SEMANTIC PARSING

Practical Semantic Parsing for Spoken Language Understanding (N19-2)

Challenge:	Existing neural semantic parsers only focus on a small subset of tasks, such as SQL queries, robotic commands, and even general-purpose programming languages like Java.
Approach:	They propose a transition-based neural semantic parser that maps natural language utterances into formal meaning representations (MRs) they use an abstract syntax description language to constrain the output space and model the information flow.
Outcome:	Experiments on four different semantic parsing and code generation tasks show that the proposed system is generalizable, extensible, and effective.

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Marco Damonte, Rahul Goel, Tagyoung Chung

Deep Learning on Graphs for Natural Language Processing (2021.naacl-tutorials)

Challenge:	Existing systems that can handle a user's utterance are unable to handle Q&A or SLU.
Approach:	They build a transfer learning framework for executable semantic parsing . they show it is effective for Q&A and for spoken language understanding .
Outcome:	The proposed framework is effective for Q&A and Spoken Language Understanding . it can be learned by exploiting data on other domains, the authors show .

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Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li

NLP Workbench: Efficient and Extensible Integration of State-of-the-art Text Mining Tools (2023.eacl-demo)

Challenge:	Graph Neural Networks (GNNs) are powerful tools for non-Euclidean data modeling and are used in many graph-related NLP tasks.
Approach:	This tutorial will cover applying deep learning on graph techniques to NLP using Graph Neural Networks (GNNs) Graph4NLP is the first library for researchers and practitioners for easy use of GNNs for various NLP tasks.
Outcome:	This tutorial will cover the latest developments in deep learning on graph techniques and their applications in various NLP tasks.

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Peiran Yao, Matej Kosmajac, Abeer Waheed, Kostyantyn Guzhva, Natalie Hervieux, Denilson Barbosa

Green & Sustainable NLP

Challenge:	NLP Workbench is a web-based text mining platform that allows non-expert users to obtain semantic understanding of large-scale corpora using state-of-the-art text mining models.
Approach:	They propose to use a microservice architecture to replace existing models or integrate a new one.
Outcome:	The proposed model is extensible and can be easily replaced or integrated with existing models.

TurkishDelightNLP: A Neural Turkish NLP Toolkit (2022.naacl-demo)

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Huseyin Alecakir, Necva Bölücü, Burcu Can

BME-UW at SRST-2019: Surface realization with Interpreted Regular Tree Grammars (D19-63)

Challenge:	a neural Turkish NLP toolkit performs computational linguistic analyses from morphological level to semantic level.
Approach:	They propose a neural Turkish NLP toolkit that performs computational linguistic analyses from morphological level to semantic level.
Outcome:	The proposed toolkit performs computational linguistic analyses from morphological level to semantic level in Turkish.

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Ádám Kovács, Evelin Ács, Judit Ács, Andras Kornai, Gábor Recski

Standardized Tests as benchmarks for Artificial Intelligence (D18-3)

Challenge:	adaamko's system restores word order and inflection from a graph of typed, directed dependencies between lemmas.
Approach:	They propose a method that restores word order and inflection from a graph of typed, directed dependencies between lemmas.
Outcome:	The proposed system restores word order and inflection from a graph of typed, directed dependencies between lemmas.

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Mrinmaya Sachan, Minjoon Seo, Hannaneh Hajishirzi, Eric Xing

Question Answering

Challenge:	Standardized tests have been proposed as replacements to the Turing test as a driver for progress in AI.
Approach:	et al. propose standardized tests as replacements to the Turing test as a driver for progress in AI.
Outcome:	a series of standardized tests have been proposed as replacements to the Turing test . the tutorial categorizes open domain and closed domain tests into two categories . open domain tests require the system to have significant domain knowledge and reasoning capabilities.

N-LTP: An Open-source Neural Language Technology Platform for Chinese (2021.emnlp-demo)

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Wanxiang Che, Yunlong Feng, Libo Qin, Ting Liu

Semantic Structural Decomposition for Neural Machine Translation (2020.starsem-1)

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.

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Elior Sulem, Omri Abend, Ari Rappoport

Matt Gardner, Pradeep Dasigi, Srinivasan Iyer, Alane Suhr, Luke Zettlemoyer

Challenge:	Existing methods for translation of long sentences are limited by the translation of single sentences to single sentences.
Approach:	They propose to use semantic splitting of the source sentence as preprocessing for machine translation.
Outcome:	The proposed approach tackles two main limitations of state-of-the-art machine translation.

Neural Semantic Parsing (P18-5)

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Semantic Parsing with Dual Learning (P19-1)

Challenge:	Semantic parsing is the study of translating natural language utterances into machine-executable programs.
Approach:	They will describe the various approaches researchers have taken to translate natural language into a formal language . they will also discuss why much recent work has chosen to use standard programming languages instead of more linguistically-motivated representations.
Outcome:	This paper will describe the various approaches researchers have taken to translate natural language into a formal language.

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Ruisheng Cao, Su Zhu, Chen Liu, Jieyu Li, Kai Yu

EUSP: An Easy-to-Use Semantic Parsing PlatForm (D19-3)

Challenge:	Existing approaches to parse natural language queries are limited by lack of labeled data and constrained decoding.
Approach:	They propose a semantic parsing framework with the dual learning algorithm that makes full use of data through a dual-learning game.
Outcome:	The proposed approach achieves state-of-the-art performance on ATIS dataset and gets competitive performance on overnight dataset.

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Bo An, Chen Bo, Xianpei Han, Le Sun

SUBS: Subtree Substitution for Compositional Semantic Parsing (2022.naacl-main)

Challenge:	Semantic parsing aims to map natural language utterances into structured meaning representations.
Approach:	They propose a modular platform that allows developers to build semantic parser from scratch.
Outcome:	The proposed platform achieves competitive performance on semantic parsing task and improves performance of a business search engine.

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Jingfeng Yang, Le Zhang, Diyi Yang

Counterfactual Explanations for Natural Language Interfaces (2022.acl-short)

Challenge:	Semantic parsing models fail at compositional generalization due to lack of reasoning ability.
Approach:	They propose to use subtree substitution for compositional data augmentation to increase the number of subtreas with similar semantic functions as exchangeable.
Outcome:	The proposed method improves performance on Scan and GeoQuery, and new SOTA on compositional split of GeoQuery.

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George Tolkachev, Stephen Mell, Stephan Zdancewic, Osbert Bastani

Explainability & Interpretability in NLP

Challenge:	Semantic parsing is a promising technique for enabling natural language interfaces, but human language can encode concepts that do not exist in the underlying system or are encoded using different language.
Approach:	They propose a novel approach for generating explanations of a natural language interface based on semantic parsing by providing a user with an utterance and a demonstration of their desired goal.
Outcome:	The proposed approach significantly improves user performance and generates explanations that more closely match the user’s intent compared to two ablations.

Improving Generalization in Language Model-based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-based Techniques (2023.acl-short)

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Daking Rai, Bailin Wang, Yilun Zhou, Ziyu Yao

One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets (2021.starsem-1)

Challenge:	Pre-trained language models (LMs)2 have been adopted for semantic parsing due to their promising performance and straightforward architectures.
Approach:	They propose to use token preprocessing to preserve semantic boundaries of tokens produced by LM tokenizers and special tokens to mark the boundaries of aligned components.
Outcome:	The proposed techniques improve the performance of pre-trained language models on two text-to-SQL semantic parsing datasets.

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Marco Damonte, Emilio Monti

Simple and Effective Text Simplification Using Semantic and Neural Methods (P18-1)

Challenge:	Existing semantic parsing datasets lack a single standard for meaning representations . lack of a standard led to the creation of plethora of datasets requiring expert annotators .
Approach:	They propose to use multi-task learning to unify different datasets and train a single model for them.
Outcome:	The proposed architectures yield better parsing accuracies and composition generalization than single-task models.

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Elior Sulem, Omri Abend, Ari Rappoport

Translate First Reorder Later: Leveraging Monotonicity in Semantic Parsing (2023.findings-eacl)

Challenge:	Sentence splitting is a major simplification operation.
Approach:	They propose a simple and efficient splitting algorithm based on an automatic semantic parser.
Outcome:	The proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification.

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Francesco Cazzaro, Davide Locatelli, Ariadna Quattoni, Xavier Carreras

The Power of Prompt Tuning for Low-Resource Semantic Parsing (2022.acl-short)

Challenge:	Existing approaches that model alignments between sentences fail at compositional generalization tasks, resulting in a resurgence of such approaches.
Approach:	They propose a two-step approach that first translates input sentences monotonically and then reorders them to obtain the correct output.
Outcome:	The proposed approach improves compositional generalization over existing models and other approaches that exploit gold alignment annotations.

