Papers with syntactic parsing
Unsupervised Natural Language Parsing (Introductory Tutorial) (2021.eacl-tutorials)
Copied to clipboard
| Challenge: | Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations. |
| Approach: | This tutorial will introduce what unsupervised parsing does and how it can be useful for and beyond syntactic parse. |
| Outcome: | This paper will provide an overview of major approaches to unsupervised parsing and analyze their strengths and weaknesses. |
N-LTP: An Open-source Neural Language Technology Platform for Chinese (2021.emnlp-demo)
Copied to clipboard
| Challenge: | Existing tools that teach an independent model for each task are not supported in Chinese. |
| Approach: | They propose an open-source neural language platform supporting six Chinese NLP tasks . source code, documentation, and pre-trained models are available at https://github.com/hit-SCIR/ltp . |
| Outcome: | The proposed platform supports six Chinese NLP tasks. |
Syntax in End-to-End Natural Language Processing (2021.emnlp-tutorials)
Copied to clipboard
| Challenge: | tutorial focuses on syntactic parsing and syntax in end-to-end natural language processing (NLP) tasks. |
| Approach: | tutorial will introduce syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks. |
| Outcome: | This tutorial will introduce the background and the latest progress of syntactic parsing and SRL/NMT. |
Deep Bidirectional Transformers for Relation Extraction without Supervision (D19-61)
Copied to clipboard
| Challenge: | Existing frameworks for relation extraction use distant supervision instead of annotated data. |
| Approach: | They propose a framework to deal with relation extraction tasks without supervision . they use syntactic parsing and pre-trained word embeddings to extract relations . |
| Outcome: | The proposed framework outperforms baselines on four biomedical datasets and achieves slightly worse results than the state-of-the-art in three out of four data sets. |
Parsing linearizations appreciate PoS tags - but some are fussy about errors (2022.aacl-short)
Copied to clipboard
| 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. |
A Stylometry Toolkit for Latin Literature (D19-3)
Copied to clipboard
| Challenge: | a stylometric toolkit for analysis of Latin literary texts is available for free at www.qcrit.org/stylometry. |
| Approach: | They propose a stylometric toolkit for analysis of Latin literary texts which generates data for a diverse range of literary features and has an intuitive point-and-click interface. |
| Outcome: | The proposed toolkit generates data for a diverse range of literary features and has an intuitive point-and-click interface. |
BanSuite: A Unified Toolkit and Software Platform for Low-Resource NLP in Bangla (2026.eacl-demo)
Copied to clipboard
Md. Abu Sayed, Faisal Ahamed Khan, Jannatul Ferdous Tuli, Nabeel Mohammed, Mohammad Ruhul Amin, Mohammad Mamun Or Rashid
| Challenge: | Existing efforts to improve Bangla's NLP performance have focused on isolated tasks such as Part-of-Speech tagging and Named Entity Recognition (NER) but comprehensive, integrated systems for core NLP tasks such Shallow Parsing and Dependency Parser are largely absent. |
| Approach: | They propose to integrate a large-scale, manually annotated Bangla Treebank with high-quality pretrained models for POS tagging, NER, shallow parsing, and dependency parse. |
| Outcome: | The proposed system achieves strong in-domain baseline performance while maintaining high efficiency in resource usage. |
Neural Open Information Extraction (P18-2)
Copied to clipboard
| Challenge: | Existing Open IE systems are built on hand-crafted patterns from syntactic parsing, yet they face errors in propagation and compounding at each stage. |
| Approach: | They propose a neural Open IE approach with an encoder-decoder framework . they propose to learn highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open ie system. |
| Outcome: | The proposed approach outperforms baseline methods while maintaining comparable computational efficiency. |
On the Difficulty of Translating Free-Order Case-Marking Languages (2021.tacl-1)
Copied to clipboard
| Challenge: | Free-order case-marking languages are more difficult to model than fixed-order languages . however, in medium- and low-resource settings, the overall NMT quality of fixed-or-fixed-order language pairs remains unmatched. |
| Approach: | They investigate whether certain languages are harder to model than others by adding case marking to their models. |
| Outcome: | The proposed models show that word order flexibility in the source language leads to a small loss of NMT quality even though the core verb arguments become impossible to disambiguate in sentences without semantic cues. |
A Truly Joint Neural Architecture for Segmentation and Parsing (2024.eacl-long)
Copied to clipboard
| Challenge: | Contemporary multilingual dependency parsers can parse a diverse set of languages, but performance is lower for Morphologically Rich Languages. |
