PARSERS

Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement (2021.tacl-1)

Challenge:	a recent study shows transformer-based parsers struggle with disambiguating intents/slots and producing syntactically valid frames.
Approach:	They propose to use seq2seq transformers to map textual utterances to semantic frames . they propose to model transformer-based parsers across monolingual and multilingual settings .
Outcome:	The proposed parsers struggle with disambiguating intents/slots and produce syntactically valid frames.

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Alireza Mohammadshahi, James Henderson

Evaluating Universal Dependency Parser Recovery of Predicate Argument Structure via CompChain Analysis (2021.starsem-1)

Challenge:	RNGTr is a non-recursive Graph-to-Graph Transformer for iterative refinement of graphs . it can improve the accuracy of initial parsers on 13 languages .
Approach:	They propose a recursive non-autoregressive Graph-to-Graph Transformer architecture for iterative refinement of arbitrary graphs and apply it to syntactic dependency parsing.
Outcome:	The proposed architecture can improve state-of-the-art on 13 languages and the German CoNLL2009 corpus.

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Sagar Indurkhya, Beracah Yankama, Robert C. Berwick

Fast Domain Adaptation of Semantic Parsers via Paraphrase Attention (D19-61)

Challenge:	Compchains are a categorization of the hierarchy of predicate dependency relations present within a UD parse.
Approach:	They introduce compchains, a categorization of the hierarchy of predicate dependency relations present within a UD parse.
Outcome:	The proposed model performs poorly on sentences with predicate-argument structure with more than one level of embedding.

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Avik Ray, Yilin Shen, Hongxia Jin

Challenges to Open-Domain Constituency Parsing (2022.findings-acl)

Challenge:	Semantic parsers are used to convert user’s natural language commands to executable logical form in intelligent personal agents. Labeled datasets required to train such parser are expensive to collect, and are never comprehensive.
Approach:	They propose to use a sequence-to-sequence/tree attention based attention-based sequence-based parsers which support fast near real time retraining.
Outcome:	The proposed parsers can maintain high accuracy and fast retraining time while leveraging paraphrases already present in the training dataset.

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Sen Yang, Leyang Cui, Ruoxi Ning, Di Wu, Yue Zhang

Challenge:	Existing findings on cross-domain constituency parsing are only made on a limited number of domains.
Approach:	They manually annotate a high-quality constituency treebank containing five domains and analyze challenges to open-domain constituency parsing using a set of linguistic features.
Outcome:	The proposed model significantly improves the performance of the proposed model on the domain-variant features.

The (Non-)Utility of Structural Features in BiLSTM-based Dependency Parsers (P19-1)

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Agnieszka Falenska, Jonas Kuhn

Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation (P19-1)

Challenge:	Existing non-neural dependency parsers benefit from information coming from structural features . however, their successors lack explicit information about the structural context .
Approach:	They propose to model the structural context of a biLSTM-based dependency parser with features built from partial subtrees.
Outcome:	The proposed models do not use any conventional structural features but capture implicitly the structural context.

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Masashi Yoshikawa, Hiroshi Noji, Koji Mineshima, Daisuke Bekki

Low-Resource NLP

Challenge:	Existing methods for Combinatory Categorial Grammar (CCG) parsing are limited to a specific parser architecture, making it non-trivial to apply to current parsers.
Approach:	They propose a domain adaptation method for Combinatory Categorial Grammar (CCG) they propose to generate CCG corpora using cheaper dependency trees.
Outcome:	The proposed method improves on speech conversation and math problems.

From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation (2020.coling-main)

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Patrick Huber, Giuseppe Carenini

Discontinuous Combinatory Constituency Parsing (2023.tacl-1)

Challenge:	Existing sentiment analysis models lack temporal information to capture semantics of long texts.
Approach:	They propose a framework to exploit task-related discourse structures for sentiment analysis.
Outcome:	The proposed framework improves the performance even beyond existing approaches based on human annotated data.

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Zhousi Chen, Mamoru Komachi

From Partial to Strictly Incremental Constituent Parsing (2024.eacl-short)

Challenge:	Discontinuous parsing is more challenging than continuous parsers because children can group with syntactic cousins in the sentence rather than its two adjacent neighbors.
Approach:	They extend a pair of combinator-based constituency parsers into a discontinuous pair . they use a swap action and biaffine attention to iteratively compose constituent vectors from word embeddings without any grammar constraints.
Outcome:	The proposed parsers achieve state-of-the-art discontinuous accuracy with a significant speed advantage over continuous parsing.

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Ana Ezquerro, Carlos Gómez-Rodríguez, David Vilares

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.

Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models (2021.findings-emnlp)

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Taeuk Kim, Bowen Li, Sang-goo Lee

SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features (2022.aacl-main)

Challenge:	Existing methods for extracting complete (binary) parses from pre-trained language models are expensive and time-consuming.
Approach:	They propose a chart-based method and an effective top-K ensemble technique to extractbinary parses from PLMs.
Outcome:	The proposed method can induce non-trivial parses for sentences from nine languages in an integrated and language-agnostic manner, and is robust to cross-lingual transfer.

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Juri Opitz, Anette Frank

Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring (P19-2)

Challenge:	Abstract Meaning Representation (S3BERT) embeddings are composed of explainable sub-embeddings that emphasize various sentence meaning features.
Approach:	They propose to induce Semantically Structured Sentence BERT embeddings (S3BERT) that emphasize various sentence meaning features.
Outcome:	The proposed model shows high correlation to human similarity ratings, but lacks interpretability.

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Farjana Sultana Mim, Naoya Inoue, Paul Reisert, Hiroki Ouchi, Kentaro Inui

Compositional Generalization in Multilingual Semantic Parsing over Wikidata (2022.tacl-1)

Challenge:	Existing document embedding approaches focus on capturing sequences of words in documents . however, some document classification and regression tasks need to consider discourse structure of text .
Approach:	They propose an unsupervised approach to capture discourse structure in terms of coherence and cohesion for document embedding that does not require expensive parsers or annotation.
Outcome:	The proposed method improves essay Organization scoring and Argument Strength scoring.

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Ruixiang Cui, Rahul Aralikatte, Heather Lent, Daniel Hershcovich

I Speak for the Árboles: Developing a Dependency Treebank for Spanish L2 and Heritage Speakers (2025.acl-srw)

Challenge:	Semantic parsers are mostly designed for and evaluated on English resources, such as CFQ.
Approach:	They propose a method for creating a multilingual, parallel question-query dataset . they analyze compositional generalization of parsers in Hebrew, Kannada, Chinese, and English .
Outcome:	The proposed method analyzes compositional generalization of parsers in Hebrew, Kannada, Chinese, and English.

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Emiliana Pulido, Robert Pugh, Zoey Liu

Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement (2025.naacl-short)

Challenge:	Existing dependency treebanks for learner writing are limited due to morphosyntactic features.
Approach:	They propose to use a dependency treebank for Spanish learner writing from the UC Davis COWSL2H corpus to incorporate lemmatization, POS tagging, and syntactic dependencies.
Outcome:	The proposed treebanks are openly accessible to motivate future development of learner-oriented language technologies.

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Nicolas Floquet, Joseph Le Roux, Nadi Tomeh, Thierry Charnois

Improving Coverage and Runtime Complexity for Exact Inference in Non-Projective Transition-Based Dependency Parsers (N18-2)

Challenge:	Existing graph-based dependency parsers use a standard two-pipeline approach that only scores arcs and labels .
Approach:	They propose a graph-based dependency parsing architecture that explicitly constructs vectors from which both arcs and labels are scored.
Outcome:	The proposed model outperforms state-of-the-art models on PTB and UD in accuracy and efficiency.

