are further to be configured to:
minimize cycle-consistency loss during training of at least a subset of the one or more neural networks.
Office action: search_report, dated 2020-11-18
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: US2018338147A1
ref_type: patent
severity: background
ref_id: WO2019052311A1
ref_type: patent
severity: background
ref_id: WO2018075927A1
ref_type: patent
severity: background
ref_id: - YANPENG ZHAO ET AL: "Language Style Transfer from Sentences with Arbitrary Unknown Styles", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 13 August 2018 (2018-08-13), XP081256283
ref_type: npl
severity: background
ref_id: CN108304436A
ref_type: patent
severity: background
ref_id: WO2018075927
ref_type: patent
severity: background
ref_id: US2018338147
ref_type: patent
severity: novelty_destroying
ref_id: WO2019052311
ref_type: patent
severity: novelty_destroying
ref_id: - YANPENG ZHAO ET AL, "Language Style Transfer from Sentences with Arbitrary Unknown Styles", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20180813), XP081256283 [X] 1-3,5,7-8,11-13,15,17-18,20,22 * the whole document * [I] 4,6,9-10,14,16,19,21
ref_type: npl
severity: novelty_destroying
Final disposition: pending (2020-11-18)