Papers by David Vilares
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. |
Viable Dependency Parsing as Sequence Labeling (N19-1)
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| Challenge: | Existing work on dependency parsing by sequence labeling suggested that it was impractical. |
| Approach: | They propose to use dependency trees as sequence labels to obtain fast and accurate parsers using a conventional BILSTM-based model. |
| Outcome: | The proposed models are conceptually simple, not needing traditional parsing algorithms or auxiliary structures, and provide a good speed-accuracy tradeoff, with results competitive with more complex approaches. |
Better, Faster, Stronger Sequence Tagging Constituent Parsers (N19-1)
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| Challenge: | Existing efforts to speed up constituent parsing have focused on chart-based or shift-reduce parsers. |
| Approach: | They propose to use auxiliary losses and sentence-level fine-tuning to mitigate greedy decoding issues. |
| Outcome: | The proposed model surpasses the performance of sequence tagging constituent parsers on the English and Chinese Penn Treebank datasets and reduces their parsing time even further. |
Parsing linearizations appreciate PoS tags - but some are fussy about errors (2022.aacl-short)
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| Challenge: | Recent work on the impact of PoS tags on graph- and transition-based parsers suggests that they are only useful when tagging accuracy is prohibitively high or in low-resource scenarios. |
| Approach: | They examine the impact of PoS tags on graph- and transition-based parsers and propose to use them in a new paradigm for sequence labeling. |
| Outcome: | The proposed model is best when tagging accuracy and resource availability are high. |
Constituent Parsing as Sequence Labeling (D18-1)
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| Challenge: | Constituent parsing is a core problem in NLP where the goal is to obtain the syntactic structure of sentences expressed as a phrase structure tree. |
| Approach: | They propose a method to reduce constituent parsing to sequence labeling by using a tree with unary branches. |
| Outcome: | The proposed method outperforms the Vinyals et al. (2015) sequence-to-sequence parser by 90% on the PTB and CTB treebanks. |
Dependency Graph Parsing as Sequence Labeling (2024.emnlp-main)
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| Challenge: | Various linearizations have been proposed to cast syntactic dependency parsing as sequence labeling, but they cannot handle reentrancy or cycles. |
| Approach: | They propose unbounded linearizations that can be used to cast dependency parsing as sequence labeling. |
| Outcome: | The proposed linearizations can cast syntactic dependency parsing as a sequence labeling task. |
Bracketing Encodings for 2-Planar Dependency Parsing (2020.coling-main)
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| Challenge: | Existing bracketing-based encodings only handle a mild extension of projective trees . encodes that encode arcs in a given plane provide almost total coverage of crossing arc . |
| Approach: | They propose a bracketing-based encoding that can be used to represent any 2-planar dependency tree over a sentence of length n as a sequence of n labels. |
| Outcome: | The proposed method improves over existing bracketing encodings in non-projective treebanks while achieving similar speed. |
HEAD-QA: A Healthcare Dataset for Complex Reasoning (P19-1)
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| Challenge: | Recent progress in question answering has been led by neural models, but current methods are too data intensive and weak. |
| Approach: | They propose a multi-choice question answering testbed to encourage research on complex reasoning. |
| Outcome: | The proposed dataset is useful as a benchmark for future work. |
4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees (2023.emnlp-main)
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| Challenge: | encodings that map trees into sequences of one discrete label per word have been proposed for constituency parsing and dependency parse. |
| Approach: | They propose an encoding that can represent any projective dependency tree as a sequence of 4-bit labels, one per word. |
| Outcome: | The proposed encoding achieves substantial accuracy gains over the previously best-performing sequence labeling encoders. |
Towards Making a Dependency Parser See (D19-1)
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| Challenge: | Eye trackers and gaze features collected from them have been recently applied to natural language processing (NLP) tasks such as part-of-speech tagging. |
| Approach: | They propose to leverage eye-tracking data in an RNN dependency parser when no aggregated or token-level gaze features are used at inference time. |
| Outcome: | The proposed model can be used to improve performance on non-gazed treebanks. |
A Transition-Based Algorithm for Unrestricted AMR Parsing (N18-2)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation language to map the meaning of English sentences into directed, cycled, labeled graphs. |
| Approach: | They propose a left-to-right non-projective transition-based parser that handles cycles and reentrancy natively within the transition system itself. |
| Outcome: | The proposed algorithm handles reentrancy and arbitrary cycles natively, i.e. within the transition system itself. |
Harry Potter and the Action Prediction Challenge from Natural Language (N19-1)