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Tianze Shi, Carlos Gómez-Rodríguez, Lillian Lee

Policy Gradient as a Proxy for Dynamic Oracles in Constituency Parsing (P18-2)

Challenge:	Non-projective dependency trees account for 12.59% of all training sentences in the annotated Universal Dependencies (UD) 2.1 data.
Approach:	They generalize Cohen et al.'s (2011) parser to a family of non-projective transition-based dependency parsers allowing polynomial-time exact inference.
Outcome:	The proposed system can be extended to include a variant that reduces time complexity to O(n6), improving over the known bounds in exact inference for non-projective transition-based parsing.

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Daniel Fried, Dan Klein

QUDSELECT: Selective Decoding for Questions Under Discussion Parsing (2024.emnlp-main)

Challenge:	Existing models for constituency parsing are transition based, decomposing production of each parse tree into a sequence of action decisions.
Approach:	They propose to use a policy gradient method to reduce exposure bias by allowing exploration during training.
Outcome:	The proposed method outperforms static oracle likelihood training on four constituency parsers in three languages on four parser-agnostic languages.

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Ashima Suvarna, Xiao Liu, Tanmay Parekh, Kai-Wei Chang, Nanyun Peng

Challenge:	Question Under Discussion (QUD) uses implicit questions to reveal discourse relationships between sentences.
Approach:	They propose a framework that selectively decodes the QUD dependency structures considering the QUC criteria.
Outcome:	The proposed framework outperforms the state-of-the-art baseline models by 9% in human evaluation and 4% in automatic evaluation.

Viable Dependency Parsing as Sequence Labeling (N19-1)

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Michalina Strzyz, David Vilares, Carlos Gómez-Rodríguez

Adaptive Structure Induction for Aspect-based Sentiment Analysis with Spectral Perspective (2023.findings-emnlp)

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.

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Hao Niu, Yun Xiong, Xiaosu Wang, Wenjing Yu, Yao Zhang, Zhonglei Guo

A Truly Joint Neural Architecture for Segmentation and Parsing (2024.eacl-long)

Challenge:	incorporating structure information can enhance the performance of aspect-based sentiment analysis.
Approach:	They propose to use pre-trained language models to induct latent structures from a spectrum perspective.
Outcome:	The proposed model shortens Aspects-sentiment Distance and improves structure induction ability.

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Danit Yshaayahu Levi, Reut Tsarfaty

Scene Graph Modification Based on Natural Language Commands (2020.findings-emnlp)

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.

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Xuanli He, Quan Hung Tran, Gholamreza Haffari, Walter Chang, Zhe Lin, Trung Bui, Franck Dernoncourt, Nhan Dam

Parsing Tweets into Universal Dependencies (N18-1)

Challenge:	Numerous parsing methods have been developed for a single sentence, while a typical human-computer interaction session or conversation is not singleturn.
Approach:	They propose to modify an existing scene graph given a new user's command by using graph-based sparse transformer and cross attention information fusion to improve performance.
Outcome:	The proposed models outperform previous systems adapted from the machine translation and graph generation literature and contribute to the research community.

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Yijia Liu, Yi Zhu, Wanxiang Che, Bing Qin, Nathan Schneider, Noah A. Smith

Hierarchical Pointer Net Parsing (D19-1)

Challenge:	a new tweet treebank for English is designed to analyze tweets with universal dependencies (UD).
Approach:	They extend the universal dependencies guidelines to include special constructions in tweets that affect tokenization, part-of-speech tagging, and labeled dependencies.
Outcome:	The proposed method outperforms state-of-the-art parsers on other treebanks in accuracy and speed.

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Linlin Liu, Xiang Lin, Shafiq Joty, Simeng Han, Lidong Bing

Challenge:	Existing approaches to parsing are greedy transition-based and globally optimized . however, the decision-making process is based on local information, causing error propagation to subsequent steps.
Approach:	They propose hierarchical pointer network parsers and apply them to dependency and sentence-level discourse parsing tasks.
Outcome:	The proposed method outperforms existing methods and sets new state-of-the-art methods on benchmark datasets.

Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing (2021.findings-acl)

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Qian Liu, Dejian Yang, Jiahui Zhang, Jiaqi Guo, Bin Zhou, Jian-Guang Lou

Cross-Lingual Abstract Meaning Representation Parsing (N18-1)

Challenge:	Recent years pretrained language models (PLMs) have shown their power on modeling language . however, few efforts have been made to explore grounding capabilities of PLMs .
Approach:	They propose to use pretrained language models to explore syntactic structures . they propose to combine their approach with an erasingthen-awakening approach . their results show that the approach can awaken latent grounding, which is understandable to humans .
Outcome:	Empirical studies show that the proposed approach can awaken latent grounding . it shows great potential to benefit downstream semantic parsing models, it says .

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Marco Damonte, Shay B. Cohen

Improving the Extraction of Supertags for Constituency Parsing with Linear Context-Free Rewriting Systems (2022.findings-emnlp)

Challenge:	Abstract Meaning Representation (AMR) research has focused on English . Qualitative analysis shows that the new parsers overcome structural differences between the languages.
Approach:	They propose to use an AMR parser for English and parallel corpora to learn AMR for Italian, Spanish, German and Chinese.
Outcome:	The proposed method overcomes structural differences between the target languages and requires no gold standard data.

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Thomas Ruprecht

Information Extraction & Text Mining

Challenge:	a new approach to parsing discontinuous constituency structures uses supertags to improve parsability . traditional approaches use grammar formalisms to model hierarchies of noncontiguous phrases . but supertags are still useful for analyzing these grammars and parsers .
Approach:	They propose to reformulate and parameterize extraction process for LCFRS supertags to improve parsing quality.
Outcome:	The proposed method improves the quality and speed of parsing with supertags over the previous method.

Sentences with Gapping: Parsing and Reconstructing Elided Predicates (N18-1)

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Sebastian Schuster, Joakim Nivre, Christopher D. Manning

Challenge:	Sentences with gapping lack an overt predicate to indicate the relation between two or more arguments.
Approach:	They propose two methods for parsing to a Universal Dependencies graph representation that explicitly encodes the elided material with additional nodes and edges.
Outcome:	The proposed methods reconstruct elided material from dependency trees with high accuracy when the parser correctly predicts the existence of a gap.

Towards Dynamic Computation Graphs via Sparse Latent Structure (D18-1)

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Vlad Niculae, André F. T. Martins, Claire Cardie

Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples (P18-1)

Challenge:	Existing approaches to learn latent structure are limited by factorization assumptions or end-to-end differentiability.
Approach:	They propose a method that allows for end-to-end learning of latent structure predictors jointly with a downstream predictor.
Outcome:	The proposed method allows for unrestricted dynamic graph construction from the global latent structure while maintaining differentiability.

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Vidur Joshi, Matthew Peters, Mark Hopkins

Text-to-SQL Error Correction with Language Models of Code (2023.acl-short)

Challenge:	Statistical parsers are often criticized for their performance outside of the domain they were trained on . we show that word representations reduce the need for domain adaptation when the target domain is syntactically similar to the source domain.
Approach:	They propose a way to adapt a parser to a syntactically similar target domain using partial annotations.
Outcome:	The proposed model increases the accuracy of a parser on the Wall Street Journal by 1.7% over the previous state-of-the-art model.

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Ziru Chen, Shijie Chen, Michael White, Raymond Mooney, Ali Payani, Jayanth Srinivasa, Yu Su, Huan Sun

Incorporating Syntactic Uncertainty in Neural Machine Translation with a Forest-to-Sequence Model (C18-1)

Challenge:	Existing semantic parsers are not accurate enough for use in text-to-SQL parsing tasks.
Approach:	They propose to build clause-level edit models to correct SQL queries instead of token-level ones.
Outcome:	The proposed model improves the exact set match accuracy of different parsers by 2.4-6.5 and obtains up to 4.3 point absolute improvement over two strong baselines.