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Nathan Schucher, Siva Reddy, Harm de Vries

Graph Enhanced Cross-Domain Text-to-SQL Generation (D19-53)

Challenge:	Prompt tuning is an effective method for adapting pre-trained language models to downstream tasks.
Approach:	They propose to use prompt tuning for semantic parsing to map natural language utterances onto formal meaning representations.
Outcome:	The proposed method outperforms the fine-tuned model on low-resource splits of Overnight and TOPv2 on language representations with increasing model scale and target representations.

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Siyu Huo, Tengfei Ma, Jie Chen, Maria Chang, Lingfei Wu, Michael Witbrock

Interactive Instance-based Evaluation of Knowledge Base Question Answering (D18-2)

Challenge:	Existing deep learning approaches for semantic parsing do not generalize to unseen data sets . existing benchmarks have shown text-to-SQL parsers do not generally perform well to unsen SQL queries.
Approach:	They propose a new cross-domain learning scheme to perform text-to-SQL translation . they demonstrate its use on a large-scale cross- domain text- to-Sql data set Spider .
Outcome:	The proposed learning scheme improves on a large-scale text-to-SQL data set.

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Daniil Sorokin, Iryna Gurevych

Align and Augment: Generative Data Augmentation for Compositional Generalization (2024.eacl-long)

Challenge:	Existing approaches to Knowledge Base Question Answering are based on semantic parsing.
Approach:	They propose a tool that aids in debugging of question answering systems that construct a structured semantic representation for the input question.
Outcome:	The proposed system allows debugging of model predictions on individual instances and simplifies manual error analysis.

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Francesco Cazzaro, Davide Locatelli, Ariadna Quattoni

Bridging the Gap between Synthetic and Natural Questions via Sentence Decomposition for Semantic Parsing (2023.tacl-1)

Challenge:	Recent work on semantic parsing has shown that seq2seq models find compositional generalization challenging.
Approach:	They propose a data-augmentation strategy that exploits alignment annotations between sentences and their corresponding meaning representations to improve compositional generalization.
Outcome:	The proposed model improves compositional generalization performance by exploiting alignment annotations between sentences and their corresponding meaning representations.

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Yilin Niu, Fei Huang, Wei Liu, Jianwei Cui, Bin Wang, Minlie Huang

Noise Robust Named Entity Understanding for Voice Assistants (2021.naacl-industry)

Challenge:	Existing methods to train a parser to perform zero-shot learning are limited by the lack of training data.
Approach:	They propose a decomposition-based method to unify the sentence structures of questions . their method can generalize to natural questions with novel text expressions .
Outcome:	The proposed method improves on synthetic data and on complex web questions with novel expressions.

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Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, Kushal Tayal, Roger Zheng, Peter Grasch, Jason D Williams, Lin Li

Robustness in NLP

Challenge:	Named Entity Recognition and Entity Linking are challenging for voice assistants . utterances are relatively short, so there is not much context to help disambiguate .
Approach:	They propose a Named Entity Understanding system that combines NER and EL in a joint reranking module.
Outcome:	The proposed framework improves NER accuracy by up to 3.13% and EL accuracy by 3.6% in F1 score . it also leads to better accuracies in other natural language understanding tasks .

Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction (2022.findings-acl)

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Lingbo Mo, Ashley Lewis, Huan Sun, Michael White

Zero-shot Learning of Classifiers from Natural Language Quantification (P18-1)

Challenge:	Existing studies on semantic parsing focus on mapping a natural-language utterance to a logical form (LF) but natural language may contain ambiguity and variability, making this challenge difficult.
Approach:	They propose an interactive semantic parsing framework that explains the predicted LF step by step in natural language and enables the user to make corrections through natural-language feedback for individual steps.
Outcome:	The proposed framework improves parsing accuracy and transparency in a crowdsourced dialogue dataset.

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Shashank Srivastava, Igor Labutov, Tom Mitchell

SmBoP: Semi-autoregressive Bottom-up Semantic Parsing (2021.naacl-main)

Challenge:	Existing methods to learn concepts from natural language are limited or no labeled examples.
Approach:	They propose a framework through which a set of explanations of a concept can be used to learn a classifier without access to any labeled examples.
Outcome:	The proposed framework outperforms previous approaches for learning with limited data and is comparable with fully supervised classifiers trained from a small number of labeled examples.

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Ohad Rubin, Jonathan Berant

SGL: Speaking the Graph Languages of Semantic Parsing via Multilingual Translation (2021.naacl-main)

Challenge:	Existing semantic parsers decode syntax using a top-down depth-first traversal.
Approach:	They propose a semi-autoregressive bottom-up parser that constructs at decoding step t the top-K sub-trees of height t.
Outcome:	The proposed method achieves 2.2x speed-up in decoding time and 5x speed up in training time on a zero-shot semantic parsing benchmark.

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Luigi Procopio, Rocco Tripodi, Roberto Navigli

Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding (2022.emnlp-industry)

Challenge:	Graph-based semantic parsing is one of the most promising general-purpose meaning representations . owing to this heterogeneity, most research focused on solutions specific to a given formalism .
Approach:	They propose a multilingual neural machine translation framework for Graph-based semantic parsing . they propose Graph2seq architecture that trains with an MNMT objective .
Outcome:	The proposed framework outperforms all competitors on cross-lingual parsing tasks.

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Jun Wang, Patrick Ng, Alexander Hanbo Li, Jiarong Jiang, Zhiguo Wang, Bing Xiang, Ramesh Nallapati, Sudipta Sengupta

Meta-Learning for Domain Generalization in Semantic Parsing (2021.naacl-main)

Challenge:	Recent research on Text-to-SQL semantic parsing relies on parser or heuristic based approach to understand natural language query.
Approach:	They propose a general-purpose, modular neural semantic parsing framework that is based on token-level fine-grained query understanding.
Outcome:	The proposed framework outperforms the state-of-the-art model by 2.7% on a WikiTableQuestions test set.

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Bailin Wang, Mirella Lapata, Ivan Titov

Multitask Parsing Across Semantic Representations (P18-1)

Challenge:	Existing approaches to parsing use standard supervised learning, but little attention has been given to domain generalization.
Approach:	They propose a meta-learning framework which targets zero-shot domain generalization for semantic parsing.
Outcome:	The proposed framework significantly boosts parser performance on English and Chinese spider datasets.

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Daniel Hershcovich, Omri Abend, Ari Rappoport

KaFSP: Knowledge-Aware Fuzzy Semantic Parsing for Conversational Question Answering over a Large-Scale Knowledge Base (2022.acl-long)

Challenge:	UCCA parsing is a test case for multitask learning, with auxiliary tasks AMR, SDP and Universal Dependencies (UD) . Semantic parsers have arguably yet to reach their full potential due to the limited amount of semantically annotated training data.
Approach:	They propose a general transition-based parser that can parse UCCA, AMR, SDP and Universal Dependencies (UD) they use a transition-driven learning architecture and a uniform transition-basic learning architecture to train the parsers.
Outcome:	The proposed parser improves UCCA, AMR, SDP and Universal Dependencies (UD) parsing over training in English, German and French.

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Junzhuo Li, Deyi Xiong

LexSym: Compositionality as Lexical Symmetry (2023.acl-long)

Challenge:	Existing semantic parsing frameworks for conversational question answering do not handle uncertain reasoning . qa over large knowledge bases has attracted broad interest due to the popularity of intelligent virtual assistants .
Approach:	They propose a fuzzy semantic parsing framework that defines fuzzy comparison operations in grammar for uncertain reasoning based on fuzzy set theory.
Outcome:	The proposed framework achieves significant improvements over state-of-the-art models on a large-scale conversational question answering benchmark.

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Ekin Akyurek, Jacob Andreas

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)

Challenge:	Existing approaches to generalize compositional models fail to generalise from small datasets.
Approach:	They propose a domain-general and model-agnostic formulation of compositionality as a constraint on symmetries of data distributions rather than models.
Outcome:	The proposed procedure matches or surpasses state-of-the-art, task-specific models on COGS semantic parsing, SCAN and Alchemy instruction following, and CLEVR-CoGenT visual question answering datasets.

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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu

On Graph-based Reentrancy-free Semantic Parsing (2023.tacl-1)

Challenge:	Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately .
Approach:	They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes .
Outcome:	The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show .