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| Challenge: | Using textual descriptions of scenes, we explore the challenge of action prediction from textual description. |
| Approach: | They propose a testbed to approximate whether text inference can be used to predict upcoming actions from textual descriptions of scenes. |
| Outcome: | The proposed model performs best for frequent actions and large scene descriptions, but logistic regression fails on infrequent actions. |
Discontinuous Constituent Parsing as Sequence Labeling (2020.emnlp-main)
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| Challenge: | Existing approaches to discontinuous parsing are complex and low-level. |
| Approach: | They propose to encode discontinuities as nearly ordered permutations of the input sequence. |
| Outcome: | The proposed model is fast and accurate under the right representation. |
Bringing Emerging Architectures to Sequence Labeling in NLP (2026.eacl-long)
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| Challenge: | Pretrained Transformer encoders are the dominant approach to sequence labeling . however, few have been applied to sequence labels on flat or simplified tasks . |
| Approach: | They propose to use pretrained Transformer encoders to model relations across words . they find that the architectures adapt well across tagging tasks that vary in complexity . |
| Outcome: | The proposed architectures perform well across tagging tasks across languages and datasets. |
The Fragility of Multi-Treebank Parsing Evaluation (2022.coling-1)
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| Challenge: | a limitation in NLP evaluation lies in the association between solving a dataset versus solving . authors often run their models only in a handful of treebanks . |
| Approach: | They propose to run a large-scale experiment on a single treebank and compare them on many parsers whose scores are available. |
| Outcome: | The proposed model can be biased on a single treebank and spurious effects can be avoided. |
Another Dead End for Morphological Tags? Perturbed Inputs and Parsing (2023.findings-acl)
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| Challenge: | Part-of-speech tags are used for word-contextualized parsers, but their impact is limited to word-based models. |
| Approach: | They propose an adversarial attack to test whether morphological tags contribute to error propagation or correct parsing mistakes. |
| Outcome: | The proposed attack on 14 treebanks shows that if morphological tags were utopically robust against lexical perturbations, they would be able to correct parsing mistakes. |
From Partial to Strictly Incremental Constituent Parsing (2024.eacl-short)
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| Challenge: | Incremental NLP aims to learn and adapt partial representations as information unfolds, but studies on incremental approaches have focused on non-incremental approaches. |
| Approach: | They build incremental constituent parsers that adhere to a strong definition of incrementality across languages and compare them against non-incremental and partially incremental models. |
| Outcome: | The proposed models are compared against non-incremental and partially incremental models. |
Sequence Labeling Parsing by Learning across Representations (P19-1)
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| Challenge: | Constituency and dependency parsing are the main abstractions for representing syntactic structure of sentences . constituency parsers are considered disjointed tasks, and their improvements have been obtained separately. |
| Approach: | They propose to add auxiliary loss to constituency parsing paradigms and explore a model that parses both paradigms at no cost. |
| Outcome: | The proposed model outperforms single-task models by 1.05 F1 points and 0.62 UAS points for constituency parsing and dependency parsers. |
Hierarchical Bracketing Encodings Work for Dependency Graphs (2025.emnlp-main)
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| Challenge: | Sequence labeling (SL) is a simple yet effective paradigm for a wide range of natural language problems. |
| Approach: | They propose a new bracketing approach for dependency graph parsing that encodes graphs as sequences and n tagging actions. |
| Outcome: | The proposed approach significantly reduces label space while preserving structural information. |
Hierarchical Bracketing Encodings for Dependency Parsing as Tagging (2025.acl-long)
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| Challenge: | Existing encodings for dependency parsing use suboptimal number of labels and a limited number of symbols. |
| Approach: | They propose a family of encodings for sequence labeling dependency parsing based on hierarchical bracketing . they propose an optimal hierarchically bracketing which minimizes the number of symbols used and encodes projective trees using only 12 distinct labels . |
| Outcome: | The proposed encodings yield competitive accuracy on a diverse set of treebanks. |
Nested Named Entity Recognition as Single-Pass Sequence Labeling (2025.findings-emnlp)
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| Challenge: | nested named entity recognition is a sequence labeling task that can be trained using any off-the-shelf sequence labelling library. |
| Approach: | They use prior work that linearizes constituency structures to create a nested named entity recognition task. |
| Outcome: | The proposed method reduces the complexity of the predicted nested entity recognition problem to a simple token classification task. |