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Poorya Zaremoodi, Gholamreza Haffari

Machine Translation

Challenge:	Incorporating syntactic information in machine translation can lead to better reorderings, especially useful when the language pairs are syntaktically highly divergent.
Approach:	They propose a forest-to-sequence NMT model which uses exponentially many parse trees of the source sentence to compensate for parser errors.
Outcome:	The proposed model outperforms the sequence-to-sequence and tree-to tree-based models on English, Chinese and Farsi translation tasks.

MIMIC: Multi-party Dialogue Augmentation via Speaker Stylistic Transfer (2026.findings-eacl)

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Gaetano Cimino, Giuseppe Carenini, Vincenzo Deufemia

Dialogue Systems & Conversational Agents

Challenge:	Existing discourse annotations are limited and annotated data scarcity has hindered progress in discourse parsing.
Approach:	They propose a framework for augmenting discourse-annotated corpora via speaker stylistic transfer using Large Language Models (LLMs).
Outcome:	The proposed framework outperforms parsers trained on STAC and Molweni corpora on a multi-party dialogue with consistent gains for underrepresented discourse patterns and in low-resource scenarios.

Detecting Independent Pronoun Bias with Partially-Synthetic Data Generation (2020.emnlp-main)

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Robert Munro, Alex (Carmen) Morrison

A Survey of Meaning Representations – From Theory to Practical Utility (2024.naacl-long)

Challenge:	linguistic differences between English pronouns that are not inherently biased can become biases in some machine learning models.
Approach:	They propose a method to detect bias by alternating pronouns in different contexts.
Outcome:	The proposed method can be used to detect bias in language models and for text generation more broadly.

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Zacchary Sadeddine, Juri Opitz, Fabian Suchanek

SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser (2022.emnlp-main)

Challenge:	Symbolic meaning representations of natural language text have been studied since at least the 1960s . with the availability of large annotated corpora, the field has recently seen several new developments .
Approach:	They propose a framework for expressing meaning in natural language text using annotated corpora and a set of tools for machine learning.
Outcome:	The frameworks are based on a set of theoretical and practical problems and their applications.

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Yue Zhang, Bo Zhang, Zhenghua Li, Zuyi Bao, Chen Li, Min Zhang

Can we obtain significant success in RST discourse parsing by using Large Language Models? (2024.eacl-long)

Challenge:	Existing approaches to grammatical error correction are unreliable when processing ungrammatically . a new approach is proposed that incorporates dependency syntactic information into the encoder part of GEC models.
Approach:	They propose a syntax-enhanced grammatical error correction approach called SynGEC that incorporates dependency syntactic information into the encoder part of GEC models.
Outcome:	The proposed approach outperforms strong baselines and achieves competitive performance on mainstream English and Chinese GEC datasets.

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Aru Maekawa, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura

Learning Morphosyntactic Analyzers from the Bible via Iterative Annotation Projection across 26 Languages (P19-1)

Challenge:	Experimental results show that LLMs with tens of billion parameters can perform discourse parsing tasks.
Approach:	They employ Llama 2 and fine-tune it with QLoRA to achieve similar results . they show that LLMs with tens of billion parameters can perform a wide range of NLP tasks .
Outcome:	The proposed model performs better than existing models on three benchmark datasets.

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Garrett Nicolai, David Yarowsky

Adaptive Learning of Local Semantic and Global Structure Representations for Text Classification (C18-1)

Challenge:	Currently, computational tools for low-resource languages are limited by a lack of supervised training data.
Approach:	They propose to use English taggers and parsers to project morphological information onto translations of the Bible in 26 different test languages.
Outcome:	The proposed method reduces lemmatization and morphological analysis over a strong initial system.

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Jianyu Zhao, Zhiqiang Zhan, Qichuan Yang, Yang Zhang, Changjian Hu, Zhensheng Li, Liuxin Zhang, Zhiqiang He

Information Retrieval

Challenge:	Existing representation models for text classification learn little structure information or rely on pre-defined structures.
Approach:	They propose a sandwich neural network to learn local semantic and global structure representations without relying on parsers.
Outcome:	The proposed approach achieves competitive performance on several text classification tasks.

Weakly Supervised Text-to-SQL Parsing through Question Decomposition (2022.findings-naacl)

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Tomer Wolfson, Daniel Deutch, Jonathan Berant

Calculating the optimal step of arc-eager parsing for non-projective trees (2021.eacl-main)

Challenge:	Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data.
Approach:	They propose a weak supervision approach for training text-to-SQL parsers by using a question meaning representation called QDMR to synthesize SQL queries from annotated NL-SqL data.
Outcome:	The proposed model performs competitively with those trained on annotated NL-SQL data.

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Mark-Jan Nederhof

Diversity-Aware Batch Active Learning for Dependency Parsing (2021.naacl-main)

Challenge:	arc-eager parsers are often driven by a classifier that determines the next step given features extracted from the current configuration.
Approach:	They propose to calculate the optimal next step of an arc-eager parser relative to a non-projective dependency structure in cubic time.
Outcome:	The proposed method can be used to train dependency parsers on configurations that correspond to 'gold' trees from a treebank.

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Tianze Shi, Adrian Benton, Igor Malioutov, Ozan İrsoy

Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification (2021.naacl-main)

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.

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Xiaochen Hou, Peng Qi, Guangtao Wang, Rex Ying, Jing Huang, Xiaodong He, Bowen Zhou

Automatic Thesaurus Construction for Modern Hebrew (L18-1)

Challenge:	Recent work on aspect-level sentiment classification has shown that syntactic information is effective in capturing long-range syntaktic relations that are obscure from the surface form.
Approach:	They propose a graph ensemble technique that integrates syntactic structures with GNNs to better leverage syntaktic information in the face of parsing errors.
Outcome:	The proposed model outperforms models with single dependency tree and beats other models without adding model parameters.

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Chaya Liebeskind, Ido Dagan, Jonathan Schler

Neural Generative Rhetorical Structure Parsing (D19-1)

Challenge:	Modern Hebrew lacks lexical resources fundamental to many natural language processing tools.
Approach:	They propose a method for generating a cooccurrence based thesaurus in a MRL and a distributional similarity method for Hebrew.
Outcome:	The proposed method is not optimal for modern Hebrew, the authors show . they used Hebrew WordNet as their gold standard for the analysis .

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Amandla Mabona, Laura Rimell, Stephen Clark, Andreas Vlachos

Predicting Discourse Structure using Distant Supervision from Sentiment (D19-1)

Challenge:	Existing approaches to RST parsing use discriminative models, but these are less sample efficient and datasets are typically small.
Approach:	They propose a document-level RNN grammar with a bottom-up traversal order and a beam search algorithm that keeps track of both structure-and word-generating actions without exhibit-ing this branching bias.
Outcome:	The proposed model outperforms a discriminative model with the same features by 2.6 F1points and achieves comparable performance to the state-of-the-art.

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Patrick Huber, Giuseppe Carenini

Discarding the Crutches: Adaptive Parameter-Efficient Expert Meta-Learning for Continual Semantic Parsing (2025.coling-main)

Challenge:	Discourse parsing is a fundamental NLP task known to enhance key downstream tasks, such as sentiment analysis, text classification and summarization.
Approach:	They propose a method that uses document supervision to generate abundant data for RST-style discourse structure prediction by using an optimal CKY-style tree generation algorithm.
Outcome:	The proposed approach performs well on the more difficult task of inter-domain discourse structure prediction, but it does not match the performance of a parser trained and tested on the same dataset.

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Ruiheng Liu, Jinyu Zhang, Yanqi Song, Yu Zhang, Bailong Yang

Ensemble Romanian Dependency Parsing with Neural Networks (L18-1)

Challenge:	Continual Semantic Parsing (CSP) enables parsers to generate SQL from natural language questions in task streams, using minimal annotated data to handle dynamically evolving databases in real-world scenarios.
Approach:	They propose a Adaptive PET eXpert meta-learning approach that assists experts in adaptively warming up, ensuring better model initialization.
Outcome:	The proposed method outperforms existing methods on two benchmarks and achieves superior performance without data replay or ideal settings.

