Papers with neural parser
AMR Parsing as Graph Prediction with Latent Alignment (P18-1)
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| Challenge: | Abstract meaning representations (AMRs) are sentence-level semantic representations . lack of explicit alignments between nodes in graphs and words in sentences is a challenge . |
| Approach: | They propose a neural parser which treats alignments as latent variables within a joint probabilistic model of concepts, relations and alignments. |
| Outcome: | The proposed parser achieves the best reported results on the standard benchmark (74.4% on LDC2016E25). |
Parsers Know Best: German PP Attachment Revisited (2020.coling-main)
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| Challenge: | PP attachment is one of the major sources of parser errors and is still one of hardest problems for syntactic parsing. |
| Approach: | They present a realistic evaluation of the potential of different PP attachment systems using fully predicted information as system input. |
| Outcome: | The proposed approach is superior to modelling PP attachment disambiguation as a separate task. |
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. |
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. |
Reranking for Neural Semantic Parsing (P19-1)
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| Challenge: | Semantic parsing is the task of transducing natural language utterances into machine executable meaning representations (e.g., Python code). |
| Approach: | They propose to rerank an n-best list of predicted MRs and use features to fix observed problems with baseline models to improve parser performance. |
| Outcome: | The proposed method outperforms the best published neural parser on four datasets and improves the baseline parsing performance by 5.7% and 2.9%. |
Text Genre and Training Data Size in Human-like Parsing (D19-1)
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| Challenge: | Using domain-specific training, NLP systems work better, but only when the training examples come from the same textual genre. |
| Approach: | They relate the states of a neural phrase-structure parser to electrophysiological measures from human participants. |
| Outcome: | The proposed model is well-matched to the training data from human participants, but only when the training examples come from the same genre. |