Coordinate Constructions in English Enhanced Universal Dependencies:
Analysis and Computational Modeling
- URL: http://arxiv.org/abs/2103.08955v1
- Date: Tue, 16 Mar 2021 10:24:27 GMT
- Title: Coordinate Constructions in English Enhanced Universal Dependencies:
Analysis and Computational Modeling
- Authors: Stefan Gr\"unewald, Prisca Piccirilli, Annemarie Friedrich
- Abstract summary: We address the representation of coordinate constructions in Enhanced Universal Dependencies (UD)
We create a large-scale dataset of manually edited syntax graphs.
We identify several systematic errors in the original data, and propose to also propagate adjuncts.
- Score: 1.9950682531209154
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we address the representation of coordinate constructions in
Enhanced Universal Dependencies (UD), where relevant dependency links are
propagated from conjunction heads to other conjuncts. English treebanks for
enhanced UD have been created from gold basic dependencies using a heuristic
rule-based converter, which propagates only core arguments. With the aim of
determining which set of links should be propagated from a semantic
perspective, we create a large-scale dataset of manually edited syntax graphs.
We identify several systematic errors in the original data, and propose to also
propagate adjuncts. We observe high inter-annotator agreement for this semantic
annotation task. Using our new manually verified dataset, we perform the first
principled comparison of rule-based and (partially novel) machine-learning
based methods for conjunction propagation for English. We show that learning
propagation rules is more effective than hand-designing heuristic rules. When
using automatic parses, our neural graph-parser based edge predictor
outperforms the currently predominant pipelinesusing a basic-layer tree parser
plus converters.
Related papers
- Unleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models [31.443478448031886]
RoSE (Relation-oriented Semantic Edge-decomposition) is a novel framework that decomposes the graph structure by analyzing raw text attributes.
Our framework significantly enhances node classification performance across various datasets, with improvements of up to 16% on the Wisconsin dataset.
arXiv Detail & Related papers (2024-05-28T20:54:47Z) - Data Augmentation for Machine Translation via Dependency Subtree
Swapping [0.0]
We present a generic framework for data augmentation via dependency subtree swapping.
We extract corresponding subtrees from the dependency parse trees of the source and target sentences and swap these across bisentences to create augmented samples.
We conduct resource-constrained experiments on 4 language pairs in both directions using the IWSLT text translation datasets and the Hunglish2 corpus.
arXiv Detail & Related papers (2023-07-13T19:00:26Z) - Scalable Learning of Latent Language Structure With Logical Offline
Cycle Consistency [71.42261918225773]
Conceptually, LOCCO can be viewed as a form of self-learning where the semantic being trained is used to generate annotations for unlabeled text.
As an added bonus, the annotations produced by LOCCO can be trivially repurposed to train a neural text generation model.
arXiv Detail & Related papers (2023-05-31T16:47:20Z) - Proton: Probing Schema Linking Information from Pre-trained Language
Models for Text-to-SQL Parsing [66.55478402233399]
We propose a framework to elicit relational structures via a probing procedure based on Poincar'e distance metric.
Compared with commonly-used rule-based methods for schema linking, we found that probing relations can robustly capture semantic correspondences.
Our framework sets new state-of-the-art performance on three benchmarks.
arXiv Detail & Related papers (2022-06-28T14:05:25Z) - Incorporating Constituent Syntax for Coreference Resolution [50.71868417008133]
We propose a graph-based method to incorporate constituent syntactic structures.
We also explore to utilise higher-order neighbourhood information to encode rich structures in constituent trees.
Experiments on the English and Chinese portions of OntoNotes 5.0 benchmark show that our proposed model either beats a strong baseline or achieves new state-of-the-art performance.
arXiv Detail & Related papers (2022-02-22T07:40:42Z) - GN-Transformer: Fusing Sequence and Graph Representation for Improved
Code Summarization [0.0]
We propose a novel method, GN-Transformer, to learn end-to-end on a fused sequence and graph modality.
The proposed methods achieve state-of-the-art performance in two code summarization datasets and across three automatic code summarization metrics.
arXiv Detail & Related papers (2021-11-17T02:51:37Z) - A Syntax-Guided Grammatical Error Correction Model with Dependency Tree
Correction [83.14159143179269]
Grammatical Error Correction (GEC) is a task of detecting and correcting grammatical errors in sentences.
We propose a syntax-guided GEC model (SG-GEC) which adopts the graph attention mechanism to utilize the syntactic knowledge of dependency trees.
We evaluate our model on public benchmarks of GEC task and it achieves competitive results.
arXiv Detail & Related papers (2021-11-05T07:07:48Z) - Learning compositional structures for semantic graph parsing [81.41592892863979]
We show how AM dependency parsing can be trained directly on a neural latent-variable model.
Our model picks up on several linguistic phenomena on its own and achieves comparable accuracy to supervised training.
arXiv Detail & Related papers (2021-06-08T14:20:07Z) - GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and
Event Extraction [107.8262586956778]
We introduce graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic sentence representations.
GCNs struggle to model words with long-range dependencies or are not directly connected in the dependency tree.
We propose to utilize the self-attention mechanism to learn the dependencies between words with different syntactic distances.
arXiv Detail & Related papers (2020-10-06T20:30:35Z) - Transition-based Semantic Dependency Parsing with Pointer Networks [0.34376560669160383]
We propose a transition system that can straightforwardly produce labelled directed acyclic graphs and perform semantic dependency parsing.
We enhance our approach with deep contextualized word embeddings extracted from BERT.
The resulting system not only outperforms all existing transition-based models, but also matches the best fully-supervised accuracy to date on SemEval 2015 18 English datasets.
arXiv Detail & Related papers (2020-05-27T13:18:27Z) - Transition-Based Dependency Parsing using Perceptron Learner [34.59241394911966]
We tackle transition-based dependency parsing using a Perceptron Learner.
Our proposed model, which adds more relevant features to the Perceptron Learner, outperforms a baseline arc-standard.
arXiv Detail & Related papers (2020-01-22T20:58:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.