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Alban Petit, Caio Corro

Natural Language Interface for Databases Using a Dual-Encoder Model (C18-1)

Challenge:	Existing graph-based approaches for semantic parsing fail on compositional generalization tasks.
Approach:	They propose a graph-based approach for semantic parsing that solves two problems . they propose two algorithms based on constraint smoothing and conditional gradient to approximate these problems.
Outcome:	The proposed graph-based approach delivers state-of-the-art results on GeoQuery, Scan, and Clevr .

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Ionel Alexandru Hosu, Radu Cristian Alexandru Iacob, Florin Brad, Stefan Ruseti, Traian Rebedea

Multi-VALUE: A Framework for Cross-Dialectal English NLP (2023.acl-long)

Challenge:	Existing approaches to train data-driven natural language interfaces for databases are limited and lack of large datasets is probably the main reason for the lack of complex machine learning approaches.
Approach:	They propose a sketch-based two-step neural model for generating structured queries based on a user’s request in natural language.
Outcome:	The proposed model improves on two recent large datasets suitable for data-driven solutions for natural language interfaces for databases.

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Caleb Ziems, William Held, Jingfeng Yang, Jwala Dhamala, Rahul Gupta, Diyi Yang

Machine Translation

Challenge:	Current systems that focus on standard American English are not dialect invariant . current systems focus on a single dialect, which results in performance discrepancies .
Approach:	They propose a resource for evaluating and achieving English dialect invariance . they stress test question answering, machine translation, and semantic parsing .
Outcome:	The proposed system is based on a rule-based translation system spanning 50 English dialects and 189 unique linguistic features.

How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering (2024.findings-acl)

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Jinxin Liu, Shulin Cao, Jiaxin Shi, Tingjian Zhang, Lunyiu Nie, Linmei Hu, Lei Hou, Juanzi Li

Bootstrapping a Crosslingual Semantic Parser (2020.findings-emnlp)

Challenge:	Recent studies have validated that large language models (LLMs) are capable of solving some KBQA problems, but there has been little discussion on the differences in LLMs’ proficiency in formal languages used in semantic parsing.
Approach:	They propose to evaluate the understanding and generation ability of large language models (LLMs) to deal with differently structured logical forms by examining the inter-conversion of natural and formal language through in-context learning of LLMs.
Outcome:	The proposed model can understand formal languages as well as humans, but generating correct logical forms remains a challenge.

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Tom Sherborne, Yumo Xu, Mirella Lapata

Content Differences in Syntactic and Semantic Representation (N19-1)

Challenge:	Recent advances in semantic parsing are limited to English but professional translation can be prohibitively expensive.
Approach:	They adapt a semantic parser trained on a single language to new languages and multiple domains with minimal annotation.
Outcome:	The proposed approach achieves parsing accuracy within 2% of translation using only 50% of training data.

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Daniel Hershcovich, Omri Abend, Ari Rappoport

OpenICL: An Open-Source Framework for In-context Learning (2023.acl-demo)

Challenge:	Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate.
Approach:	They propose to use Universal Dependencies and UCCA as test cases to compare syntactic and semantic schemes.
Outcome:	The proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement.

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Zhenyu Wu, Yaoxiang Wang, Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Jingjing Xu, Yu Qiao

Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing (2021.findings-emnlp)

Challenge:	In-context Learning (ICL) is a new paradigm for large language model evaluation.
Approach:	They propose an open-source toolkit for ICL and LLM evaluation.
Outcome:	The proposed framework is highly flexible and flexible and can be easily combined with other tools to suit users' needs.

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Denis Lukovnikov, Sina Daubener, Asja Fischer

Multimodal Logical Inference System for Visual-Textual Entailment (P19-2)

Challenge:	Neural network models suffer from performance losses when faced with compositionally out-of-distribution data.
Approach:	They propose to use neural semantic parsers to detect compositionally out-of-distribution (OOD) data.
Outcome:	The proposed methods perform well on the standard SCAN and CFQ datasets.

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Riko Suzuki, Hitomi Yanaka, Masashi Yoshikawa, Koji Mineshima, Daisuke Bekki

Visual Data in NLP

Challenge:	Recent studies of multimodal inference provide challenging tasks such as visual question answering and visual reasoning.
Approach:	They propose an unsupervised multimodal logical inference system that can prove entailment relations between texts and images by combing semantic parsing and theorem proving.
Outcome:	The proposed system can handle semantically complex sentences for visual-textual inference.

Point-of-Interest Oriented Question Answering with Joint Inference of Semantic Matching and Distance Correlation (2020.aacl-main)

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Yifei Yuan, Jingbo Zhou, Wai Lam

Question Answering

Challenge:	Existing methods for POI oriented question answering lack ability to handle important POI related information.
Approach:	They propose a deep learning framework integrated with joint inference to capture tag semantic and geographic correlation between question and POIs.
Outcome:	The proposed model captures both tag semantic and geographic correlation between question and POIs.

DART: An AIGT Detector using AMR of Rephrased Text (2025.naacl-short)

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Hyeonchu Park, Byungjun Kim, Bugeun Kim

Syntactic and Semantic Uniformity for Semantic Parsing and Task-Oriented Dialogue Systems (2022.findings-emnlp)

Challenge:	Existing methods for detecting AIGTs focus on probabilistic features, causing problems . performance of black-box detectors is low, and it is difficult to detect black- box models .
Approach:	They propose a detector that can discriminate multiple black-box LLMs without probabilistic features and the origin of AIGT.
Outcome:	The proposed method can discriminate multiple black-box LLMs without probabilistic features and the origin of AIGT.

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Bowen Chen, Yusuke Miyao

Semantic Structural Evaluation for Text Simplification (N18-1)

Challenge:	Existing approaches to model natural language use pre-trained language models, but little attention has been paid to the representation of machine-readable formats.
Approach:	They propose a data representation framework for semantic parsing and task-oriented dialogue systems . they define a meta grammar for syntactically uniform representations and translate semantically equivalent functions into a uniform vocabulary.
Outcome:	The proposed representation improves accuracy and allows for transfer learning across datasets.

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Elior Sulem, Omri Abend, Ari Rappoport

Paraphrasing

Challenge:	Current measures for evaluating text simplification systems focus on lexical aspects, neglecting its structural aspects.
Approach:	They propose to use a reference-less automatic evaluation procedure to assess simplification quality by decomposing the input based on its semantic structure and comparing it to the output.
Outcome:	The proposed measure has a significant correlation with human judgments and is highly comparable with existing measures.

Skeletons Matter: Dynamic Data Augmentation for Text-to-Query (2025.emnlp-main)

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Yuchen Ji, Bo Xu, Jie Shi, Jiaqing Liang, Deqing Yang, Yu Mao, Hai Chen, Yanghua Xiao

Polyglot Semantic Parsing in APIs (N18-1)

Challenge:	Existing studies focus on a single query language, resulting in limited generalizability . a new task paradigm is proposed to unify semantic parsing tasks across different query languages .
Approach:	They propose a task paradigm that unifies parsing tasks across query languages . they identify query skeletons as a shared optimization target of Text-to-Query tasks .
Outcome:	The proposed method achieves state-of-the-art performance using only a small amount of synthesized data.

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Kyle Richardson, Jonathan Berant, Jonas Kuhn

Calibrated Interpretation: Confidence Estimation in Semantic Parsing (2023.tacl-1)

Challenge:	Existing approaches to semantic parsing work by training individual models for each available parallel dataset of text-meaning pairs.
Approach:	They propose a polyglot semantic translation approach that trains on multiple datasets and natural languages to learn parsing models.
Outcome:	The proposed model can be used for parsing a wide variety of natural languages and output languages, and achieves state-of-the-art performance on the above datasets.

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Elias Stengel-Eskin, Benjamin Van Durme

Structural generalization in COGS: Supertagging is (almost) all you need (2023.emnlp-main)

Challenge:	Sequence generation models are increasingly being used to translate natural language into programs . calibration of such models is a key component of safety, says aaron sagar .
Approach:	They investigate whether calibration of popular generation models varies across models and datasets . they find that calibration varies among models and data sets, and that it is important to include it in evaluations if it is included .
Outcome:	The calibration of popular generation models varies across models and datasets . the authors find that the accuracy of models is dependent on confidence .

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Alban Petit, Caio Corro, François Yvon

StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing (P18-1)

Challenge:	Recent studies have shown that neural networks fail to generalize on out-of-distribution examples.
Approach:	They extend a neural graph-based parsing framework to address compositional generalization limitations . they introduce a supertagging step with valency constraints and reduce the graph prediction problem .
Outcome:	The proposed approach improves results on COGS datasets that require structural generalization.

