Papers with dependency parser
Spa: On the Sparsity of Virtual Adversarial Training for Dependency Parsing (2022.findings-aacl)
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| Challenge: | Virtual adversarial training (VAT) is a powerful approach to improving robustness and performance, leveraging both labeled and unlabeled data to compensate for the scarcity of labeles. |
| Approach: | They propose a Sparse Parse Adjustment algorithm which combines VAT and a graph-based dependency parsing model in an exact computational manner and enhances the dependency parsed with controllable and adjustable sparsity. |
| Outcome: | Empirical results show that the proposed algorithm outperforms other methods without sparsity regularization. |
A Simple and Effective Dependency Parser for Telugu (2020.acl-srw)
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| Challenge: | Existing dependency parsers for Telugu use hand-crafted features based on linguistic information like part-of-speech and morphology which are expensive to annotate. |
| Approach: | They propose to replace linguistic feature templates with a minimal feature function for Telugu . they train a BERT model on the Telugus Wikipedia data and use contextual vector representations to train the parser. |
| Outcome: | The proposed parser achieves state-of-the-art for Telugu using contextual vector representations . the proposed model trains on the Telugus Wikipedia data and trains with a greedy transition based approach . |
Hexatagging: Projective Dependency Parsing as Tagging (2023.acl-short)
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| Challenge: | Using a pretrained language model, we can train language models on increasingly large amounts of data. |
| Approach: | They propose a dependency parser that constructs dependency trees by tagging words with elements from a finite set of possible tags. |
| Outcome: | The proposed approach achieves state-of-the-art performance of 96.4 LAS and 97.4 UAS on the Penn Treebank test set. |
Dependency Parsing for Spoken Dialog Systems (D19-1)
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| Challenge: | Dependency parsing of conversational input can help to understand dialogs . currently available annotation schemes do not adapt well to spoken human-machine dialogs. |
| Approach: | They propose an annotation scheme that extends Universal Dependencies guidelines to spoken dialogs. |
| Outcome: | The proposed scheme disambiguates relationships between entities extracted from dialogs . it is better than existing models on public datasets and fine-tuned on ConvBank data . |
Diversity-Aware Batch Active Learning for Dependency Parsing (2021.naacl-main)
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| Challenge: | a high annotation cost for dependency parsers is a challenge . batch active learning (AL) is based on batch mode, which is more efficient for annotators to label in bulk. |
| Approach: | They propose to reduce the number of labeled examples needed to train a strong dependency parser using batch active learning. |
| Outcome: | The proposed approach improves on an English newswire corpus by enforcing diversity in the sampled batches. |
A Survey of Unsupervised Dependency Parsing (2020.coling-main)
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| Challenge: | Syntactic dependency parsing is an important task in natural language processing . unsupervised learning of dependency parses requires training sentences to be manually annotated with their correct parse trees. |
| Approach: | They propose to survey existing approaches to unsupervised dependency parsing . they identify two major classes of approaches and discuss recent trends . |
| Outcome: | The proposed methods can be used in semantic parsing, machine translation, relation extraction, and many other tasks. |
Relation Extraction with Word Graphs from N-grams (2021.emnlp-main)
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| Challenge: | Recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to improve performance. |
| Approach: | They propose to use a graph convolutional network to build a context graph without dependency parsers. |
| Outcome: | The proposed approach improves neural RE methods without dependency parsers on English benchmark datasets. |
Quantifying training challenges of dependency parsers (C18-1)
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| Challenge: | a new metric is introduced to evaluate the difficulty to learn a given class of dependencies . a series of systematic computations using that metric have revealed interesting properties of the 3 considered parsing algorithms . |
| Approach: | They introduce a new metric to evaluate the difficulty to learn a given class of dependencies . they use it to characterize the information conveyed by cross-lingual parsers . |
| Outcome: | The proposed metric reveals the kind of dependencies that require high effort during training . it also shows that cross-lingual parsers can provide better quality information . |
Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl (L18-1)
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| Challenge: | DepCC is the largest-to-date linguistically analyzed corpus in English . large corpora are essential for the modern data-driven approaches to natural language processing . |
| Approach: | They present a large-to-date linguistically analyzed corpus in English with 365 million documents . they build an index of all sentences and their linguistic meta-data enabling quick search across the corpus . |