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Radu Ion, Elena Irimia, Verginica Barbu Mititelu

A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing (2023.findings-acl)

Challenge:	SSPR is a Python 3.5 application based on the Microsoft Cognitive Toolkit 2.0 Python API.
Approach:	a Python 3.5 application is based on the Microsoft Cognitive Toolkit 2.0 Python API.
Outcome:	SSPR outperforms the best individual parser at the CONLL 2017 dependency parsing shared task.

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Ye Ma, Mingming Sun, Ping Li

An Empirical Investigation of Error Types in Vietnamese Parsing (C18-1)

Challenge:	Existing parsers that capture dependency graphs are lacking in capturing explicit dependencies . graph-based parsing is a popular choice for capturing dependency relationships between words .
Approach:	They propose a semi-autoregressive dependency parser that generates dependency graphs by adding nodes and edge groups autoregressively while pouring out all group elements in parallel.
Outcome:	The proposed method outperforms baselines on Enhanced Universal Dependencies of multiple languages.

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Quy Nguyen, Yusuke Miyao, Hiroshi Noji, Nhung Nguyen

An AMR Aligner Tuned by Transition-based Parser (D18-1)

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 .

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Yijia Liu, Wanxiang Che, Bo Zheng, Bing Qin, Ting Liu

MRL Parsing Without Tears: The Case of Hebrew (2024.findings-acl)

Challenge:	Experimental results show that our AMR aligner outperforms the rule-based aligner by achieving higher alignment F1 score and consistently improving two open-source AMR parsers.
Approach:	They propose a rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser.
Outcome:	The proposed AMR aligner outperforms the current state-of-the-art parser by achieving higher alignment F1 score and consistently improving two open-source AMR parsers.

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Shaltiel Shmidman, Avi Shmidman, Moshe Koppel, Reut Tsarfaty

Information Extraction & Text Mining

Challenge:	a new approach to parsing morphologically rich languages (MRLs) is needed to overcome the deficiencies of current approaches.
Approach:	They propose a "flipped pipeline" where multiple layers are predicted independently on whole-token basis and then synthesized.
Outcome:	The proposed model achieves near-SOTA performance on Hebrew NLP tasks.

Seq2seq Dependency Parsing (C18-1)

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Zuchao Li, Jiaxun Cai, Shexia He, Hai Zhao

Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing - A Tale of Two Parsers Revisited (D19-1)

Challenge:	Recent trend for dependency parsing is adopting neural networks due to their significant success in a wide range of applications.
Approach:	They propose a sequence to sequence (seq2seque) dependency parser that predicts the relative position of head for each word.
Outcome:	The proposed parser achieves 94.11% UAS on PTB and 88.78% UAS .

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Artur Kulmizev, Miryam de Lhoneux, Johannes Gontrum, Elena Fano, Joakim Nivre

Representation Learning

Challenge:	In recent years, dependency parsing has shifted from discrete features to neural networks and continuous representations.
Approach:	They propose to use deep contextualized word embeddings to pack information about global sentence structure into local feature representations to make the two approaches virtually equivalent in terms of accuracy and error profile.
Outcome:	The proposed model improves the accuracy and error profile of transition-based and graph-based dependency parsers on 13 languages.

Neural Constituency Parsing of Speech Transcripts (N19-1)

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Paria Jamshid Lou, Yufei Wang, Mark Johnson

Value-Agnostic Conversational Semantic Parsing (2021.acl-long)

Challenge:	a neural parser for transcribed speech can find EDITED disfluency nodes . this makes specialized mechanisms for parsing disfluencies unnecessary .
Approach:	They propose a neural self-attentive parser that finds EDITED disfluency nodes in transcribed speech.
Outcome:	The proposed parser finds EDITED disfluency nodes with an accuracy surpassing that of specialized systems.

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Emmanouil Antonios Platanios, Adam Pauls, Subhro Roy, Yuchen Zhang, Alexander Kyte, Alan Guo, Sam Thomson, Jayant Krishnamurthy, Jason Wolfe, Jacob Andreas, Dan Klein

Natural Language Interfaces

Challenge:	Existing models rely on rich representations of dialogue history that include all previously generated components of the output.
Approach:	They propose a model that abstracts over values to focus prediction on type- and function-level context.
Outcome:	The proposed model outperforms baseline models by 7.3% and 10.6% on SMCalFlow and TreeDST datasets.

Contextual Distortion Reveals Constituency: Masked Language Models are Implicit Parsers (2023.acl-long)

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Jiaxi Li, Wei Lu

Grounding language acquisition by training semantic parsers using captioned videos (D18-1)

Challenge:	a novel chart-based method for extracting parse trees from masked language models is proposed . a graph-based approach can be used to extract parser trees without training separate parsers .
Approach:	They propose a chart-based method for extracting parse trees from masked language models . they use a set of perturbations motivated by the linguistic concept of constituency tests to score each span .
Outcome:	The proposed method outperforms state-of-the-art methods on english with masked LMs and in multilingual settings.

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Candace Ross, Andrei Barbu, Yevgeni Berzak, Battushig Myanganbayar, Boris Katz

Parse Me if You Can: Artificial Treebanks for Parsing Experiments on Elliptical Constructions (L18-1)

Challenge:	a new method for parsing sentences using captioned videos is being developed . we use video clips to ground the semantics of language, but without annotations .
Approach:	They develop a semantic parser that is trained in a grounded setting using captioned videos . they use a corpus of sentences paired with videos without other annotations to train it .
Outcome:	The proposed parser recovers the meaning of English sentences despite no annotations . learning a grounded semantic parsers can expand the range of data that parseurs can be trained on .

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Kira Droganova, Daniel Zeman, Jenna Kanerva, Filip Ginter

Neural Transition Based Parsing of Web Queries: An Entity Based Approach (D18-1)

Challenge:	ellipsis is a phenomenon present in many natural languages, but it complicates syntactic parsing of the content that is not omitted.
Approach:	They analyze outputs of state-of-the-art parsers to learn about parsing accuracy and typical errors from the perspective of elliptical constructions.
Outcome:	The proposed treebank is a semi-artificially constructed treebank of ellipsis.

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Rivka Malca, Roi Reichart

On Robustness of Neural Semantic Parsers (2021.eacl-main)

Challenge:	Existing query parsers that account for the unique grammar of web queries rely on resources not available outside of big web corporations.
Approach:	They propose a biLSTM query parser that explicitly accounts for the unique grammar of queries.
Outcome:	The proposed query parser outperforms existing state-of-the-art parsers on 2500 annotated queries.

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Shuo Huang, Zhuang Li, Lizhen Qu, Lei Pan

Adversarial Learning for Discourse Rhetorical Structure Parsing (2021.acl-long)

Challenge:	Semantic parsing maps natural language (NL) utterances into logical forms (LFs) adversarial examples are created by adding tiny perturbations to inputs but can severely deteriorate model performance.
Approach:	They propose to construct robustness test sets based on existing benchmark corpora and to evaluate the effect of data augmentation.
Outcome:	The proposed method measures the performance of the proposed parsers on robustness test sets and evaluates the effect of data augmentation.

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Longyin Zhang, Fang Kong, Guodong Zhou

Robustness in NLP

Challenge:	Existing top-down discourse rhetorical structure parsers make local decisions and ignore global parsing.
Approach:	They propose a method to transform gold standard and predicted constituency trees into tree diagrams with two color channels.
Outcome:	The proposed method improves performance on RST-DT and CDTB corpora and can leverage global context.

Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers (2021.emnlp-main)

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Zhuang Li, Lizhen Qu, Gholamreza Haffari

Evaluating Sentence Segmentation in Different Datasets of Neuropsychological Language Tests in Brazilian Portuguese (2020.lrec-1)

Challenge:	Existing methods for continual learning for semantic parsing fail to account for special properties of structured outputs . retraining from scratch is not feasible due to the fast growing number of tasks .
Approach:	They propose a continual learning method that uses sequential learning to learn tasks without accessing full training data from previous tasks.
Outcome:	The proposed method achieves a 3-6 times speedup compared to re-training from scratch.

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Edresson Casanova, Marcos Treviso, Lilian Hübner, Sandra Aluísio

Alignment-free Cross-lingual Semantic Role Labeling (2020.emnlp-main)

Challenge:	Using automated analysis of connected speech is a promising direction for diagnosing cognitive impairments.
Approach:	They propose to use a novel model to segment impaired speech transcriptions . they propose to include a Linear Chain CRF and a self-attention mechanism .
Outcome:	The proposed system performs better than the existing model with three new datasets used to diagnose cognitive impairments.

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Rui Cai, Mirella Lapata

MGAD: Multilingual Generation of Analogy Datasets (L18-1)

Challenge:	Existing approaches to semantic role labeling rely on word alignments, translation engines or preprocessing tools.
Approach:	They propose a cross-lingual semantic role labeling model which only requires annotations in a source language and access to raw text in .
Outcome:	The proposed model minimizes the effort required to construct annotations or models for a new target language.

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Mostafa Abdou, Artur Kulmizev, Vinit Ravishankar

Accurate polyglot semantic parsing with DAG grammars (2020.findings-emnlp)

Challenge:	Existing methods for word embedding evaluation are computationally expensive and task-specific.
Approach:	They propose a minimally supervised method for generating word embedding evaluation datasets for a large number of languages using existing dependency treebanks and parsers.
Outcome:	The proposed method evaluates three popular word embedding algorithms against these datasets and shows that their performance varies between syntactic categories.

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Federico Fancellu, Ákos Kádár, Ran Zhang, Afsaneh Fazly

Fast semantic parsing with well-typedness guarantees (2020.emnlp-main)

Challenge:	Semantic parsers treat graphs as strings or trees, but there is no guarantee that the output is a well-formed graph.
Approach:	They propose a graph-aware sequence model that utilizes a DAG grammar to guide graph generation.
Outcome:	The proposed model outperforms string-based and DAG-grammar models by a large margin and can guarantee the well-formed graphs.

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Matthias Lindemann, Jonas Groschwitz, Alexander Koller

Target Word Masking for Location Metonymy Resolution (2020.coling-main)

Challenge:	Existing algorithms for AM dependency parsing are slow and do not support linguistic principles.
Approach:	They propose an A* parser and a transition-based parsing algorithm which guarantee well-typedness and improve parse speed by up to 3 orders of magnitude.
Outcome:	The proposed algorithms guarantee well-typedness and improve parsing speed by up to 3 orders of magnitude while maintaining or improving accuracy.

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Haonan Li, Maria Vasardani, Martin Tomko, Timothy Baldwin

Accounting for Language Effect in the Evaluation of Cross-lingual AMR Parsers (2022.coling-1)

Challenge:	Existing word sense disambiguation and named entity recognition systems have no explicit metonymy detection.
Approach:	They propose an end-to-end word-level classification approach based only on BERT . they show that their approach generalises well to unseen data .
Outcome:	The proposed approach surpasses conventional models and benchmarks on 5 datasets and generalises well to unseen data.

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Shira Wein, Nathan Schneider

Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing (P19-1)

Challenge:	Existing multilingual AMR evaluation metrics are not available for cross-lingual parsers . existing studies show that source language has a dramatic effect on cross-linguistic AMRs .
Approach:	They propose to use three multilingual adaptations of monolingual AMR evaluation metrics to evaluate cross-lingual AML parsers.
Outcome:	The proposed metric is the most highly correlated to english AMRs, while the most correlated is S2match.

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Aparna Garimella, Carmen Banea, Dirk Hovy, Rada Mihalcea

Better, Faster, Stronger Sequence Tagging Constituent Parsers (N19-1)

Challenge:	linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have not adapted to account for these differences.
Approach:	They annotate the Wall Street Journal part of the Penn Treebank with the gender information of the articles’ authors and build taggers and parsers trained on this data.
Outcome:	The proposed model can account for gendered differences in syntactic tasks and highlight future venues for developing more accurate taggers and parsers.

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David Vilares, Mostafa Abdou, Anders Søgaard

DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Parsing (2022.coling-1)

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.

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Bin Li, Miao Gao, Yunlong Fan, Yikemaiti Sataer, Zhiqiang Gao, Yaocheng Gui

Building a Universal Dependencies Treebank for Occitan (2020.lrec-1)

Challenge:	Existing parsers that learn graph representations based on static graphs are error-prone and disjointed . Graph-based parser can parse sentences efficiently but suffer from error propagation .
Approach:	They propose a dynamic graph learning framework to learn graph representations based on a static graph constructed by an existing parser.
Outcome:	The proposed parser outperforms the previous parsers on the SemEval-2015 task 18 dataset in three languages.

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Aleksandra Miletic, Myriam Bras, Marianne Vergez-Couret, Louise Esher, Clamença Poujade, Jean Sibille

Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion (2022.acl-long)

Challenge:	Low-resourced regional, non-official or minority languages often face lack of institutional support . low-resource languages often find themselves in a similar situation .
Approach:	They propose to create the first treebank for Occitan, a low-resourced regional language . they use an agile annotation approach and rely on pre-processing using existing tools .
Outcome:	The proposed treebank is the first for the low-resourced regional language Occitan . the project uses an agile annotation approach and automated pre-annotation .

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Chen Zhao, Yu Su, Adam Pauls, Emmanouil Antonios Platanios

Cross-lingual Text-to-SQL Semantic Parsing with Representation Mixup (2022.findings-emnlp)

Challenge:	Existing text-to-SQL parsers struggle with out-of-domain generalization problems, arguing that they lack the ability to match domain specific phrases to composite operations over columns.
Approach:	They propose to use a synthetic dataset and a re-purposed train/test split to quantify out-of-domain generalization over column operations to address this problem.
Outcome:	The proposed method outperforms baseline parsers on the domain generalization problem, while boosting the underlying parser’ overall performance by 13.8% relative accuracy gain (5.1% absolute).

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Peng Shi, Linfeng Song, Lifeng Jin, Haitao Mi, He Bai, Jimmy Lin, Dong Yu

Please Mind the Root: Decoding Arborescences for Dependency Parsing (2020.emnlp-main)

Challenge:	Experimental results show that Rex can benefit from cross-lingual training and improve the effectiveness of semantic parsers.
Approach:	They propose a Representation Mixup Framework for effectively exploiting translations in the cross-lingual Text-to-SQL task.
Outcome:	The proposed framework can benefit from cross-lingual training and improve the effectiveness of semantic parsers, achieving state-of-the-art performance.

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Ran Zmigrod, Tim Vieira, Ryan Cotterell

Typological Features for Multilingual Delexicalised Dependency Parsing (N19-1)

Challenge:	a dependency tree has a root constraint, but only one edge may emanate from the root node.
Approach:	They propose an algorithm which enforces a root constraint without compromising the original runtime.
Outcome:	The proposed algorithm satisfies the constraint without compromising the original runtime.

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Manon Scholivet, Franck Dary, Alexis Nasr, Benoit Favre, Carlos Ramisch

Maximum Bayes Smatch Ensemble Distillation for AMR Parsing (2022.naacl-main)

Challenge:	Existing universal models to describe the syntax of languages are debated for decades . a new study examines the plausibility of universal grammars in dependency parsing .
Approach:	They propose to use typological features to describe the syntax of languages to train a multilingual dependency parser.
Outcome:	The proposed model can be trained on 40 languages with the help of typological features.

