| 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. |
Similar Papers
Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on multilingual sentence embeddings focus on cross-lingual semantic textual similarity and transfer tasks. |
| Approach: | They propose a method to improve existing multilingual sentence embeddings with Abstract Meaning Representation (AMR) . they compare existing multi-lingual sentence embedded with AMR and improve their versions by reducing the surface variations across different languages and expressions. |
| Outcome: | The proposed method improves state-of-the-art multilingual sentence embeddings on transfer tasks and semantic textual similarity tests. |
Factorising AMR generation through syntax (N19-1)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) is a semantic annotation framework which abstracts away from the surface form of text to capture the core 'who did what to whom' structure. |
| Approach: | They propose to decompose the generation process into two steps: first generate a syntactic structure, and then generate the surface form. |
| Outcome: | The proposed approach generates meaning-preserving syntactic paraphrases of the same graph, as judged by humans. |
Multilingual Generation in Abstractive Summarization: A Comparative Study (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing models for multilingual generation lack thorough analysis due to extensive linguistic diversity. |
| Approach: | They propose to classify multilingual generation methodologies into three categories based on their underlying modeling principles . they introduce an automatic metric to mitigate spurious correlations associated with language mixing . |
| Outcome: | The proposed model improves in high-resource, low-resourced, and zero-shot scenarios. |
A Partially Rule-Based Approach to AMR Generation (N19-3)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) is a representation of a sentence as a labeled graph . because of these abstractions, it can be difficult to generate from AMR back to a fluent English sentence . |
| Approach: | They propose a new approach to generating English text from Abstract Meaning Representation (AMR) it is largely rule-based, supplemented by a language model and simple statistical linearization models . they also address difficulties of automatically evaluating AMR generation systems . |
| Outcome: | The proposed approach produces a fluent English sentence with a high quality . it is difficult to generate from an AMR back to a sentence which preserves original meaning . |
Bootstrapping Multilingual AMR with Contextual Word Alignments (2021.eacl-main)
Copied to clipboard
Janaki Sheth, Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Radu Florian, Salim Roukos, Todd Ward
| Challenge: | Abstract Meaning Representation (AMR) is a sentence-level graph that is biased towards English. |
| Approach: | They propose a technique for foreign-text-to-English AMR alignment using contextual word alignment between English and foreign language tokens. |
| Outcome: | The proposed technique outperforms the best results for German, Italian, Spanish and Chinese. |
Using Structured Content Plans for Fine-grained Syntactic Control in Pretrained Language Model Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Large pretrained language models can generate powerful text but cannot be controlled at a sub-sentential level. |
| Approach: | They propose to make such fine-grained control possible in pretrained LMs by generating text directly from a semantic representation, Abstract Meaning Representation (BART), which is augmented at the node level with syntactic control tags. |
| Outcome: | The proposed method can generate text from a semantic representation, which is augmented at the node level with syntactic control tags. |
GPT-too: A Language-Model-First Approach for AMR-to-Text Generation (2020.acl-main)
Copied to clipboard
Manuel Mager, Ramón Fernandez Astudillo, Tahira Naseem, Md Arafat Sultan, Young-Suk Lee, Radu Florian, Salim Roukos
| Challenge: | Existing approaches to generating text from AMRs focus on training sequence-to-sequence or graph-tosequent models on annotated data. |
| Approach: | They propose a strong pre-trained language model with cycle consistency-based re-scoring to generate AMR text. |
| Outcome: | The proposed model outperforms existing methods on the English LDC2017T10 dataset. |
Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent work on multilingual AMR-to-text generation has focused on data augmentation strategies that utilize generated silver AMRs, but this assumes a high quality of generated AMR. |
| Approach: | They propose to combine gold AMR with silver AMRs to generate multilingual AMR annotations. |
| Outcome: | The proposed models outperform the current state of the art for German, Italian, Spanish, and Chinese by a large margin. |
XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation (2021.acl-long)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) research is limited and challenging for languages other than English. |
| Approach: | They propose a cross-lingual pre-training approach for AMR parsing and text generation . they use an English-to-English parallel dataset and a multi-task learning approach . |
| Outcome: | The proposed approach outperforms baseline pre-training methods on English parsing and text generation tasks. |
Definition Generation for Word Meaning Modeling: Monolingual, Multilingual, and Cross-Lingual Perspectives (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in text generation have opened up new opportunities for word meaning modeling. |
| Approach: | They extend definition generation task beyond English to a suite of 22 languages . they use Llama-based models to evaluate models in monolingual, multilingual, cross-lingual settings . |
| Outcome: | The proposed model outperforms pretrained models in monolingual, multilingual, and cross-lingual settings. |