| Challenge: | Existing parsing systems use local or global models of the parser state to improve performance. |
| Approach: | They propose to modify the sequence-to-sequence Transformer to model global or local parser states in transition-based parsing. |
| Outcome: | The proposed model significantly improves performance on dependency and Abstract Meaning Representation (AMR) parsing tasks. |
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Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing (2021.emnlp-main)
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| Challenge: | Recent work shows that pre-trained sequence-to-sequence Transformer models are effective in predicting linearized Abstract Meaning Representation graphs. |
| Approach: | They propose a structure-aware transition-based approach to AMR parsing that integrates general pre-trained sequence-to-sequence language models with a structured transition set. |
| Outcome: | The proposed approach retains the desirable properties of previous approaches while reaching the new parsing state of the art for AMR 2.0. |
AMR Parsing with Action-Pointer Transformer (2021.naacl-main)
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| Challenge: | Abstract Meaning Representation parsing is a sentence-to-graph prediction task . graph nodes are semantically based on one or more sentence tokens, so implicit alignments can be derived. |
| Approach: | They propose a transition-based system that decouples hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments. |
| Outcome: | The proposed system achieves the second best Smatch score on AMR 2.0 (81.8) it decouples source tokens from node representations and addresses alignments, but lacks expressiveness. |
Recursive Subtree Composition in LSTM-Based Dependency Parsing (N19-1)
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| Challenge: | Existing studies show that tree structure modelling on top of sequence modelling is not feasible. |
| Approach: | They propose to recursively compose subtree representations in a biLSTM-based parser to capture subtreas. |
| Outcome: | The proposed model improves performance under ablating the backward LSTM and the forward LS. |
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning (P19-1)
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| Challenge: | Abstract meaning representations (AMRs) are labeled directed acyclic graphs that represent a non intersentential abstraction of natural language with broad-coverage semantic representations. |
| Approach: | They build upon a transition-based AMR parser that uses Stack-LSTMs and augment training with policy learning. |
| Outcome: | The proposed parser performs comparable to the best published parsers. |
A Unifying Theory of Transition-based and Sequence Labeling Parsing (2020.coling-main)
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| Challenge: | Existing parsers that read sentences from left to right are not learning to parse them. |
| Approach: | They propose a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees. |
| Outcome: | The proposed algorithms are learnable and comparable to existing encodings. |
Sentence-State LSTM for Text Representation (P18-1)
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| Challenge: | LSTMs have been shown to suffer from various limitations due to their sequential nature. |
| Approach: | They propose to model hidden states of all words simultaneously at each recurrent step rather than one word at a time. |
| Outcome: | The proposed model has strong representation power, giving competitive performances compared to stacked BiLSTM models with similar parameter numbers. |
Stack-Pointer Networks for Dependency Parsing (P18-1)
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| Challenge: | Existing approaches to dependency parsing are local and greedy transitionbased . StackPtr parsers use the information of whole sentences and previously derived subtree structures . |
| Approach: | They propose a stack-pointer network-based dependency parser that reads whole sentence and builds dependency tree top-down in a depth-first fashion. |
| Outcome: | The proposed model reads and encodes whole sentence, then builds dependency tree top-down (from root-to-leaf) in a depth-first fashion. |
Sequence-to-sequence Models for Cache Transition Systems (P18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic formalism where the meaning of a sentence is encoded as a rooted, directed graph. |
| Approach: | They propose a sequence-to-sequence based approach for mapping natural language sentences to AMR semantic graphs using a special transition system called a cache transition system. |
| Outcome: | The proposed model outperforms other sequence-to-sequence approaches and achieves competitive results in comparison with the best-performing models. |
AMR Parsing as Sequence-to-Graph Transduction (P19-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is the task of transducing natural language text into AMR, a graphbased formalism used for capturing sentence-level semantics. |
| Approach: | They propose a model that treats AMR parsing as sequence-to-graph transduction by aligner-free, and can be effectively trained with limited amounts of labeled AMR data. |
| Outcome: | The proposed model outperforms all previously reported SMATCH scores on AMR 2.0 (76.3%) and AMR 1.0 (70.2%). |
Cross-domain Generalization for AMR Parsing (2022.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input. |
| Approach: | They evaluate five representative AMR parsers on five domains and analyze challenges to cross-domain parsing. |
| Outcome: | The proposed method reduces the domain distribution divergence of text and AMR features on two out-of-domain sets. |