| Approach: | They propose a joint neural architecture where a lattice-based representation is provided to an arc-factored model and solves the morphological segmentation and syntactic parsing tasks at once. |
| Outcome: | The proposed architecture is language-agnostic and language-based to improve on Hebrew . it shows that the proposed model can parse morphological segmentation and syntactic parsing tasks at once. |
Neural Machine Translation for Bilingually Scarce Scenarios: a Deep Multi-Task Learning Approach (N18-1)
Copied to clipboard
| Challenge: | Neural machine translation requires large amount of parallel training text to learn a reasonable quality translation model. |
| Approach: | They propose a multi-task learning approach that leverages monolingual linguistic resources in the source side of a machine translation task. |
| Outcome: | The proposed approach is effective on three translation tasks: English-to-French, English- to-Farsi, and English-à-Vietnamese. |
Towards Making a Dependency Parser See (D19-1)
Copied to clipboard
| 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. |
Syntax-guided Contrastive Learning for Pre-trained Language Model (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing studies rely on additional syntax-driven attention components to enhance the transformer, which require more parameters and additional syntactic parsing in downstream tasks. |
| Approach: | They propose a syntax-guided contrastive learning method which does not change the transformer architecture and does not alter the transformer structure. |
| Outcome: | The proposed method achieves consistent improvements in a variety of tasks including grammatical error detection, entity tasks, structural probing and GLUE. |
Improving AMR Parsing with Sequence-to-Sequence Pre-training (2020.emnlp-main)
Copied to clipboard
| Challenge: | Abstract meaning representation (AMR) parsing is limited by the size of curated datasets. |
| Approach: | They propose a seq2seq pre-training approach to build pre-trained models on three relevant tasks. |
| Outcome: | The proposed model improves performance on three relevant tasks while maintaining the response of pre-trained models. |
Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations (2020.coling-main)
Copied to clipboard
| Challenge: | Existing dependency parsing models for Arabic use complementary annotations, CATiB and UD treebanks, and partially created trees for one annotation are also available to the other as features for the score function. |
| Approach: | They propose to use Arabic dependency annotations to parse projective dependency trees using CATiB and UD treebanks. |
| Outcome: | The proposed model gives 9.9% error reduction on CATiB and 6.1% on UD compared to a strong baseline and ablation tests show that the main contribution is given by sharing tree representation between tasks, and not simply sharing biLSTM layers as is often performed in NLP multitask systems. |
Pre- and In-Parsing Models for Neural Empty Category Detection (P18-1)
Copied to clipboard
| Challenge: | Existing studies on empty category detection have shown positive effects on syntactic parsing . empty categories are used to indicate long-distance dependencies, discontinuous constituents, and certain dropped elements. |
| Approach: | They propose to use ECD to detect empty categories without syntactic analysis. |
| Outcome: | The proposed models outperform the prior state-of-the-art by significant margins. |
An Empirical Investigation of Error Types in Vietnamese Parsing (C18-1)
Copied to clipboard
| Challenge: | Syntactic parsing improves the quality of natural language processing tasks. |
| Approach: | They evaluated Vietnamese Treebank model to find most suitable parsing method . they found that Vietnamese parsers produced limited training data and POS errors . |
| Outcome: | The proposed method improves the parsing quality in Vietnamese . the results highlight three possible sources of parser errors . |
Structural generalization is hard for sequence-to-sequence models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Sequence-to-sequence models have been successful across many NLP tasks, but they have low generalization accuracy . |
| Approach: | They propose to use linguistic knowledge to overcome generalization limitations of seq2seq models . they show that human beings are able to understand and produce linguistic structures they have never observed before . |
| Outcome: | The proposed models can overcome this limitation by having linguistic knowledge built in. |
Graph-Based Decoding for Task Oriented Semantic Parsing (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing paradigms for semantic parsing are sequence-to-sequence and AMR parsers. |
| Approach: | They propose to formulate parsing as a sequence-to-sequence task using graph-based decoding techniques developed for syntactic parsers. |
| Outcome: | The proposed approach is competitive with sequence decoders on the standard setting and offers significant improvements in data efficiency and data availability. |
