Papers with graph networks
Neural Speech Translation using Lattice Transformations and Graph Networks (D19-53)
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| Challenge: | Existing work on end-to-end systems bypass the need for intermediate representations, but this approach is limited in practical applications. |
| Approach: | They propose a lattice-tosequence model which uses lattics as encoders and graph networks to address two problems by applying latticae transformations and a neural model. |
| Outcome: | The proposed model beats pipeline approaches while being orders of magnitude faster than previous work. |
Discriminative Reasoning for Document-level Relation Extraction (2021.findings-acl)
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| Challenge: | Existing models use graph networks to implicitly model reasoning skills . but it is yet to be seen whether modeling these reasoning skills implicitly is competitive with intuitive reasoning skills between one entity pair in this document. |
| Approach: | They propose a discriminative reasoning framework to explicitly model the paths of reasoning skills between entity pairs in a document. |
| Outcome: | The proposed method outperforms the previous state-of-the-art on the large-scale DocRE dataset. |
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering (C18-1)
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| Challenge: | Existing approaches to Knowledge Base Question Answering focus on semantic parsing . previous work focused on selecting the correct semantic relations and not on the structure of the semantic parses . |
| Approach: | They propose to use Gated Graph Neural Networks to encode the graph structure of the semantic parse. |
| Outcome: | The proposed approach outperforms baseline models that do not explicitly model the structure. |
Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation (2023.emnlp-main)
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| Challenge: | Using a shared vocabulary is common practice in multilingual machine translation . however, when words overlap is small, e.g., using different writing systems, knowledge transfer is inhibited . |
| Approach: | They propose a re-parameterized method for building word embeddings using word equivalence classes and graph networks to fuse word embeds across languages. |
| Outcome: | The proposed method achieves evident BLEU improvements on high- and low-resource MNMT scenarios. |
NER-guided Comprehensive Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (2024.lrec-main)
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| Challenge: | Hierarchical text classification (HTC) is a challenging task in natural language processing due to its complex taxonomic label hierarchy. |
| Approach: | They propose to use prompts to model hierarchical text classification (HTC) they propose to introduce conditional random fields and Global Pointer to establish hierarchic dependencies . |
| Outcome: | The proposed approach achieves state-of-the-art (SoTA) performance on three public datasets. |