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Young-Suk Lee, Ramón Astudillo, Hoang Thanh Lam, Tahira Naseem, Radu Florian, Salim Roukos

Computational Etymology and Word Emergence (2020.lrec-1)

Challenge:	AMR parsing has experienced an unprecendented increase in performance in the last three years due to a mixture of effects including architecture improvements and transfer learning.
Approach:	They propose to combine Smatch-based ensembling techniques with ensemble distillation to overcome this diminishing returns of silver data.
Outcome:	The proposed technique can produce gains rivaling those of human annotated data for QALD-9 and achieve a new state-of-the-art for BioAMR.

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Winston Wu, David Yarowsky

Challenge:	etymology is the study of words' origins.
Approach:	They develop an extensible Wiktionary parser that predicts the etymology of a word across the full range of ethymological types and languages in Wiktionaries.
Outcome:	The proposed parser predicts the etymology of a word across the full range of ethymologies and languages in Wiktionary, and shows the application of tymatics in modeling this phenomenon.

DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement (2025.emnlp-main)

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Shaoqing Lin, Chong Teng, Fei Li, Donghong Ji, Lizhen Qu, Zhuang Li

Domain-Aware Dependency Parsing for Questions (2021.findings-acl)

Challenge:	Current approaches typically merge sentence-level parsing outputs for discourse input, resulting in fragmented graphs and degraded downstream performance.
Approach:	They propose a task for discourse-level text scene graph parsing that merges sentence-level outputs for discourse input and propose 'DiscoSG' a dataset of 400 expert-annotated and 8,430 synthesised multi-sentence caption-graph pairs is used to test the new task.
Outcome:	The proposed task improves SPICE by 30% over the baseline while achieving 86 faster inference than existing models.

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Aparna Garimella, Laura Chiticariu, Yunyao Li

Dependencies over Times and Tools (DoTT) (2024.lrec-main)

Challenge:	Pre-trained parsers perform poorly on domain-specific questions, a paper argues . retraining parser with domain- specific questions is expensive, as these require linguistic expertise.
Approach:	They propose an automatic labeled domain question generation framework leveraging domain knowledge and seed domain questions.
Outcome:	The proposed framework improves state-of-the-art parsers on domain questions.

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Andy Luecking, Giuseppe Abrami, Leon Hammerla, Marc Rahn, Daniel Baumartz, Steffen Eger, Alexander Mehler

Challenge:	Using the examples of English and German, we examine how parsers trained on modern variants of these languages can be transferred to older language levels without loss.
Approach:	They develop a treebank of diachronic corpora enriched with dependency annotations using 3 parsers, 6 pre-trained language models, 5 newly trained models for German, and two tag sets.
Outcome:	The proposed treebank covers the time period from 1800 until today and is based on the DependencyAnnotator annotation tool.

Another Dead End for Morphological Tags? Perturbed Inputs and Parsing (2023.findings-acl)

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Alberto Muñoz-Ortiz, David Vilares

Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing (2021.emnlp-main)

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.

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Jingfeng Yang, Federico Fancellu, Bonnie Webber, Diyi Yang

TwittIrish: A Universal Dependencies Treebank of Tweets in Modern Irish (2022.acl-long)

Challenge:	Existing training data is limited for languages other than English, so is the performance of the developed parsers.
Approach:	They propose to apply a pre-trained multilingual model to Italian, German and Dutch parsers where only a small number of manually annotated parses are available.
Outcome:	The proposed model improves on six parsers in English and Italian, German and Dutch, with the addition of universal dependency relations and universal POS tags as model-agnostic features.

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Lauren Cassidy, Teresa Lynn, James Barry, Jennifer Foster

The Fragility of Multi-Treebank Parsing Evaluation (2022.coling-1)

Challenge:	Modern Irish is a minority language lacking computational resources for accurate automatic syntactic parsing of user-generated content.
Approach:	They propose to use a treebank to facilitate natural language parsing of user-generated content in Irish.
Outcome:	The proposed treebank enables natural language processing of user-generated content in Irish.

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Iago Alonso-Alonso, David Vilares, Carlos Gómez-Rodríguez

Enhancing Structure-aware Encoder with Extremely Limited Data for Graph-based Dependency Parsing (2022.coling-1)

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.

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Yuanhe Tian, Yan Song, Fei Xia

Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing (2021.emnlp-main)

Challenge:	Dependency parsing is an important natural language processing task which analyzes the syntactic structure of an input sentence.
Approach:	They propose a structure-aware encoder pre-trained on auto-parsed data to improve dependency parsing . they propose combining gold dependency trees with existing parsers to improve parser performance .
Outcome:	The proposed approach outperforms baselines under different parsers and dependency standards under different parameters and model architectures.

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Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo, Young-Suk Lee, Radu Florian, Salim Roukos

End-to-end Parsing of Procedural Text into Flow Graphs (2024.lrec-main)

Challenge:	Recent work shows that pre-trained sequence-to-sequence Transformer models are effective in predicting linearized Abstract Meaning Representation graphs.
Approach:	They propose a structure-aware transition-based approach to AMR parsing that integrates general pre-trained sequence-to-sequence language models with a structured transition set.
Outcome:	The proposed approach retains the desirable properties of previous approaches while reaching the new parsing state of the art for AMR 2.0.

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Dhaivat J. Bhatt, Seyed Ahmad Abdollahpouri Hosseini, Federico Fancellu, Afsaneh Fazly

The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning (2023.acl-long)

Challenge:	Existing flow graph parsers lack sufficient annotated data to train them . a lack of annotation can cause costly training, and poor flow graph training results in a large improvement.
Approach:	They propose a multi-task framework that performs tagging and graph generation simultaneously . they take advantage of the abundance of unlabelled recipes and generate noisy silver annotations .
Outcome:	The proposed model can unify the input representation and use compact encoders, resulting in small models with significantly fewer parameters than existing models.

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Zhuang Li, Lizhen Qu, Philip Cohen, Raj Tumuluri, Gholamreza Haffari

Enough Is Enough! a Case Study on the Effect of Data Size for Evaluation Using Universal Dependencies (2024.lrec-main)

Challenge:	Prior studies have focused on translating utterances from high-resource languages to low-resourced languages.
Approach:	They propose an active learning approach that exploits the strengths of both human and machine translations by iteratively adding small batches of human translations into the machine-translated training set.
Outcome:	The proposed approach reduces errors and bias in the translated data, resulting in higher parser accuracies than the current model trained on machine translations.

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Rob van der Goot, Zoey Liu, Max Müller-Eberstein

Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction (2021.acl-long)

Challenge:	a recent study suggests that large datasets require large amounts of simulated data and scores to perform model evaluation.
Approach:	They propose to use dependency parsing to optimize annotation efforts by using a sample size of 5,000 tokens for in-language in-domain datasets.
Outcome:	The proposed method can be applied to other tasks, including classification and translation tasks.

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Tianze Shi, Lillian Lee

Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference (2020.acl-main)

Challenge:	Existing bubble representations encoding coordination boundaries and internal relationships are difficult to detect and parse .
Approach:	They propose a bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously.
Outcome:	The proposed bubble parser beats state-of-the-art approaches on coordination structure prediction . the proposed system is based on a GENIA corpus and a Penn treebank .

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Nikita Kitaev, Dan Klein

“What Do You Mean by That?” A Parser-Independent Interactive Approach for Enhancing Text-to-SQL (2020.emnlp-main)

Challenge:	Using custom architectures, constituency parsers are limited and require specialized hardware.
Approach:	They propose an algorithm that assigns labels to each word in a sentence in parallel and then performs a reconciliation phase to extract a tree in (empirically) linear time.
Outcome:	The proposed model achieves 95.4 F1 on the WSJ test set while also achieving substantial speedups compared to current state-of-the-art parsers with comparable accuracies.

