Papers by Yinliang Yue
Improving Encoder by Auxiliary Supervision Tasks for Table-to-Text Generation (2021.acl-long)
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| Challenge: | Experimental results show that our method not only has a good generalization but also outperforms previous methods on several metrics: BLEU, Content Selection, Content Ordering. |
| Approach: | They propose to build an entity graph from the input tables and introduce a reasoning module to perform reasoning on the graph. |
| Outcome: | The proposed method outperforms previous methods on several metrics: BLEU, Content Selection, Content Ordering. |
Graph-to-Text Generation with Dynamic Structure Pruning (2022.coling-1)
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| Challenge: | Recent studies show that explicitly modeling the input graph structure can significantly improve the performance. |
| Approach: | They propose a structure-aware cross-attention mechanism to re-encode the graph representation conditioning on the newly generated context at each decoding step. |
| Outcome: | The proposed model improves performance on two graph-to-text datasets with only minor increase on computational cost. |
A Generic Method for Fine-grained Category Discovery in Natural Language Texts (2024.emnlp-main)
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| Challenge: | Existing methods for fine-grained category discovery neglect semantic similarities of fine-grain categories. |
| Approach: | They propose a method that detects fine-grained clusters of semantically similar texts guided by a novel objective function. |
| Outcome: | The proposed method surpasses state-of-the-art methods on three benchmark tasks. |