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Pengcheng Yin, Chunting Zhou, Junxian He, Graham Neubig

Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing (P18-1)

Challenge:	Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures.
Approach:	They propose a variational auto-encoding model for semi-supervised semantic parsing which learns from limited amounts of parallel data and readily-available unlabeled NL utterances.
Outcome:	Experiments on ATIS domain and Python show that with extra unlabeled data, StructVAE outperforms strong supervised models.

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Bo Chen, Le Sun, Xianpei Han

Benchmarking Long-tail Generalization with Likelihood Splits (2023.findings-eacl)

Challenge:	Existing methods for semantic parsing are difficult to design and learn, especially in wideopen domains.
Approach:	They propose a neural semantic parsing approach which models semantic par- sing as an end-to-end semantic graph generation process.
Outcome:	The proposed model achieves state-of-the-art performance on Overnight dataset and gets competitive performance on Geo and Atis datasets.

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Ameya Godbole, Robin Jia

Memory-Based Semantic Parsing (2021.tacl-1)

Challenge:	Existing methods to test out of distribution generalization have their own strengths and weaknesses.
Approach:	They propose a method to create challenging benchmarks that require generalizing to the tail of the distribution by re-splitting existing datasets.
Outcome:	The proposed approach can be customized to construct meaningful splits for a wide range of tasks.

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Parag Jain, Mirella Lapata

Consistency Regularization Training for Compositional Generalization (2023.acl-long)

Challenge:	Existing models for context-dependent semantic parsing focus on parse utterances in isolation . a decoder cannot copy or modify the parser from the previous utterrance .
Approach:	They propose to represent contextual information using an external memory by maintaining the cumulative meaning of sequential user utterances.
Outcome:	The proposed model can better process context-dependent information without task-specific decoders.

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Yongjing Yin, Jiali Zeng, Yafu Li, Fandong Meng, Jie Zhou, Yue Zhang

Span-based Semantic Parsing for Compositional Generalization (2021.acl-long)

Challenge:	Existing neural models have difficulty generalizing to unseen combinations of seen components.
Approach:	They propose to improve the capability of Transformer on compositional generalization by consistency regularization training without modifying model architectures.
Outcome:	The proposed model performs well on semantic parsing and machine translation benchmarks.

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Jonathan Herzig, Jonathan Berant

Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both? (2021.acl-long)

Challenge:	despite success of sequence-to-sequence models, they fail in compositional generalization . a span-based parser that predicts a utterance over spans improves performance .
Approach:	They propose a span-based parser that predicts a utterance over a given span tree . they propose to use CKY to encode how partial programs compose over spans .
Outcome:	The proposed model performs better on random splits than baselines that require compositional generalization.

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Peter Shaw, Ming-Wei Chang, Panupong Pasupat, Kristina Toutanova

Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints (2025.acl-short)

Challenge:	Existing approaches to semantic parsing only evaluated on synthetic datasets that are not representative of natural language variation.
Approach:	They propose a semantic parsing approach that handles both natural language variation and compositional generalization.
Outcome:	The proposed model outperforms existing models across compositional generalization challenges on non-synthetic datasets while being competitive with the state-of-the-art on standard evaluations.

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Kaikai An, Shuzheng Si, Helan Hu, Haozhe Zhao, Yuchi Wang, Qingyan Guo, Baobao Chang

Compositional Generalization via Semantic Tagging (2021.findings-emnlp)

Challenge:	Semantic Parsing improves performance of smaller models, but it is unclear whether it extends similarly to large language models.
Approach:	They propose a prompting approach that embeds semantic hints within the prompt to improve LLM performance.
Outcome:	The proposed approach improves LLMs’ performance across various tasks, highlighting the potential of integrating semantic information to improve LLM capabilities.

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Hao Zheng, Mirella Lapata

Grammar-based Decoding for Improved Compositional Generalization in Semantic Parsing (2023.findings-acl)

Challenge:	Existing neural sequence-to-sequence models fail at compositional generalization, i.e., they cannot generalize to unseen compositions of seen components.
Approach:	They propose a decoding framework that preserves expressivity and generality of sequence-to-sequence models while featuring lexicon-style alignments and disentangled information processing.
Outcome:	The proposed framework improves compositional generalization across model architectures, domains, and semantic formalisms on three semantic parsing datasets.

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Jing Zheng, Jyh-Herng Chow, Zhongnan Shen, Peng Xu

Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labelling (2021.emnlp-main)

Challenge:	Sequence-to-sequence (seq2sequ) models have been successful in semantic parsing tasks but struggle on out-of-distribution data.
Approach:	They propose to use a large-scale dialogue dataset to evaluate compositional generalization of semantic parsing.
Outcome:	The proposed model outperforms BART- and T5-based models on the SMCalflow-CS dataset on the zero-shot learning task.

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Leon Weber, Jannes Münchmeyer, Samuele Garda, Ulf Leser

Structured Data in NLP

Challenge:	Generating or modifying graphs from natural language text has applications in many subfields, such as dependency parsing or knowledge graph construction.
Approach:	They propose a method that first embeds the graph and the instructions with a joint encoder and then rebuilds it using a separate generative model for graphs conditioned on h.
Outcome:	The proposed method improves accuracy on three scene graph modification data sets while the state-of-the-art fails to generalize.

On Evaluating Multilingual Compositional Generalization with Translated Datasets (2023.acl-long)

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Zi Wang, Daniel Hershcovich

Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data (2021.acl-short)

Challenge:	a growing amount of research investigating compositional generalization in NLP is done on English . a critical semantic distortion is a limitation of the translation of datasets .
Approach:	They propose to translate a dataset for evaluating compositional generalization in semantic parsing.
Outcome:	The proposed benchmarks show that the translation of the MCWQ dataset suffers from semantic distortion.

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Sajad Norouzi, Keyi Tang, Yanshuai Cao

S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers (2022.findings-acl)

Challenge:	a generic transformer-based model can achieve competitive performance with minimal code-generation-specific inductive bias design.
Approach:	They investigate whether a generic transformer-based seq2seq model can achieve competitive performance with minimal code-generation-specific inductive bias design.
Outcome:	The proposed model achieves 81.03% exact match accuracy on Django and 32.57 BLEU score on CoNaLa.

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Binyuan Hui, Ruiying Geng, Lihan Wang, Bowen Qin, Yanyang Li, Bowen Li, Jian Sun, Yongbin Li

On The Ingredients of an Effective Zero-shot Semantic Parser (2022.acl-long)

Challenge:	Existing graph-based encoders for text-to-SQL do not model the syntax of natural language questions.
Approach:	They propose to inject Syntax to question-Schema graph encoder for text-to-SQL parsers and employ the decoupling constraint to induce diverse relational edge embedding.
Outcome:	The proposed approach outperforms all existing methods when pre-training models are used, resulting in a performance ranks first on the Spider leaderboard.

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Pengcheng Yin, John Wieting, Avirup Sil, Graham Neubig

Learning Cross-lingual Distributed Logical Representations for Semantic Parsing (P18-2)

Challenge:	Recent studies have performed zero-shot learning by synthesizing training examples of canonical utterances and programs from a grammar, and further paraphrasing these utterrances to improve linguistic diversity.
Approach:	They propose to bridge gaps between canonical and real-world user-issued examples by using stronger paraphrasers and improved grammars.
Outcome:	The proposed model achieves strong performance on two semantic parsing benchmarks with zero labeled data.

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Yanyan Zou, Wei Lu

Few-Shot Semantic Parsing for New Predicates (2021.eacl-main)

Challenge:	Recent research efforts have looked into the problem of learning semantic parsers in a multilingual setup, but how to improve the performance of a monolingual semantic parsed system remains a research question that is under-explored.
Approach:	They propose to use data annotated in different languages to learn distributed representations of logical forms for improving a monolingual semantic parser.
Outcome:	The proposed method improves on the standard multilingual GeoQuery dataset.

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Zhuang Li, Lizhen Qu, Shuo Huang, Gholamreza Haffari

Benchmarking Meaning Representations in Neural Semantic Parsing (2020.emnlp-main)

Challenge:	a recent study shows that state-of-the-art neural semantic parsers are less accurate when there is only a handful of utterance-logical form pairs per predicate.
Approach:	They propose to use a meta-learning method to train a few-shot learning problem . they also propose to regularize attention scores with alignment statistics and apply a smoothing technique .
Outcome:	The proposed method outperforms baselines in one and two-shot settings.

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Jiaqi Guo, Qian Liu, Jian-Guang Lou, Zhenwen Li, Xueqing Liu, Tao Xie, Ting Liu

ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models (2024.findings-acl)

Challenge:	Existing work on meaning representations is not comprehensively evaluated due to the lack of readily-available execution engines.
Approach:	They propose a unified benchmark on meaning representations by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines.
Outcome:	The proposed benchmark combines existing parsing datasets, completes missing logical forms, and implements missing execution engines.