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Yuntao Li, Bei Chen, Qian Liu, Yan Gao, Jian-Guang Lou, Yan Zhang, Dongmei Zhang

Incorporating Contextual Information for Language-Independent, Dynamic Disambiguation Tasks (L18-1)

Challenge:	In Natural Language Interfaces to Databases systems, text-to-SQL parsers allow users to query databases by using natural language questions.
Approach:	They propose a parser-independent interactive approach that interacts with users using multi-choice questions and can easily work with arbitrary parsers.
Outcome:	The proposed approach improves performance with limited interaction turns by using simulation and human evaluation on two cross-domain datasets with five state-of-the-art parsers.

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Tobias Staron, Özge Alaçam, Wolfgang Menzel

Video Discourse Parsing and Its Application to Multimodal Summarization: A Dataset and Baseline Approaches (2024.findings-emnlp)

Challenge:	a proposed multimodal system can resolve syntactic ambiguities by exploiting external evidence, says a researcher . a parser that processes linguistic information is expected to handle syntakically unambiguous sentences, but it cannot.
Approach:	They propose to exploit external contextual information to resolve ambiguous sentences . they propose to use data-driven and grammar-based approaches to solve ambiguities .
Outcome:	The proposed system confirms this hypothesis in experiments on syntactically ambiguous sentences.

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Tsutomu Hirao, Naoki Kobayashi, Hidetaka Kamigaito, Manabu Okumura, Akisato Kimura

Text Generation

Challenge:	Fig. 1 shows the video's story structure and event relationships in discourse parsing.
Approach:	They propose to construct an RST tree for a video to represent its storyline and illustrate the event relationships between events.
Outcome:	The proposed model outperforms two existing approaches to video RST parsing: the ‘parsing after captioning’ framework and parser using visual features.

Enhancing Discourse Parsing for Local Structures from Social Media with LLM-Generated Data (2025.coling-main)

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Martial Pastor, Nelleke Oostdijk, Patricia Martin-Rodilla, Javier Parapar

Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples (2020.acl-main)

Challenge:	Existing discourse parsers do not generalize well across genres and text types.
Approach:	They propose to integrate large language models into RST discourse parsers to improve parser performance in a social media context.
Outcome:	The proposed model improves parser performance in a social media context without pre-identified discourse units.

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Xiaoqing Zheng, Jiehang Zeng, Yi Zhou, Cho-Jui Hsieh, Minhao Cheng, Xuanjing Huang

Robustness in NLP

Challenge:	Previously studies focused on semantic tasks such as sentiment analysis, question answering and reading comprehension.
Approach:	They propose two approaches to study where and how adversarial examples exist in dependency parsing . they use a state-of-the-art parser to find adversarials in existing texts .
Outcome:	The proposed approaches show that adversarial examples exist in dependency parsing . they show that up to 77% of input examples admit adversarials .

MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision (2020.emnlp-main)

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Patrick Huber, Giuseppe Carenini

On Parsing as Tagging (2022.emnlp-main)

Challenge:	Existing discourse treebanks are limited in the application of data-driven approaches to discourse parsing.
Approach:	They propose a method to automatically generate discourse treebanks using distant supervision from sentiment annotated datasets by heuristic beam-search strategy extended with a stochastic component.
Outcome:	The proposed method generates discourse trees incorporating structure and nuclearity for documents of arbitrary length using an efficient beam-search strategy, extended with a stochastic component.

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Afra Amini, Ryan Cotterell

Parser combinators for Tigrinya and Oromo morphology (L18-1)

Challenge:	Existing approaches to reduce constituency parsing to tagging are based on linearization, learning, and decoding . linearization of the derivation tree is the most critical factor in achieving accurate parsers as taggers .
Approach:	They propose a pipeline with three steps for reducing constituency parsing to tagging . they find that linearization and learning are critical factors for accurate parsers .
Outcome:	The proposed pipelines are linearized, learning, and decoded, and have three steps to achieve accurate parsing as taggers.

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Patrick Littell, Tom McCoy, Na-Rae Han, Shruti Rijhwani, Zaid Sheikh, David Mortensen, Teruko Mitamura, Lori Levin

Align-smatch: A Novel Evaluation Method for Chinese Abstract Meaning Representation Parsing based on Alignment of Concept and Relation (2022.lrec-1)

Challenge:	morphological parsers for two Afroasiatic languages are developed using a parser-combinator paradigm . the paradigm allows rapid development and ease of integration with other systems, but at a cost of non-optimal theoretical efficiency.
Approach:	They propose a rule-based morphological parser paradigm for Tigrinya and Oromo languages . they use a parsers-combinator paradigm instead of a finite-state paradigm .
Outcome:	The proposed paradigm allows rapid development and ease of integration with other systems, but at cost of non-optimal theoretical efficiency.

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Liming Xiao, Bin Li, Zhixing Xu, Kairui Huo, Minxuan Feng, Junsheng Zhou, Weiguang Qu

AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite (2023.emnlp-main)

Challenge:	Abstract Meaning Representation abstracts the meaning of sentences into a single-rooted, acyclic and directed graph.
Approach:	They propose to use a metric to evaluate concept alignment and relation alignment to improve Chinese AMR parsing evaluation methods.
Outcome:	The proposed method is more robust and compatible with concept alignment and relation alignment and more robust in evaluating arcs.

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Jonas Groschwitz, Shay Cohen, Lucia Donatelli, Meaghan Fowlie

An Empirical Revisiting of Linguistic Knowledge Fusion in Language Understanding Tasks (2022.emnlp-main)

Challenge:	Abstract Meaning Representation parsers have improved in recent years, but not solved.
Approach:	They propose an evaluation suite that evaluates AMR parsers on a range of phenomena . they find that current parser outputs are far from satisfactory .
Outcome:	The proposed evaluation suite reveals the abilities and shortcomings of current parsers.

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Changlong Yu, Tianyi Xiao, Lingpeng Kong, Yangqiu Song, Wilfred Ng

Adaptive Constraint Propagation: Scaling Structured Inference for Large Language Models via Meta-Reinforcement Learning (2026.acl-long)

Challenge:	Recent work attempts to explicitly incorporate human-defined linguistic priors into fine-tuning tasks.
Approach:	They replace parsed graphs or trees with trivial ones to investigate linguistic priors . they propose to use trivial graphs as baselines to design advanced knowledge fusion methods .
Outcome:	The use of trivial graphs improves performance in fully-supervised and few-shot settings.

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Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

HyGenar: An LLM-Driven Hybrid Genetic Algorithm for Few-Shot Grammar Generation (2025.findings-acl)

Challenge:	Large language models increasingly require structured inference, says aaron sagar . meta-learning learns universal constraint propagation policies without task-specific training . standard schedulers are inexpensive but myopic, because they optimize local effects .
Approach:	MetaJuLS learns universal constraint propagation policies applicable across languages and tasks without task-specific retraining.
Outcome:	MetaJuLS achieves 1.5-2.0 speedups over GPU-optimized baselines while maintaining accuracy within 0.2% of state-of-the-art parsers.

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Weizhi Tang, Yixuan Li, Chris Sypherd, Elizabeth Polgreen, Vaishak Belle

Low-Resource NLP

Challenge:	Existing grammar generation models perform sub-optimally, resulting in inconsistent syntactic and semantic accuracy.
Approach:	They propose an LLM-driven hybrid genetic algorithm to optimize grammar generation by inferring grammars from a set of examples and generated in Backus-Naur Form.
Outcome:	The proposed algorithm improves syntactic and semantic accuracy of generated grammars across LLMs.

