Papers with Generating text
Promoting Graph Awareness in Linearized Graph-to-Text Generation (2021.findings-acl)
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| Challenge: | Recent applications of pretrained transformers to linearizations of graph inputs yield stateof-the-art results on graph-to-text tasks. |
| Approach: | They propose to use pretrained transformers to encode local graph structures . they find they can improve the quality of models' implicit graph encodings . |
| Outcome: | The proposed models can encode local graph structures and reconstruct corrupted inputs. |
Multilingual AMR-to-Text Generation (2020.emnlp-main)
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| Challenge: | Existing work on generating text from structured data into English has focused on bridging the gap between structure and natural language (NL) and semantically underspecified input and fully specified output. |
| Approach: | They propose a multilingual approach that can decode into 21 different languages . they leverage advances in cross-lingual embeddings and pretraining to generate multilingual models . |
| Outcome: | The proposed model surpasses baselines that generate into one language in eighteen languages. |
Enhancing AMR-to-Text Generation with Dual Graph Representations (D19-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a linguistically-grounded semantic formalism that represents the meaning of a sentence as a directed graph. |
| Approach: | They propose a graph-to-sequence model that encodes different but complementary perspectives of the structural information contained in the graph. |
| Outcome: | The proposed model achieves state-of-the-art results on two AMR datasets. |
Unsupervised Natural Language Generation with Denoising Autoencoders (D18-1)
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| Challenge: | Unsupervised approaches to generating text from structured data are costly to obtain and limited to a limited domain. |
| Approach: | They propose an unsupervised approach that learns its parameters without the slot pairs on target sequences only. |
| Outcome: | The proposed approach can generate sentences out of corrupted data without supervision . it can be used in question answering and dialog systems, the authors show . |