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Haoran Luo, Haihong E, Zichen Tang, Shiyao Peng, Yikai Guo, Wentai Zhang, Chenghao Ma, Guanting Dong, Meina Song, Wei Lin, Yifan Zhu, Anh Tuan Luu

Neural Machine Translation for Bilingually Scarce Scenarios: a Deep Multi-Task Learning Approach (N18-1)

Challenge:	Existing KBQA methods address inefficient knowledge retrieval and semantic parsing errors.
Approach:	They propose a generatethen-retrieve KBQA framework that generates logical form and replaces entities and relations with an unsupervised retrieval method to improve both generation and retrieval more directly.
Outcome:	Experimental results show that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets, WebQSP, and CWQ.

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Poorya Zaremoodi, Gholamreza Haffari

DialSQL: Dialogue Based Structured Query Generation (P18-1)

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.

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Izzeddin Gur, Semih Yavuz, Yu Su, Xifeng Yan

XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages (2023.findings-emnlp)

Challenge:	Recent advances in deep learning and semantic parsing have improved the translation accuracy of natural language questions to structured queries.
Approach:	They propose a dialogue-based structured query generation framework that leverages human intelligence to boost performance of existing algorithms via user interaction.
Outcome:	The proposed framework improves on a WikiSQL dataset from 61.3% to 69.0% using only 2.4 validation questions per dialogue.

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Sebastian Ruder, Jonathan Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David Adelani, Vera Axelrod, Isaac Caswell, Colin Cherry, Dan Garrette, Reeve Ingle, Melvin Johnson, Dmitry Panteleev, Partha Talukdar

Improving compositional generalization for multi-step quantitative reasoning in question answering (2022.emnlp-main)

Challenge:	Existing datasets are often informed by established research directions in the NLP community.
Approach:	They propose a benchmark to evaluate the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks.
Outcome:	The proposed benchmark evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks.

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Armineh Nourbakhsh, Cathy Jiao, Sameena Shah, Carolyn Rosé

Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base (2022.findings-naacl)

Challenge:	Quantitative reasoning is an important aspect of question answering when numeric and verbal cues interact to indicate sophisticated, multi-step programs.
Approach:	They propose a method that encourages QA models to adjust attention patterns and capture input/output alignments that are meaningful to the reasoning task.
Outcome:	The proposed approach improves program accuracy and renders models more robust against overfitting as the number of reasoning steps grows.

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Minhao Zhang, Ruoyu Zhang, Yanzeng Li, Lei Zou

The Role of Semantic Parsing in Understanding Procedural Text (2023.findings-eacl)

Challenge:	Existing methods for knowledge base question answering lack causality modeling . previous work fails to model such causalities in their pipeline .
Approach:	They propose a causal-enhanced table-filler to overcome sequence-modelling issues . they propose an efficient beam-search algorithm to scale complex queries on large-scale KBs.
Outcome:	Experiments on LC-QuAD 1.0 show that the proposed method surpasses state-of-the-arts by a large margin while remaining time and space efficient.

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Hossein Rajaby Faghihi, Parisa Kordjamshidi, Choh Man Teng, James Allen

Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts (2022.emnlp-main)

Challenge:	Inferring actions and their impact on entities involved in a procedural text can be challenging in various aspects.
Approach:	They propose a symbolic parser and semantic role labeling as two sources of semantic parsing knowledge.
Outcome:	The proposed framework integrates semantic parsing knowledge into state-of-the-art neural models and shows that it improves procedural understanding.

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Ben Zhou, Kyle Richardson, Xiaodong Yu, Dan Roth

ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering (2022.coling-1)

Challenge:	Existing approaches to end-to-end questionanswering assume that pre-trained language can decompose complex tasks into more straightforward sub-tasks.
Approach:	They propose to use distant supervision to train decomposition-based transformers for large-scale parallel news.
Outcome:	The proposed model improves on semantic parsing and on hotpotQA and strategyQA datasets by 20% to 30%.

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Yu Gu, Yu Su

Semantic Parsing of Disfluent Speech (2021.eacl-main)

Challenge:	Existing ranking-based KBQA models struggle with flexibility in predicting complicated queries and have impractical running time.
Approach:	They propose a new generation-based question answering on knowledge bases model that addresses both large search space and ambiguities in schema linking.
Outcome:	The proposed model overcomes two intertwined challenges on popular KBQA datasets and is highly competitive and efficient.

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Priyanka Sen, Isabel Groves

Semantic Frame Parsing for Information Extraction : the CALOR corpus (L18-1)

Challenge:	Semantic parsing is a key component for understanding user utterances in voice assistants . however, most research on disfluent speech is focused on written text .
Approach:	They investigate semantic parsing of disfluent speech with the ATIS dataset . they add real and synthetic disfluencies at training time to improve model performance .
Outcome:	The proposed parser outperforms the state-of-the-art parsers on the ATIS dataset in terms of performance and accuracy.

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Gabriel Marzinotto, Jeremy Auguste, Frederic Bechet, Geraldine Damnati, Alexis Nasr

Information Extraction & Text Mining

Challenge:	a recent study compares the semantic parsing of encyclopedic history texts with the Berkeley FrameNet project.
Approach:	They propose to use Berkeley FrameNet to parse encyclopedic history texts . they use a sequence labeling model which optimizes frame identification and role segmentation .
Outcome:	The proposed approach leverages the manual annotation of larger corpora than full text parsing.

Cross-lingual CCG Induction (N19-1)

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Kilian Evang

Low-Resource NLP

Challenge:	Combinatory categorial grammars are linguistically motivated and useful for semantic parsing, but costly to acquire in a supervised way and difficult to acquire unsupervised.
Approach:	They propose an alternative using a source-language parser and a parallel corpus to induce a grammar and parsing model for a target language.
Outcome:	The proposed model outperforms POS tags on 3 out of 8 languages and unsupervised CCG induction on 6 out of 8.

Backpropagating through Structured Argmax using a SPIGOT (P18-1)

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Hao Peng, Sam Thomson, Noah A. Smith

GRAMMAR-LLM: Grammar-Constrained Natural Language Generation (2025.findings-acl)

Challenge:	Structured projection of intermediate gradients (SPIGOT) is a new method for backpropagating through neural networks . structure-based learning methods for natural language processing are increasingly dominated by end-to-end differentiable functions .
Approach:	They propose a structured projection of intermediate gradients method for backpropagating through neural networks that includes hard-decision structured predictions in intermediate layers.
Outcome:	The proposed method improves on two structured NLP pipelines: syntactic-then-semantic dependency parsing and semantic parser followed by sentiment classification.

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Gabriele Tuccio, Luana Bulla, Maria Madonia, Aldo Gangemi, Misael Mongiovì

Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)

Challenge:	Existing approaches to fine-tuning and prompting are insufficient to ensure compliance with predefined taxonomies, syntactic structures, or domain-specific rules.
Approach:	They propose a framework that integrates formal grammatical constraints into the decoding process to enforce syntactic correctness in linear time while maintaining expressiveness in grammar rule definition.
Outcome:	The proposed framework enforces syntactic correctness in linear time while maintaining expressiveness in grammar rule definition.

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Jun Yang, Huipeng Ma, Chenhao Li, Luan Zhang, Yading Li, Xudong Li, Shenxi Liu, Jing Jiang

Few-Shot Novel Concept Learning for Semantic Parsing (2021.findings-emnlp)

Challenge:	Existing retrieval-augmented approaches focus on ignoring the structural information of the Knowledge Base (KB) and the question.
Approach:	They propose a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question’s structure, along with semantic relevance.
Outcome:	Experiments on GrailQA, WebQSP, and GraphQuestions show that the proposed framework achieves state-of-the-art performance.

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Soham Dan, Osbert Bastani, Dan Roth

Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback (2020.acl-main)

Challenge:	Existing deep learning algorithms typically require thousands of examples to learn novel concepts.
Approach:	They propose an algorithm for learning novel concepts by representing them as programs over existing concepts.
Outcome:	The proposed approach outperforms end-to-end neural semantic parsers in a few-shot novel concept learning setting.

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Ahmed Elgohary, Saghar Hosseini, Ahmed Hassan Awadallah

Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing (D18-1)

Challenge:	a natural language interface (NLI) can be used to correct semantic parsing errors . human correction accuracy is 81.5%, but the best model achieves only 25.1% .
Approach:	They propose a task where humans can provide free-form natural language feedback to correct a system when it generates an inaccurate interpretation of an initial utterance.
Outcome:	The proposed model improves the parsing accuracy while maintaining flexibility of natural language interaction.

