Entity-conditioned sentence generation
AssigneeInternational Business Machines Corporation
InventorsSiyu Huo, Vatche Isahagian, Vinod Muthusamy, Praveen Venkateswaran, Kushal Mukherjee, Jayachandu Bandlamudi
Life of this patent
Estimating the term…
Abstract
An example operation may include one or more of tuning a language model based on dependencies between an original data set and a paraphrase data set of the original data set, parsing and annotating the paraphrase dataset with entity identifiers of predefined entities to generate an annotated paraphrase dataset, additionally tuning the language model based on entity dependencies between the original data set and the paraphrase data set based on the annotated paraphrase dataset, and storing the additionally tuned language model in a storage device.
Claims20
An apparatus, comprising:
- one or more processors;
- and
- one or more memory devices coupled to the one or more processors and configured to store a language model, wherein the one or more processors are configured to:
- tune the language model based on dependencies between an original data set and a paraphrase data set of the original data set, wherein the language model is a machine learning language model;
- parse and annotate the paraphrase data set with entity identifiers of predefined entities to generate an annotated paraphrase data set;
- iteratively execute the tuned language model on the annotated paraphrase data set to identify entity dependencies between sentences included in the original data set and entities annotated within the annotated paraphrase data set;
- additionally tune the language model, by changing variables and weights of the language model, based on the entity dependencies between the original data set and the paraphrase data set based on the annotated paraphrase data set;
- store the additionally tuned language model in the one or more memory devices;
- and
- execute the additionally tuned model on a different data set to generate a plurality of additional paraphrase data sets based on entities in the different data set.
A method, comprising:
- tuning a language model based on dependencies between an original data set and a paraphrase data set of the original data set;
- parsing and annotating the paraphrase data set with entity identifiers of predefined entities to generate an annotated paraphrase data set;
- iteratively executing the tuned language model on the annotated paraphrase data set to identify entity dependencies between sentences included in the original data set and entities annotated within the annotated paraphrase data set;
- additionally tuning the language model, by changing variables and weights of the language model, based on the entity dependencies between the original data set and the paraphrase data set based on the annotated paraphrase data set;
- storing the additionally tuned language model in a storage device;
- and
- executing the additionally tuned model on a different data set to generate a plurality of additional paraphrase data sets based on entities in the different data set.
A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
- one or more instructions that, when executed by one or more processors of a device, cause the device to:
- tune a language model based on dependencies between an original data set and a paraphrase data set of the original data set;
- parse and annotate the paraphrase data set with entity identifiers of predefined entities to generate an annotated paraphrase data set;
- iteratively execute the tuned language model on the annotated paraphrase data set to identify entity dependencies between sentences included in the original data set and entities annotated within the annotated paraphrase data set;
- additionally tune the language model, by changing variables and weights of the language model, based on the entity dependencies between the original data set and the paraphrase data set based on the annotated paraphrase data set;
- store the additionally tuned language model in a storage device;
- and
- execute the additionally tuned model on a different data set to generate a plurality of additional paraphrase data sets based on entities in the different data set.
Dependencies are inferred from the claim text.