| Challenge: | Knowledge bases are becoming an enabling resource for many applications including Q&A systems, recommender systems, and summarization tools. |
| Approach: | They propose a system to translate RDF triples into natural sentences using an encoder-decoder framework. |
| Outcome: | The proposed model outperforms the baseline model by 17.6%, 6.0%, and 16.4% in terms of BLEU, METEOR, and TER scores. |
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Text Generation from Knowledge Graphs with Graph Transformers (N19-1)
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| Challenge: | Existing methods for generating text with structured inputs are expensive and require manual annotation. |
| Approach: | They propose a graph transforming encoder which leverages relational structure of knowledge graphs without imposing linearization or hierarchical constraints. |
| Outcome: | The proposed system produces more informative texts than competing methods. |
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph (2020.emnlp-main)
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| Challenge: | Existing models for knowledge-to-text generation use RDF triples or key-value pairs to generate a natural language description. |
| Approach: | They propose a large-scale dataset to facilitate the study of KG-to-text . they propose MGCN model architecture that incorporates aggregation methods to extract the rich graph information. |
| Outcome: | The proposed model can represent the original graph information more comprehensively and integrates multiple aggregation methods to extract the rich graph information. |
A Graph-to-Sequence Model for AMR-to-Text Generation (P18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic formalism that encodes the meaning of a sentence as a rooted, directed graph. |
| Approach: | They propose a neural graph-to-sequence model that leverages LSTM to encode a linearized AMR structure. |
| Outcome: | The proposed model outperforms existing methods on a benchmark. |
Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modelling (2025.emnlp-main)
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Sydney Anuyah, Mehedi Mahmud Kaushik, Sri Rama Krishna Reddy Dwarampudi, Rakesh Shiradkar, Arjan Durresi, Sunandan Chakraborty
| Challenge: | integrating coreference and decomposition increases recall on rare relations by over 20%. |
| Approach: | They propose an open-source pipeline for extracting sentence-level knowledge graphs by combining robust coreference resolution with syntactic sentence decomposition. |
| Outcome: | The proposed pipeline achieves a 99.8% exact-match accuracy on sentence simplification. |
Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters (2021.naacl-main)
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| Challenge: | Abstractive multi-document summarization (MDS) is a task that has seen advances with the introduction of large-scale datasets and powerful Transformer-based models. |
| Approach: | They propose an efficient graph-enhanced approach to multi-document summarization with an encoder-decoder Transformer model. |
| Outcome: | The proposed model scales to large input documents and improves on a multi-document dataset. |
Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction (L18-1)
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| Challenge: | Currently, the construction and updating of knowledge bases rely on human labor. |
| Approach: | They propose to map relational phrases in triples from natural language to knowledge base predicate format. |
| Outcome: | The proposed mapping results show high quality and promising coverage on relational phrases compared to previous research. |
Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation (2020.acl-main)
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| Challenge: | Current sequence-to-sequence models require serialized input, resulting in loss of structural information. |
| Approach: | They propose a dual encoding model that incorporates the graph structure and caters to the linear structure of the output text. |
| Outcome: | Empirical results show that dual encoding can improve the quality of natural language descriptions. |
Hallucination Mitigation in Natural Language Generation from Large-Scale Open-Domain Knowledge Graphs (2023.emnlp-main)
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| Challenge: | Graph-to-text models trained on small-scale datasets or datasets with limited variety of graph shapes are not adequate for more realistic large-scale, open-domain settings. |
| Approach: | They propose a novel approach that, given a graph-sentence pair in GraphNarrative, trims the sentence to eliminate portions that are not present in the corresponding graph. |
| Outcome: | The proposed model can be trained on existing datasets and is available on github. |
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information. |
| Approach: | They propose to use syntactic graph to represent three types of syntaktic information . they then employ a graph-to-sequence model to encode the syntastic graph and decode a logical form . |
| Outcome: | The proposed model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. |
A Simple and Effective Unified Encoder for Document-Level Machine Translation (2020.acl-main)
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| Challenge: | Existing models for document-level machine translation use two separate encoders to model the source sentences and document- level contexts. |
| Approach: | They propose a unified encoder that can outperform existing models of dual-encoder models . they propose to use document-level contexts to model the interaction between the contexts and the source sentences . |
| Outcome: | The proposed model outperforms baseline models of dual-encoder models in terms of BLEU and METEOR scores. |