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Jonathan Herzig, Jonathan Berant

SLOG: A Structural Generalization Benchmark for Semantic Parsing (2023.emnlp-main)

Challenge:	Existing methods for training semantic parsers in new domains require expensive supervision and lack the ability to generalize to new domain.
Approach:	They propose a zero-shot approach to parsing utterances in unseen domains . they map an utterant to an abstract, domain independent, logical form and replace slots with KB constants based on lexical alignment scores and global inference .
Outcome:	The proposed model achieves 53.4% accuracy on 7 domains in the OVERNIGHT dataset, significantly better than other zero-shot baselines and performs as good as a parser trained on over 30% of the target domain examples.

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Bingzhi Li, Lucia Donatelli, Alexander Koller, Tal Linzen, Yuekun Yao, Najoung Kim

DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue (2023.acl-long)

Challenge:	Existing compositional generalization benchmarks focus on lexical generalisation, the interpretation of novel lexicals in syntactic structures familiar from training.
Approach:	They propose a semantic parsing dataset that extends COGS with 17 structural generalization cases to evaluate how well models generalize to new complex linguistic expressions.
Outcome:	The proposed model generalization accuracy is far below the near-perfect accuracy of existing models on COGS, demonstrating the role of SLOG in foregrounding the large discrepancy between models’ lexical and structural generalization capacities.

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William Held, Christopher Hidey, Fei Liu, Eric Zhu, Rahul Goel, Diyi Yang, Rushin Shah

Robustness in NLP

Challenge:	Existing studies show that multilingual models are less robust for semantic parsing compared to other tasks.
Approach:	They propose a constrained optimization technique to optimize multilingual parsing systems for multilingual use.
Outcome:	The proposed technique outperforms XLM-R and mT5-Large on three benchmarks and significantly outperformed other models.

Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization (P19-1)

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Hai Ye, Wenjie Li, Lu Wang

Compositional Generalization with Grounded Language Models (2024.findings-acl)

Challenge:	Semantic parsing aims to transform natural language utterances into formal meaning representations (MRs) whereas an NL generator achieves the reverse, the two tasks are often studied separately.
Approach:	They propose a method of dual information maximization to regularize the learning process by matching the joint distributions of p and q of NLs.
Outcome:	The proposed method empirically maximizes the variational lower bounds of expected joint distributions of NL and MRs.

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Sondre Wold, Étienne Simon, Lucas Charpentier, Egor Kostylev, Erik Velldal, Lilja Øvrelid

Learning to Synthesize Data for Semantic Parsing (2021.naacl-main)

Challenge:	Existing methods for combining language models with knowledge graphs struggle with generalization to sequences of unseen lengths and novel combinations of seen base components.
Approach:	They propose a procedure for generating natural language questions paired with knowledge graphs that targets different aspects of compositionality and avoids grounding models in information already encoded in their weights.
Outcome:	The proposed method fails to generalize to unseen lengths and to novel combinations of seen base components.

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Bailin Wang, Wenpeng Yin, Xi Victoria Lin, Caiming Xiong

FLIN: A Flexible Natural Language Interface for Web Navigation (2021.naacl-main)

Challenge:	Existing methods for synthesizing data for semantic parsing require handcrafted rules to synthesize new programs or utterance-program pairs.
Approach:	They propose to use a (non-neural) PCFG to model the composition of programs and a BART-based translation model to map a program to an utterance to learn a generative model from existing data.
Outcome:	The proposed model can be efficiently learned from existing data on benchmarks of GeoQuery and Spider.

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Sahisnu Mazumder, Oriana Riva

Challenge:	Existing semantic parsing and slot-filling techniques cannot adapt to many different websites without being constantly re-trained.
Approach:	They propose a natural language interface for web navigation that maps user commands to concept-level actions rather than low-level UI actions.
Outcome:	The proposed interface can adapt to new websites in a given domain.

Improving Compositional Generalization in Semantic Parsing (2020.findings-emnlp)

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Inbar Oren, Jonathan Herzig, Nitish Gupta, Matt Gardner, Jonathan Berant

ZOGRASCOPE: A New Benchmark for Semantic Parsing over Property Graphs (2025.findings-emnlp)

Challenge:	Generalization of models to out-of-distribution data has sparked substantial interest . compositional generalization is the ability to systematically generalize to test examples composed of components seen during training .
Approach:	They propose to extend compositional generalization in semantic parsing by using contextual representations and training attention to agree with pre-computed token alignments.
Outcome:	The proposed extensions improve compositional generalization on OOD compositions.

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Francesco Cazzaro, Justin Kleindienst, Sofia Márquez Gomez, Ariadna Quattoni

Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog (2021.naacl-main)

Challenge:	PGs are increasingly used in knowledge graphs, but they are underrepresented in research . a benchmark is designed specifically for PG and queries written in Cypher.
Approach:	They propose a benchmark specifically for PGs and queries written in Cypher.
Outcome:	The proposed benchmark is designed specifically for PGs and queries written in Cypher.

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Arun Babu, Akshat Shrivastava, Armen Aghajanyan, Ahmed Aly, Angela Fan, Marjan Ghazvininejad

Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing (2022.emnlp-main)

Challenge:	Semantic parsing using sequence-to-sequence models is stymied by higher compute requirements and higher latency.
Approach:	They propose a non-autoregressive approach to predict semantic parse trees with an efficient seq2seq model architecture.
Outcome:	The proposed architecture achieves an 81% reduction in latency on TOP dataset and retains competitive performance over non-pretrained models on three different semantic parsing datasets.

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Zhenwen Li, Jiaqi Guo, Qian Liu, Jian-Guang Lou, Tao Xie

Text-to-Text Extraction and Verbalization of Biomedical Event Graphs (2022.coling-1)

Challenge:	Existing studies show that the design of Meaning Representation (MR) greatly influences the final model performance of a neural semantic parser.
Approach:	They propose a data-aware metric called ISS to measure the final performance of MRs.
Outcome:	The proposed metric denoting incremental structural stability (ISS) of MRs can be used as an indicator for MR design to avoid the costly training-testing process.

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Giacomo Frisoni, Gianluca Moro, Lorenzo Balzani

Information Extraction & Text Mining

Challenge:	Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature.
Approach:	They propose a framework to solve event extraction and event verbalization with a unified text-to-text approach.
Outcome:	The proposed framework achieves greater state-of-the-art performance than single-task competitors and can generate coherent natural language utterances from structured data.

DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs (N19-1)

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Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner

Reasoning

Challenge:	a large body of work has highlighted the brittleness of reading comprehension systems . a crowdsourced, adversarially-created, 55k-question benchmark requires a more comprehensive understanding of paragraphs .
Approach:	They propose a reading comprehension benchmark that requires Discrete Reasoning over the content of paragraphs.
Outcome:	The proposed benchmarks show that the best systems only achieve 38.4% F1 on the generalized accuracy metric, while human performance is 96%.

Making Transformers Solve Compositional Tasks (2022.acl-long)

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Santiago Ontanon, Joshua Ainslie, Zachary Fisher, Vaclav Cvicek

Object-oriented Neural Programming (OONP) for Document Understanding (P18-1)

Challenge:	Several studies have reported the inability of Transformer models to generalize compositionally . a key aspect of natural language is the ability to learn basic primitives .
Approach:	They propose to use Transformers to generalize compositionally in a large range of tasks . they find that Transformers generalize significantly better than previous models .
Outcome:	The proposed models generalize compositionally significantly better than previous models . a set of 12 datasets shows that the proposed models can be improved .

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Zhengdong Lu, Xianggen Liu, Haotian Cui, Yukun Yan, Daqi Zheng

Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem (2020.findings-emnlp)

Challenge:	Object-oriented Neural Programming (OONP) is a framework for semantically parsing documents in domains.
Approach:	They propose a framework for semantically parsing documents in specific domains using OONP . OOPN parsers use a rich family of operations to represent the semantics of the document .
Outcome:	The proposed framework can learn to handle fairly complicated ontology with training data of modest sizes.

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Shucheng Li, Lingfei Wu, Shiwei Feng, Fangli Xu, Fengyuan Xu, Sheng Zhong

Dependency-based Hybrid Trees for Semantic Parsing (D18-1)

Challenge:	Graph2Tree model encodes graph-structured input and decodes tree-structures output.
Approach:	They propose a novel Graph-to-Tree Neural Network consisting of a graph encoder and a hierarchical tree decoder that encodes an augmented graph-structured input and decodes a tree-structure-output.
Outcome:	The proposed model outperforms or matches the performance of other state-of-the-art models on two problems, neural semantic parsing and math word problem.