Self-Correction Makes LLMs Better Parsers (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have achieved remarkable success across various natural language processing tasks, but they still face challenges in performing fundamental NLP tasks, such as syntactic parsing. |
| Approach: | They propose a method that leverages grammar rules from existing treebanks to guide LLMs in correcting previous errors. |
| Outcome: | The proposed method significantly improves performance on in-domain and cross-domain datasets. |
Large Language Models Are No Longer Shallow Parsers (2024.acl-long)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have reshaped the field of natural language processing (NLP) however, fundamental NLP tasks that involve linguistic analysis still play essential roles in the field. |
| Approach: | They propose to use constituency parsing to improve performance of LLMs on deep syntactic parse trees to prompt LLM chunking, filter out low-quality chunks and add remaining chunks to prompts to instruct LLM for parser. |
| Outcome: | The proposed approach improves LLMs' performance on constituency parsing on English and Chinese benchmark datasets. |
Parsing All: Syntax and Semantics, Dependencies and Spans (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Syntactic and semantic structures are key linguistic contextual clues, but few studies have explored how they can be used to improve syntactical parsing. |
| Approach: | They propose a syntactic and semantic parsing model which integrates syntaktic information in the encoder of neural network and benefits from two representation formalisms in a uniform way. |
| Outcome: | The proposed model achieves state-of-the-art or competitive results on both span and dependency representations and on Penn Treebank. |
Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data (D18-1)
Copied to clipboard
| Challenge: | a learner language (interlanguage) is an idiolect developed by a learning of a second or foreign language. |
| Approach: | They propose to use semantic role labeling as a case task to parse interlanguages . they then evaluate three off-the-shelf SRL systems to gauge how successful they are . |
| Outcome: | The proposed model achieves an F-score of 72.06, a 2.02 point improvement over the baseline. |
An In-depth Study on Internal Structure of Chinese Words (2021.acl-long)
Copied to clipboard
Chen Gong, Saihao Huang, Houquan Zhou, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan
| Challenge: | Unlike English letters, Chinese characters have rich and specific meanings. |
| Approach: | They propose to model Chinese words' internal structures as dependency trees with 11 labels for distinguishing syntactic relationships. |
| Outcome: | The proposed model of Chinese word-internal structures shows it can be used to parse sentences . it shows that the model can be applied to a sentence-level task with a competitive dependency parser. |
Tracing Syntactic Change in the Scientific Genre: Two Universal Dependency-parsed Diachronic Corpora of Scientific English and German (2022.lrec-1)
Copied to clipboard
| 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 . |
Multitask Learning for Cross-Lingual Transfer of Broad-coverage Semantic Dependencies (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for developing broad-coverage semantic dependency parsers for languages without semantically annotated data are limited to English, Czech and Chinese. |
| Approach: | They propose a multitask learning framework coupled with annotation projection to build broad-coverage semantic dependency parsers for languages without annotated resources. |
| Outcome: | The proposed model improves labeled F1 score on multitask tasks from English to Czech compared to baseline models . |
Survey on Thai NLP Language Resources and Tools (2022.lrec-1)
Copied to clipboard
| Challenge: | Thai language is one of the under-resourced languages in the NLP domain, although it is spoken by approximately 70 million people globally. |
| Approach: | They propose to use Thai language as an example to understand how NLP works and how it can be applied to Thai language. |
| Outcome: | The results show that Thai NLP research has progressed over the past three decades, especially on upstream tasks such as tokenisation, but research on downstream tasks such syntactic parsing and semantic analysis is still limited. |
Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis (2022.lrec-1)
Copied to clipboard
| 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. |
LCGbank: A Corpus of Syntactic Analyses Based on Proof Nets (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent studies have focused on statistical syntactic parsing with proof nets . however, there has been a paucity of corpora in formalisms for which proof net is applicable . |
| Approach: | They propose a corpus of syntactic analyses based on Lambek categorial grammar . they leverage the relationship between LCG and CCG to address this problem . |
| Outcome: | The proposed method exploits the relationship between LCG and CCG to build an English-language corpus of syntactic analyses based on proof nets . the results suggest that the proposed method is weakly context-free equivalent and NP-complete . |