Structured graph-to-text generation with two step fine-tuning
Embodiments described herein provide systems and methods for data-to-text generation. The embodiments receive input data that includes a resource description framework (RDF) triples in an RDF graph. A data-to-text generation system generates position aware embeddings, including position embeddings, triple role embeddings, and tree-level embeddings. Using the position aware embeddings and the RDF graph, the data-to-text generation system generates a textual description for the RDF graph.
1. A system comprising:
a non-transitory memory; and
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
receiving, at a data-to-text generation system that includes a generative language model, an input data that includes resource description framework (RDF) triples in an RDF graph;
generating, using the data-to-text generation system, embeddings from the RDF graph based on tokens of the input data, wherein the embeddings include a position aware embedding that identifies a position of an RDF triple of the RDF triples in the RDF graph; and
generating, using the data-to-text generation system, a textual description of the input data based on the embeddings and the RDF graph,
wherein the position aware embedding includes a position embedding that identifies a position of a token indicating whether a word in the RDF triple of the RDF triples is a subject, a relation, or an object.
2. The system of claim 1 , wherein the RDF triple includes words that correspond to a subject, a relation, or an object.
3. The system of claim 1 , wherein the position aware embedding includes a position embedding that identifies a position of a token that stores a word in the RDF triple from the RDF triples.
4. The system of claim 1 , wherein the position aware embedding includes a triple role embedding that identifies that a token includes a word or an indication of a role of the word in the RDF triple from the RDF triples that corresponds to a subject, an object, or a relation.
5. The system of claim 1 , wherein the position aware embedding includes a tree-level embedding that identifies a tree distance from a root of a parsing tree to a level in the parsing tree that includes a token, wherein the token stores a word or an indication of a role of the word in the RDF triple from the RDF triples.
6. The system of claim 1 , wherein the generating the embeddings further comprises generating a token embedding that identifies a token that stores a word or an indication of a role of the word in the RDF triple from the RDF triples.
7. A method comprising:
receiving, at a data-to-text generation system that includes a generative language model, the data-to-text generation system configured to execute on a processor, an input data that includes resource description framework (RDF) triples in an RDF graph;
generating, using the data-to-text generation system, embeddings from the RDF graph based on tokens of the input data, wherein the embeddings include a position aware embedding that identifies a position of an RDF triple of the RDF triples in the RDF graph; and
generating, using the data-to-text generation system, a textual description of the input data based on the position aware embedding and the RDF graph,
wherein the position aware embedding includes a position embedding that identifies a position of a token indicating whether a word in the RDF triple of the RDF triples is a subject, a relation, or an object.
8. The method of claim 7 , further comprising:
training the generative language model to generate the position aware embeddings.
9. The method of claim 7 , wherein the position aware embedding includes a position embedding that identifies a position of a token that stores a word in the RDF triple from the RDF triples.
10. The method of claim 7 , wherein the position aware embedding includes a triple role embedding that identifies that a token includes a word or an indication of a role of the word in the RDF triple from the RDF triples that corresponds to a subject, an object, or a relation.
11. The method of claim 7 , wherein the position aware embedding includes a tree-level embedding that identifies a tree distance from a root of a parsing tree to a level in the parsing tree that includes a token, wherein the token stores a word or an indication of a role of the word in the RDF triple from the RDF triples.
12. The method of claim 7 , wherein the generating the embeddings further comprises generating a token embedding that identifies a token that stores a word or an indication of a role of the word in the RDF triple from the RDF triples.
13. A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:
receiving, at a data-to-text generator that includes a generative language model, an input data that includes structured data triples in a structured graph;
generating, using the data-to-graph generator, embeddings from the structured graph based on tokens of the input data, wherein the embeddings include position aware embeddings that identify position of a triple of triples in the structured graph; and
generating, using a data-to-text module, a textual description of the input data based on the position aware embeddings and the structured graph,
wherein the position aware embeddings include a position embedding that identifies a position of a token indicating whether a word in the triple of the triples is a subject, a relation, or an object.
14. The non-transitory machine-readable medium of claim 13 , wherein the position aware embeddings include a position embedding that identifies a position of a token that stores a word in the triple from the triples.
15. The non-transitory machine-readable medium of claim 13 , wherein the position aware embeddings include a triple role embedding that identifies that a token includes a word or an indication of a role of the word in the triple from the triples that corresponds to a subject, an object, or a relation.
16. The non-transitory machine-readable medium of claim 13 , wherein the position aware embeddings include a tree-level embedding that identifies a tree distance from a root of a parsing tree to a level in the parsing tree that stores a token, wherein the token includes a word or an indication of a role of the word in the triple from the triples.
17. The non-transitory machine-readable medium of claim 13 , wherein the generating the embeddings further comprises generating token embeddings that identify a token that stores a word or an indication of a role of the word in the triple from the triples.