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Zhanming Jie, Wei Lu

QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback based Self-Correction (2024.acl-long)

Challenge:	Existing models for semantic parsing focus on structure-based models, but none deal with dependency information.
Approach:	They propose a dependency-based hybrid tree model which converts natural language utterances into machine interpretable meaning representations.
Outcome:	The proposed model achieves state-of-the-art performance across eight languages and is highly tractable inferenceable.

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Xiang Huang, Sitao Cheng, Shanshan Huang, Jiayu Shen, Yong Xu, Chaoyun Zhang, Yuzhong Qu

Semantic graph parsing with recurrent neural network DAG grammars (D19-1)

Challenge:	Existing methods for semantic parsing fail when hallucinations are encountered . QueryAgent solves a question step-by-step and performs stepwise self-correction .
Approach:	They propose a framework that solves a query step-by-step and performs stepwise self-correction.
Outcome:	The proposed framework outperforms existing methods on GrailQA and GraphQ by 5.7 and 15.0 points.

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Federico Fancellu, Sorcha Gilroy, Adam Lopez, Mirella Lapata

Leveraging Code to Improve In-Context Learning for Semantic Parsing (2024.naacl-long)

Challenge:	Semantic parsing is the task of mapping natural language to machine interpretable meaning representations.
Approach:	They propose a graph-aware sequence model that generates only well-formed graphs . their model is based on a multilingual semantic graphbank .
Outcome:	The proposed model yields competitive results in English and establishes the first results for German, Italian and Dutch.

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Ben Bogin, Shivanshu Gupta, Peter Clark, Ashish Sabharwal

Transferring Knowledge from Structure-aware Self-attention Language Model to Sequence-to-Sequence Semantic Parsing (2022.coling-1)

Challenge:	In-context learning is an attractive approach for semantic parsing, but learning to parse to rare domain-specific languages from a few demonstrations is challenging.
Approach:	They propose to use Python instead of DSLs to augment prompts with a structured domain description.
Outcome:	The proposed approach improves accuracy and generalization across three datasets.

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Ran Ji, Jianmin Ji

Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering (C18-1)

Challenge:	Semantic parsing aims to map a natural language sentence into a machine executable formal representation.
Approach:	They propose a structure-aware self-attention language model to capture structural information of target representations and propose incorporating it into a seq2seq model.
Outcome:	The proposed model improves the baseline model on four semantic parsing and Python code generation tasks.

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Daniil Sorokin, Iryna Gurevych

Structured Data in NLP

Challenge:	Existing approaches to Knowledge Base Question Answering focus on semantic parsing . previous work focused on selecting the correct semantic relations and not on the structure of the semantic parses .
Approach:	They propose to use Gated Graph Neural Networks to encode the graph structure of the semantic parse.
Outcome:	The proposed approach outperforms baseline models that do not explicitly model the structure.

Insertion-based Tree Decoding (2021.findings-acl)

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Denis Lukovnikov, Asja Fischer

Diverse In-Context Example Selection After Decomposing Programs and Aligned Utterances Improves Semantic Parsing (2025.naacl-long)

Challenge:	Existing non-autoregressive decoders that are sub-linear can speed up inference for longer sequences.
Approach:	They propose a sub-linear nonautoregressive tree decoder that uses tree-based insertion operations to generate trees in sub-lines . they evaluate their approach on semantic parsing and compare it against strong baselines .
Outcome:	The proposed approach achieves competitive accuracies while reducing the number of decoding steps.

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Mayank Kothyari, Sunita Sarawagi, Soumen Chakrabarti, Gaurav Arora, Srujana Merugu

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

Challenge:	Large language models (LLMs) are well suited for seq2seq translation . a lack of pretraining corpora can hinder the use of LLMs for structured interpretation .
Approach:	They propose to decompose available ICE trees into fragments and use additional invocations to map them to corresponding utterances.
Outcome:	The proposed method shows visible gains on diverse parsing benchmarks on popular languages.

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Shuo Huang, Zhuang Li, Lizhen Qu, Lei Pan

Disentangled Sequence to Sequence Learning for Compositional Generalization (2022.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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Hao Zheng, Mirella Lapata

Semantic Parsing for Task Oriented Dialog using Hierarchical Representations (D18-1)

Challenge:	Existing models struggle to generalize to unseen compositions of seen components . a new approach allows for disentangled representations and better generalization .
Approach:	They propose an extension to sequence-to-sequence models which encourage disentanglement by re-encoding source input.
Outcome:	The proposed extension delivers better generalization and more disentangled representations . human expressions can be understood by combining known atomic components .

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Sonal Gupta, Rushin Shah, Mrinal Mohit, Anuj Kumar, Mike Lewis

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

Challenge:	Existing work on task oriented dialog systems has limited expressive power to one intent per query and one slot label per token.
Approach:	They propose a hierarchical annotation scheme for semantic parsing that allows representation of compositional queries.
Outcome:	The proposed representation outperforms sequence-to-sequence approaches on a 44k annotated query dataset.

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Zhuang Li, Lizhen Qu, Gholamreza Haffari

Leveraging Data Recasting to Enhance Tabular Reasoning (2022.findings-emnlp)

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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Aashna Jena, Vivek Gupta, Manish Shrivastava, Julian Eisenschlos

KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering (2025.findings-emnlp)

Challenge:	Existing approaches to create tabular inference data are limited by human annotation and synthetic generation.
Approach:	They propose a framework for semi-automatically recasting tabular data to make use of both approaches.
Outcome:	The proposed framework can be used to build tabular NLI instances from five datasets.

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Yushi Sun, Kai Sun, Yifan Ethan Xu, Xiao Yang, Xin Luna Dong, Nan Tang, Lei Chen

Structured Data in NLP

Challenge:	Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing to retrieve knowledge strictly necessary for answer generation.
Approach:	They propose a retrieval-filtering-summarization pipeline that enhances QA coverage by retrieving a broader subgraph likely to contain relevant information.
Outcome:	The proposed pipeline surpasses state-of-the-art solutions by about 7% in quality and exceeds GPT-4o (Tool) by 10-21%.

Structural generalization is hard for sequence-to-sequence models (2022.emnlp-main)

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Yuekun Yao, Alexander Koller

Graph-Based Decoding for Task Oriented Semantic Parsing (2021.findings-emnlp)

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.

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Jeremy Cole, Nanjiang Jiang, Panupong Pasupat, Luheng He, Peter Shaw

Context Dependent Semantic Parsing over Temporally Structured Data (N19-1)

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.

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Charles Chen, Razvan Bunescu

A Double-Graph Based Framework for Frame Semantic Parsing (2022.naacl-main)

Challenge:	Existing semantic parsing tools only allow for natural language interactions, but the graphical interface could be improved significantly.
Approach:	They propose a semantic parsing setting that allows users to query the system using both natural language questions and actions within a graphical user interface.
Outcome:	The proposed architecture outperforms standard sequence generation baselines and achieves sequence-level accuracy of 88.7% on artificial data and 74.8% on real data.

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Ce Zheng, Xudong Chen, Runxin Xu, Baobao Chang

A Pilot Study for Chinese SQL Semantic Parsing (D19-1)

Challenge:	Frame semantic parsing is a fundamental NLP task, which consists of three subtasks: frame identification, argument identification and role classification.
Approach:	They propose a frame semantic parser with a double-graph to derive knowledge-enhanced representations for frames and FEs.
Outcome:	The proposed method outperforms the state-of-the-art method by up to 1.7 F1-score on two FrameNet datasets.

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Qingkai Min, Yuefeng Shi, Yue Zhang

Lexicon Learning for Few Shot Sequence Modeling (2021.acl-long)

Challenge:	Existing datasets to map natural language text into SQL are limited in their use in question-to-sql mapping.
Approach:	They propose to use a Chinese-based semantic parser to map natural language text into SQL.
Outcome:	The proposed dataset compares a character-based parser with a word embedding scheme for Chinese . the results show that the parsers are subject to segmentation errors and cross-lingual embedders are useful for text-to-SQL mapping.

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Ekin Akyurek, Jacob Andreas

Language Models

Challenge:	Past work has shown that many failures of systematic generalization arise from neural models’ inability to disentangle lexical phenomena from syntactic ones.
Approach:	They propose a lexical translation mechanism that generalizes existing copy mechanisms to incorporate learned, decontextualized, token-level translation rules.
Outcome:	The proposed model improves generalization on a diverse set of sequence modeling tasks drawn from cognitive science, formal semantics, and machine translation.

