Papers by Zi Lin
Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification (2022.emnlp-main)
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| Challenge: | Pre-trained models excel at graph semantic parsing with rich annotated data, but generalize poorly to out-of-distribution and long-tail examples. |
| Approach: | They propose a compositionality-aware approach to neural-symbolic inference informed by model confidence to capture different aspects of the graph prediction. |
| Outcome: | The proposed method outperforms state-of-the-art models on an English resource grammar parsing problem on standard in-domain and seven OOD corpora. |
Retrieval-Augmented Parsing for Complex Graphs by Exploiting Structure and Uncertainty (2023.findings-emnlp)
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| Challenge: | Retrieval augmentation is effective for large graph parsing tasks, but can fail to identify the most informative exemplars . structure-aware and uncertainty-guided adaptive retrieval (SUGAR) exploits two unique sources of information: structural similarity and model uncertainty. |
| Approach: | They propose a structure-aware and uncertainty-guided adaptive retrieval approach that exploits structural similarity and model uncertainty to improve retrieval-augmented parsing for complex graph problems. |
| Outcome: | The proposed method improves retrieval-augmented parsing for graph parsers with large output graphs and non-trivial structure. |
Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior (2020.findings-emnlp)
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| Challenge: | Spectral-normalized identity priors (SNIP) is a structured pruning approach for a Transformer model. |
| Approach: | They propose a structured pruning approach which penalizes an entire residual module toward an identity mapping. |
| Outcome: | The proposed method improves on 5 GLUE benchmark tasks while maintaining comparable performance. |
Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data (D18-1)
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| Challenge: | a learner language (interlanguage) is an idiolect developed by a learning of a second or foreign language. |
| Approach: | They propose to use semantic role labeling as a case task to parse interlanguages . they then evaluate three off-the-shelf SRL systems to gauge how successful they are . |
| Outcome: | The proposed model achieves an F-score of 72.06, a 2.02 point improvement over the baseline. |
Hint-Based Training for Non-Autoregressive Machine Translation (D19-1)
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| Challenge: | AutoRegressive Translation models have to generate tokens sequentially during decoding and thus suffer from high inference latency. |
| Approach: | They propose to use hidden states and word alignments to help train NART models. |
| Outcome: | The proposed model improves on the WMT14 En-De and De-En datasets but is faster in inference than the current models. |
ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation (2023.findings-emnlp)
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| Challenge: | toxicity detection has been largely based on social media content, leaving the unique challenges inherent to real-world user-AI interactions insufficiently explored. |
| Approach: | They propose a benchmark to detect toxicity in real-world user-AI conversations . they compare existing models with social media content to find toxicity . |
| Outcome: | The proposed benchmark reveals that existing models fail to recognize toxicity in real-world user-AI conversations. |
Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty (2022.findings-acl)
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| Challenge: | Recent work in task-independent graph semantic parsing has shifted from symbolic approaches to neural models, showing strong performance on different types of meaning representations. |
| Approach: | They propose a framework that incorporates prior knowledge from a symbolic parser into a decision criterion for beam search to address these limitations. |
| Outcome: | The proposed framework improves on the in-distribution test set but degrades significantly on long-tail situations while the symbolic parser performs more robustly. |