Papers with parsing accuracy
Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation (2022.tacl-1)
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| Challenge: | Discourse parsing accuracy degrades significantly on out-of-domain text. |
| Approach: | They propose to use bootstrapping methods to adapt modern discourse dependency parsers to out-of-domain text without additional human supervision. |
| Outcome: | The proposed methods are significantly and consistently effective for unsupervised domain adaptation of discourse dependency parsing, but the low coverage of accurately predicted pseudo labels is a bottleneck for further improvement. |
Sanskrit Voyager: Unified Web Platform for Interactive Reading and Linguistic Analysis of Sanskrit Texts (2025.emnlp-demos)
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| Challenge: | Sanskrit Voyager enables users to search for words and phrases as they actually appear in texts . evaluation shows over 92% parsing accuracy on complex compounds compared to BuddhaNexus . |
| Approach: | Sanskrit Voyager is a web application for searching, reading and analyzing the Sanskrt literary corpus. |
| Outcome: | Sanskrit Voyager is a web application for searching, reading, and analyzing the Sanskrt literary corpus. |
Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations (D19-61)
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| Challenge: | Pretrained sentence representations have set the new state of the art in many language understanding tasks. |
| Approach: | They propose to use a multilingual corpus to train deep bidirectional sentence representations that are fully lexicalized to allow for the development of an unsupervised universal dependency parser. |
| Outcome: | The proposed approach outperforms the best CoNLL 2018 systems in all of the shared task’s six truly low-resource languages while using a single system. |
Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing (D18-1)
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| Challenge: | Neural machine translation (NMT) models are based on sequential decoding or serialisation of structured data into sequence. |
| Approach: | They propose a model that combines sequential encoder with tree-structured decoding augmented with a syntax-aware attention model. |
| Outcome: | The proposed model produces fluent translations with better reordering than previous models. |
Neural Syntactic Generative Models with Exact Marginalization (N18-1)
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| Challenge: | Recent models have added structure to recurrent neural networks at the cost of giving up exact inference, or using soft structure instead of latent variables. |
| Approach: | They propose a syntactic generative model with exact marginalization that supports dependency parsing and language modeling. |
| Outcome: | The proposed models achieve state-of-the-art for supervised dependency parsing and language modeling. |
Syntactic Nuclei in Dependency Parsing – A Multilingual Exploration (2021.eacl-main)
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| Challenge: | Existing models for syntactic dependency parsing assume words are elementary units that enter into dependency relations. |
| Approach: | They propose to use composition functions to make a transition-based dependency parser aware of the notion of nucleus. |
| Outcome: | The proposed concept of nucleus gives small but significant improvements in parsing accuracy on 12 languages. |
Syntactic Substitutability as Unsupervised Dependency Syntax (2023.emnlp-main)
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| Challenge: | Syntax is a latent hierarchical structure which underpins the robust and compositional nature of human language. |
| Approach: | They propose a method to induce syntactic dependencies theory-agnostically by substituting words from the same category for words at either end of a dependency. |
| Outcome: | The proposed method achieves 79.5% recall on long-distance subject-verb agreement constructions compared to 8.9% using a previous method. |
Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction (P19-1)
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| Challenge: | In unsupervised grammar induction, data likelihood is only weakly correlated with parsing accuracy, especially at convergence after multiple runs. |
| Approach: | They propose to use VAS instead of data likelihood to find better grammars by examining linguistically-motivated constraints related to syntax. |
| Outcome: | The proposed model is better suited for word order typology classification than data likelihood. |
Supervised Treebank Conversion: Data and Approaches (P18-1)
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| Challenge: | Existing work on treebank conversion focuses on unsupervised treebanks . lack of manually labeled data means that sentences have two syntactic trees at the same time. |
| Approach: | They propose supervised treebank conversion using bi-tree aligned sentences . they propose two conversion approaches based on state-of-the-art deep biaffine parser . |
| Outcome: | The proposed method outperforms the state-of-the-art deep biaffine parser on the English WSJ dataset by 0.97 (93.76% -92.79%) |
A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing (2020.coling-main)
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| Challenge: | Existing methods for text-to-SQL semantic parsing require strict structured prediction due to its application scenario where the output SQL will be sent to an executor program directly. |
| Approach: | They propose to use schema linking and structural linking to link NL to the database schema. |
| Outcome: | The proposed method shows significant gains on the Spider dataset. |
Deep Inside-outside Recursive Autoencoder with All-span Objective (2020.coling-main)
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| Challenge: | Existing neural approaches for constituency parsing are limited for low-resource languages and domains. |
| Approach: | They extend the training objective of DIORA by making use of all spans instead of only leaf-level spans. |
| Outcome: | The proposed model improves on two languages and provides better parsing accuracy than the original model. |
Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations (2020.coling-main)
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| Challenge: | Recent advances in deep neural network models have improved parsing performance on in-domain texts . however, the problem is to improve performance on out-of-domain text data when there is only a small-scale out-domain labeled data. |
| Approach: | They propose to use adversarial learning and fine-tuning BERT to improve contextualized word representations on out-of-domain texts. |
| Outcome: | The proposed models achieve consistent improvement and fine-tune BERT processes boost parsing accuracy by a large margin. |
A Transition-based Method for Complex Question Understanding (2022.coling-1)
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| Challenge: | Existing work on complex question understanding does not model intermediate states and does not provide step-wise information. |
| Approach: | They propose a transition-based method where a decider predicts a sequence of actions to build the graph node-by-node. |
| Outcome: | The proposed method parses complex questions to QDMR using atomic operators . it has transparent and human-readable intermediate results, showing improved interpretability . |
Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing (2021.acl-long)
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| Challenge: | Discourse dependency parsing is a task that requires a large amount of training data, but there is little research on it. |
| Approach: | They propose to adapt unsupervised syntactic dependency parsing methods for unsupervised discourse dependency parses using unlabeled training data. |
| Outcome: | The proposed methods outperform existing methods in semi-supervised and supervised settings and outperformed existing methods. |
Representation Alignment and Adversarial Networks for Cross-lingual Dependency Parsing (2024.findings-emnlp)
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| Challenge: | Pre-trained language models have improved dependency parsing accuracy in resource-rich languages . however, the accuracy drops sharply when the model is transferred to low-resource language . |
| Approach: | They propose a representation alignment and adversarial model to filter out useful knowledge from rich-resource language and ignore useless ones. |
| Outcome: | The proposed model outperforms baseline models on the benchmark datasets by 1.37 LAS and 1.34 UAS. |
Tracing Syntactic Change in the Scientific Genre: Two Universal Dependency-parsed Diachronic Corpora of Scientific English and German (2022.lrec-1)
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| Challenge: | a recent study has focused on the syntactic development of scientific discourse in English and German. |
| Approach: | They present two comparable diachronic corpora of scientific English and German from the Late Modern Period (17th c.–19th d.) annotated with Universal Dependencies. |
| Outcome: | The presented corpora are comparable to existing studies on grammatical change in English and German . the results show that the pre-processing steps significantly improve parsing accuracy . |
Syntax and Semantics in a Treebank for Esperanto (2020.lrec-1)
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| Challenge: | In this paper we describe syntactic and semantic aspects of Arbobanko, a treebank for the artificial language Esperanto, as well as tools and methods used in the production of the treebank. |
| Approach: | They describe syntactic and semantic aspects of Arbobanko, a treebank for the artificial language Esperanto, and evaluate tools and methods used in the production of the treebank. |
| Outcome: | The proposed treebank is enriched with lexical-semantic layer covering named entities, semantic type ontology for nouns and adjectives and framenet-inspired semantic classification of verbs. |