Extrinsic Evaluation of French Dependency Parsers on a Specialized Corpus: Comparison of Distributional Thesauri (2020.lrec-1)

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Ludovic Tanguy, Pauline Brunet, Olivier Ferret

High-order Joint Constituency and Dependency Parsing (2024.lrec-main)

Challenge:	Using a frequency-based method, we can identify subsets of the same word contexts without any reference data.
Approach:	They compare 11 different French dependency parsers on a specialized corpus to generate distributional thesauri using a frequency-based method.
Outcome:	The proposed method can identify relevant subsets without reference data and the similarity is confirmed on a restricted distributional benchmark.

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Yanggan Gu, Yang Hou, Zhefeng Wang, Xinyu Duan, Zhenghua Li

Informal Persian Universal Dependency Treebank (2022.lrec-1)

Challenge:	Syntactic parsing aims to reveal how sentences are syntactically structured.
Approach:	They propose to produce compatible constituency and dependency trees simultaneously for input sentences . they adopt a much more efficient decoding algorithm and explore joint modeling at training phase .
Outcome:	The proposed model significantly improves matching ratio of whole trees compared to separate models . the proposed model adopts a much more efficient decoding algorithm .

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Roya Kabiri, Simin Karimi, Mihai Surdeanu

Automatic Correction of Syntactic Dependency Annotation Differences (2022.lrec-1)

Challenge:	phonological, morphological, and syntactic distinctions between formal and informal Persian are important . formal Persian is not a universally recognized form of language, but is a dialect of informal Persian .
Approach:	They develop an open-source treebank for informal Persian to train dependency parsers . they then train dependency lexicographers on existing treebanks and evaluate them on out-of-domain data .
Outcome:	The proposed treebanks show that they perform poorly when training on formal and informal Persians.

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Andrew Zupon, Andrew Carnie, Michael Hammond, Mihai Surdeanu

Representation Learning

Challenge:	Annotation inconsistencies between data sets can cause problems for low-resource NLP . a simple method for automatically detecting annotation mismatches between corpora is proposed .
Approach:	They propose a method for automatically detecting annotation mismatches between dependency parsing corpora . they also propose three related methods for automatically configuring the mismatch .
Outcome:	The proposed method improves performance on both converted and unconverted data.

Extracting Headless MWEs from Dependency Parse Trees: Parsing, Tagging, and Joint Modeling Approaches (2020.acl-main)

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Tianze Shi, Lillian Lee

Error Detection for Text-to-SQL Semantic Parsing (2023.findings-emnlp)

Challenge:	Headless multi-word expressions are frequent in natural language but lack internal syntactic dominance relations.
Approach:	They propose an efficient joint decoding algorithm that combines scores from both strategies.
Outcome:	The proposed algorithm combines scores from parsing and tagging for predicting flat MWEs . the proposed algorithm is more accurate than parse and more efficient for non-BERT features .

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Shijie Chen, Ziru Chen, Huan Sun, Yu Su

Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages (2025.findings-emnlp)

Challenge:	Existing text-to-SQL parsers are often over-confident, thus casting doubt on their trustworthiness when deployed for real use.
Approach:	They propose a parser-independent error detection model for text-to-SQL semantic parsing . they use a language model of code as its bedrock and graph neural networks to learn structural features of queries .
Outcome:	The proposed model outperforms parser-dependent uncertainty metrics on three strong parsers . it could improve the performance and usability of text-to-SQL semantic parsing, it is shown .

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Olga Kellert, Nemika Tyagi, Muhammad Imran, Nelvin Licona-Guevara, Carlos Gómez-Rodríguez

Low-Resource NLP

Challenge:	Existing approaches to analyzing code-switched data are limited in their ability to generalize to multilingual and mixed-language inputs.
Approach:	They propose a large-language model-based annotation pipeline to produce UD annotations for code-switched text.
Outcome:	The proposed pipeline outperforms existing parsers and baselines in syntactic analysis.

LIMIT: Language Identification, Misidentification, and Translation using Hierarchical Models in 350+ Languages (2023.emnlp-main)

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Milind Agarwal, Md Mahfuz Ibn Alam, Antonios Anastasopoulos

Structural Optimization Ambiguity and Simplicity Bias in Unsupervised Neural Grammar Induction (2024.findings-acl)

Challenge:	Currently, existing systems cannot accurately identify most of the world's 7000 languages due to lack of data and computational challenges.
Approach:	They propose a misprediction-resolution hierarchical model, LIMIT, that reduces error by 55% on a children's stories dataset and by 40% on 'fLORES-200' benchmark.
Outcome:	The proposed model reduces error by 55% on the MCS-350 and 40% on the FLORES-200 benchmarks.

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Jinwook Park, Kangil Kim

LMDX: Language Model-based Document Information Extraction and Localization (2024.findings-acl)

Challenge:	Unsupervised grammar induction models lack analysis of traditional challenges, especially regarding the training loss.
Approach:	They propose a method to reduce the parse pool per sentence for loss evaluation using structural bias from pre-trained parsers.
Outcome:	The proposed method significantly improves performance while reducing variance and bias toward overly simplistic parses.

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Vincent Perot, Kai Kang, Florian Luisier, Guolong Su, Xiaoyu Sun, Ramya Sree Boppana, Zilong Wang, Zifeng Wang, Jiaqi Mu, Hao Zhang, Chen-Yu Lee, Nan Hua

Improving Dialogue Discourse Parsing through Discourse-aware Utterance Clarification (2025.acl-long)

Challenge:	Large Language Models have revolutionized Natural Language Processing but their application in extracting information from visually rich documents has not been successful.
Approach:	They propose a language model-based document information extraction and localization methodology to reframe the document information extract task for a LLM.
Outcome:	The proposed method enables extraction of singular, repeated, and hierarchical entities with and without training data.

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Yaxin Fan, Peifeng Li, Qiaoming Zhu

Dialogue Systems & Conversational Agents

Challenge:	Extensive experiments on the STAC and Molweni datasets demonstrate that our approach effectively resolves ambiguities and significantly outperforms the state-of-the-art (SOTA) baselines.
Approach:	They propose a Discourse-aware Clarification Module (DCM) that generates clarifications for the parser through systematic clarification type reasoning and discourse goal reasoning.
Outcome:	Extensive experiments on the STAC and Molweni datasets demonstrate that the proposed module significantly outperforms the state-of-the-art (SOTA) framework.

Linking Adaptive Structure Induction and Neuron Filtering: A Spectral Perspective for Aspect-based Sentiment Analysis (2024.lrec-main)

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Hao Niu, Maoyi Wang, Yun Xiong, Biao Yang, Xing Jia, Zhonglei Guo

Approximating CKY with Transformers (2023.findings-emnlp)

Challenge:	incorporating structure information can improve the performance of aspect-based sentiment analysis.
Approach:	They propose a method to conduct neuron-level manipulations on word representations in the frequency domain.
Outcome:	The proposed method can achieve or come close to state-of-the-art in ABSA.

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Ghazal Khalighinejad, Ollie Liu, Sam Wiseman

Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing (2024.lrec-main)

Challenge:	CKY algorithm is a cubic dependence on sentence length, but transformers can be used to approximate it.
Approach:	They propose a transformer-based approach that approximates the CKY algorithm by directly predicting a sentence's parse and avoiding its cubic dependence on sentence length.
Outcome:	The proposed approach achieves better performance than comparable parsers that make use of CKY, while being faster.

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Jiangming Liu

Sentence Smith: Controllable Edits for Evaluating Text Embeddings (2025.emnlp-main)

Challenge:	Discourse Representation Structure (DRS) parsers are constrained when trained exclusively on monolingual data.
Approach:	They propose a cross-lingual training strategy that leverages cross-linguistic training data to train models in multiple languages.
Outcome:	The proposed method improves clause and graph parsing in English, German, Italian and Dutch.

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Hongji Li, Andrianos Michail, Reto Gubelmann, Simon Clematide, Juri Opitz