| Outcome: | The proposed model outperforms state-of-the-art models on smaller corpora on the SimVerb3500 dataset. |
The Morpho-syntactic Annotation of Animacy for a Dependency Parser (L18-1)
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| Challenge: | Animacy is a feature found in nouns such as 'gender', 'number' and 'case' that improves parser accuracy. |
| Approach: | They propose an annotation scheme and parser results for the animacy feature in Russian and Arabic, morphologically rich languages, using the universal dependency framework. |
| Outcome: | The proposed scheme and parser improve on the animacy feature in Russian and Arabic, and the results show that the feature is more accurate than other features found in nouns, namely, 'gender', , and 'number' |
Compositional Generalization in Dependency Parsing (2022.acl-long)
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| Challenge: | Compositionality is the ability to combine familiar units like words into novel phrases and sentences. |
| Approach: | They introduce a set of dependency parses for Compositional Freebase Queries (CFQ) they analyze the behaviour of a state-of-the-art dependency parser on the CFQ dataset . |
| Outcome: | The proposed dependency parser performs lower on the most challenging splits with the highest compound divergence. |
Multi-Layer Pseudo-Siamese Biaffine Model for Dependency Parsing (2022.coling-1)
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| Challenge: | Existing work only uses biaffine method at the end of the dependency parser as a scorer, and its application in multi-layer form is ignored. |
| Approach: | They propose a multi-layer pseudo-Siamese biaffine model for neural dependency parsing that uses biaffin method as a scorer and a biaffin module to construct arc weight matrix. |
| Outcome: | The proposed model achieves state-of-the-art on PTB, CTB, and UD datasets with low efficiency loss. |
NEUROSTRUCTURAL DECODING: Neural Text Generation with Structural Constraints (2023.acl-long)
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| Challenge: | Current approaches for conditional text generation focus on lexical constraints, but lack syntactic constraints to support complex semantic constraints. |
| Approach: | They propose a decoding algorithm that incorporates syntactic constraints to improve the quality of the generated text. |
| Outcome: | The proposed method improves on three different language generation tasks and shows improved lexical and syntactic metrics. |
A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling (D19-1)
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| Challenge: | Semantic role labeling (SRL) aims to identify the predicate-argument structure of a sentence. |
| Approach: | They propose to use a unified span-based model for Chinese SRL as a strong baseline. |
| Outcome: | The proposed framework achieves state-of-the-art 87.54 and 88.5 F1 scores on the Chinese Proposition Bank and CoNLL-2009 datasets. |
Towards the Conversion of National Corpus of Polish to Universal Dependencies (2020.lrec-1)
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| Challenge: | a paper aims at enriching the manually annotated part of National Corpus of Polish with a syntactic layer. |
| Approach: | They enrich manually annotated part of Polish National Corpus with a syntactic layer and a UD dependency graph. |
| Outcome: | The proposed model outperforms a model trained on a smaller set of gold-standard trees in predicting part-of-speech tags, morphological features, lemmata and labelled dependency trees. |
Multipath parsing in the brain (2024.acl-long)
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| Challenge: | a major unsolved problem in computational psycholinguistics is determining whether human comprehension considers a single analysis path 1 at a time. |
| Approach: | They compare syntactic surprisal from a state-of-the-art dependency parser with fMRI data . they find evidence for multipath parsing in English and Chinese data based on fm data a major unsolved problem in computational psycholinguistics is determining whether human sentence comprehension considers a single analysis path 1 at a time . |
| Outcome: | The proposed model shows that human parsing is multipath, with a higher r2 increase for multipath surprisal than single-path surpresal. |
Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis (2022.lrec-1)
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| Challenge: | Social media data such as Twitter messages pose a particular challenge to NLP systems because of their short, noisy nature. |
| Approach: | They create a Twitter-based NER corpus and train Tweet NLP models on it . they annotate named entities in TB2 using Amazon Mechanical Turk . |
| Outcome: | The proposed model outperforms existing models on Twitter and other social media platforms. |
A Root of a Problem: Optimizing Single-Root Dependency Parsing (2021.emnlp-main)
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| Challenge: | Graph-based dependency parsers can be improved without compromising on accuracy or accuracy. |
| Approach: | They propose two approaches to single-root dependency parsing that yield speed ups . they show that one approach is fully correct and finds the optimal dependency tree . |
| Outcome: | The proposed approach finds the optimal dependency tree without loss of accuracy or optimality. |