Papers by Ruiying Geng
Plan-then-Seam: Towards Efficient Table-to-Text Generation (2023.findings-eacl)
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| Challenge: | Recent work explicitly decomposes the generation process into content planning and surface generation stages, employing two autoregressive networks for them respectively. |
| Approach: | They propose a non-parallelelizable table-to-text model that produces outputs in parallel with one network. |
| Outcome: | The proposed model achieves 3.0 5.6 times speedup for inference time, reducing 50% parameters, while maintaining as least comparable performance against strong two-stage table-to-text competitors. |
Induction Networks for Few-Shot Text Classification (D19-1)
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| Challenge: | Recent studies have used meta-learning to simulate the few-shot task . however, this sample-wise comparison may be severely disturbed by the various expressions in the same class. |
| Approach: | They propose a meta-learning-based induction network to learn a generalized class-wise representation of each class in a support set. |
| Outcome: | The proposed model outperforms existing state-of-the-art models on a sentiment and dialogue intent datasets. |
Dynamic Memory Induction Networks for Few-Shot Text Classification (2020.acl-main)
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| Challenge: | Recent studies have shown that models can benefit from query-aware methods for few-shot text classification. |
| Approach: | They propose a dynamic memory-based network for few-short text classification that uses static memory to adapt to unseen classes. |
| Outcome: | The proposed model improves on the miniRCV1 and ODIC datasets by 24% . Detailed analysis is performed to show how the proposed network achieves the new performance. |
S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers (2022.findings-acl)
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| Challenge: | Existing graph-based encoders for text-to-SQL do not model the syntax of natural language questions. |
| Approach: | They propose to inject Syntax to question-Schema graph encoder for text-to-SQL parsers and employ the decoupling constraint to induce diverse relational edge embedding. |
| Outcome: | The proposed approach outperforms all existing methods when pre-training models are used, resulting in a performance ranks first on the Spider leaderboard. |
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. |
CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality (2023.acl-long)
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Liang Li, Ruiying Geng, Chengyang Fang, Bing Li, Can Ma, Rongyu Cao, Binhua Li, Fei Huang, Yongbin Li
| Challenge: | Current datasets bias in the English language while leaving other languages underexplored. |
| Approach: | They propose a Chinese answer-to-sequence dataset with high quality and large scale . they propose encoding space for two hybrid knowledge resources to convert this task to a graph-totext problem. |
| Outcome: | The proposed method is effective in generating textual descriptions for the Chinese answer-to-sequence dataset. |