Papers with WebNLG dataset
A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation (2024.eacl-short)
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| Challenge: | Generating natural language text from graph-structured data is essential for conversational information seeking. |
| Approach: | They conduct an empirical analysis of conversational large language models in generating natural language text from semantic triples using a WebNLG dataset. |
| Outcome: | The proposed models improve their ability to generate natural language text from semantic triples using few-shot prompting, post-processing, and efficient fine-tuning techniques. |
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs (2020.tacl-1)
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| Challenge: | Recent graph-to-text models generate text from graph data using global or local aggregation . global node encoding allows explicit communication between two distant nodes, but fails to capture long-range relationships. |
| Approach: | They propose to combine global and local aggregation to learn node representations . they propose to use global and locally encoding to learn contextualized node embeddings based on graph data . |
| Outcome: | The proposed models outperform state-of-the-art models on two graph-to-text datasets by 18.01 and 63.69 points. |
Generating Syntactic Paraphrases (D18-1)
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| Challenge: | Using data-to-text generation, text-totext generation and text reduction, we show that conditioning text generation on syntactic constraints permits the generation of syntakically distinct paraphrases for the same input. |
| Approach: | They propose to use four different models for automatic generation of syntactic paraphrases to study the automatic generation process. |
| Outcome: | The proposed models can generate syntactic paraphrases for the same input and exploit different types of input to increase the number of distinct paraphrased for a given input. |
Incremental Beam Manipulation for Natural Language Generation (2021.eacl-main)
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| Challenge: | a larger beam size can lead to deteriorating performance of natural language generation systems due to model errors . performance of NLG systems can plateau or even decrease when beam sizes larger than 10 are used . |
| Approach: | They propose to rerank the output of beam search to produce a good set of hypotheses . they propose incremental beam manipulation to discarded hypothese . |
| Outcome: | The proposed method outperforms a strong reranker on the E2E and WebNLG datasets while being on par with the existing method. |
Referring Expression Generation Using Entity Profiles (D19-1)
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| Challenge: | Existing REG systems rely on entity-specific supervised training, which means they cannot handle entities not seen during training. |
| Approach: | They propose a deep neural network model that encodes both the local context and an external profile of the entity to generate reference realizations. |
| Outcome: | The proposed model outperforms baselines on three different splits of the WebNLG dataset according to automatic and human evaluations. |