Analysis of Tree-Structured Architectures for Code Generation (2021.findings-acl)

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Samip Dahal, Adyasha Maharana, Mohit Bansal

Few-Shot Semantic Parsing with Language Models Trained on Code (2022.naacl-main)

Challenge:	Code generation is the task of generating code snippets from input user specifications written in natural language (NL).
Approach:	They evaluate the significance of input parse trees for code generation by using constituency-based parsers as input and an abstract syntax tree as the target.
Outcome:	The proposed models on a Python-based code generation dataset and a semantic parsing dataset show that constituency trees encoded using a structure-aware model improve performance.

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Richard Shin, Benjamin Van Durme

From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding (2021.acl-long)

Challenge:	Large language models can perform semantic parsing with little training data, when prompted with in-context examples.
Approach:	They propose to map natural language to a controlled natural language-like representation . they find that OpenAI Codex performs better on such tasks than equivalent GPT-3 models .
Outcome:	The proposed model performs better on large parsing tasks than GPT-3 models on Overnight and SMCalFlow.

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Shan Wu, Bo Chen, Chunlei Xin, Xianpei Han, Le Sun, Weipeng Zhang, Jiansong Chen, Fan Yang, Xunliang Cai

Multimodal Contextualized Semantic Parsing from Speech (2024.acl-long)

Challenge:	Experimental results show that Synchronous Semantic Decoding (SSD) can achieve state-of-the-art unsupervised semantic parsing performance on multiple datasets.
Approach:	They propose an unsupervised method which solves the semantic gap and the structure gap by leveraging paraphrasing and grammar-constrained decoding.
Outcome:	The proposed method can solve the semantic gap and structure gap on multiple datasets.

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Jordan Voas, David Harwath, Ray Mooney

Parsing All: Syntax and Semantics, Dependencies and Spans (2020.findings-emnlp)

Challenge:	Towards this goal, we introduce Semantic Parsing in Contextual Environments (SPICE) task designed to enhance artificial agents’ contextual awareness by integrating multimodal inputs with prior contexts.
Approach:	They introduce a task designed to enhance artificial agents’ contextual awareness by integrating multimodal inputs with prior contexts.
Outcome:	The proposed task is based on the VG-SPICE dataset and the Audio-Vision Dialogue Scene Parser (AViD-SP) it allows agents to maintain their contextual state within a structured, dense information framework that is scalable and interpretable .

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Junru Zhou, Zuchao Li, Hai Zhao

MC2: A Minimum-Coverage and Dataset-Agnostic Framework for Compositional Generalization of LLMs on Semantic Parsing (2025.findings-emnlp)

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.

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Ziyao Xu, Zhe Yang, Houfeng Wang

Learning to Retrieve Iteratively for In-Context Learning (2024.emnlp-main)

Challenge:	Existing research relies on dataset-specific designs or a large number of samples to improve compositional generalization of large language models (LLMs) .
Approach:	They propose a minimum-coverage framework that can help LLMs achieve compositional generalization by selecting and organizing samples that satisfy the primitive coverage.
Outcome:	The proposed framework can improve compositional generalization on different parsing datasets in the minimum-coverage setting.

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Yunmo Chen, Tongfei Chen, Harsh Jhamtani, Patrick Xia, Richard Shin, Jason Eisner, Benjamin Van Durme

Conversational Semantic Parsing (2020.emnlp-main)

Challenge:	In-context learning is a powerful tool for learning large language models.
Approach:	They propose an iterative retrieval framework that empowers retrievers to make iterable decisions through policy optimization.
Outcome:	The proposed framework outperforms existing methods on semantic parsing datasets with 4M additional parameters for state encoding.

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Armen Aghajanyan, Jean Maillard, Akshat Shrivastava, Keith Diedrick, Michael Haeger, Haoran Li, Yashar Mehdad, Veselin Stoyanov, Anuj Kumar, Mike Lewis, Sonal Gupta

A Two-Stage Approach towards Generalization in Knowledge Base Question Answering (2022.findings-emnlp)

Challenge:	Structured representations for task-oriented assistant systems are limited due to the limitations of the representation.
Approach:	They propose a semantic representation for task-oriented conversational systems that can represent co-reference and context carryover.
Outcome:	The proposed model improves the best results on ATIS, SNIPS, TOP and DSTC2 by up to 5 points for slot-carryover.

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Srinivas Ravishankar, Dung Thai, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Tahira Naseem, Pavan Kapanipathi, Gaetano Rossiello, Achille Fokoue

Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data (D18-1)

Challenge:	Existing approaches for Knowledge Base Question Answering focus on a specific knowledge base or evaluating it on underlying knowledge base requires non-trivial changes.
Approach:	They propose a framework that separates semantic parsing from knowledge base interaction . they propose KBQA framework that allows generalization across knowledge bases .
Outcome:	The proposed framework achieves comparable or state-of-the-art performance on datasets with a different knowledge base.

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Zi Lin, Yuguang Duan, Yuanyuan Zhao, Weiwei Sun, Xiaojun Wan

AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing (P19-1)

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.

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Xiang Zhang, Shizhu He, Kang Liu, Jun Zhao

Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings (2020.findings-emnlp)

Challenge:	Semantic parsing (SP) maps a natural language utterance into a formal language . standard Seq2Seq models ignore underlying grammars and may give ill-formed results.
Approach:	They propose an end-to-end model for semantic parsing that transduces a natural language sentence to the formal semantic representation.
Outcome:	The proposed model outperforms the state-of-the-art models and does not need expertise like predefined grammar or sketches in the meantime.

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Prafull Prakash, Saurabh Kumar Shashidhar, Wenlong Zhao, Subendhu Rongali, Haidar Khan, Michael Kayser

CraftAssist Instruction Parsing: Semantic Parsing for a Voxel-World Assistant (2020.acl-main)

Challenge:	Existing task-oriented semantic parsing models use BERT or RoBERTa as pretrained encoders.
Approach:	They propose to learn compositional code embeddings to greatly reduce the sizes of BERT and RoBERTa encoders.
Outcome:	The proposed model reduces the size of BERT and RoBERTa encoders while maintaining performance.

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Kavya Srinet, Yacine Jernite, Jonathan Gray, Arthur Szlam

An Investigation of LLMs’ Inefficacy in Understanding Converse Relations (2023.emnlp-main)

Challenge:	Existing datasets for semantic parsing are limited due to the wide variety of robot setups and proprietary nature of personal assistants.
Approach:	They propose a semantic parsing dataset for Minecraft's voxel-based crafting game . they propose to use 7K human utterances and their corresponding parses to execute them in game if proper world state is provided .
Outcome:	The proposed dataset can be used to analyze human-bot interactions in the game Minecraft.

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Chengwen Qi, Bowen Li, Binyuan Hui, Bailin Wang, Jinyang Li, Jinwang Wu, Yuanjun Laili

Challenge:	Existing benchmarks for Large Language Models (LLMs) follow the data distribution of pre-training data.
Approach:	They propose a benchmark ConvRe focusing on converse relations which contains 17 relations and 1240 triples extracted from popular knowledge graph completion datasets.
Outcome:	The proposed benchmark focuses on converse relations, which contains 17 relations and 1240 triples extracted from popular knowledge graph completion datasets.

Complex Question Decomposition for Semantic Parsing (P19-1)

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Haoyu Zhang, Jingjing Cai, Jianjun Xu, Ji Wang

NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction (2021.naacl-main)

Challenge:	Existing methods that ignore the decompositionality of complex questions are not suitable for complex question semantic parsing.
Approach:	They propose a hierarchical semantic parsing method which utilizes the decompositionality of complex questions for semantic paring.
Outcome:	The proposed method improves on a large scale complex question semantic parsing dataset.

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Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, Ahmed Hassan Awadallah

Sequence-Level Mixed Sample Data Augmentation (2020.emnlp-main)

Challenge:	Existing systems frame semantic parsing as a one-shot translation from a natural language question to the logical form.
Approach:	They propose a model that uses natural language feedback to correct parsers . they show that NL-EDIT can boost the accuracy of existing parser by 20% .
Outcome:	The proposed model can boost parsers' accuracy by 20% with just one turn of correction.

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Demi Guo, Yoon Kim, Alexander Rush

Reranking for Neural Semantic Parsing (P19-1)

Challenge:	Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language.
Approach:	They propose a data augmentation approach to encourage compositional behavior in neural networks . they propose to softly combine input/output sequences from the training set .
Outcome:	The proposed approach yields 1.0 BLEU improvement on translation datasets over baselines.

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Pengcheng Yin, Graham Neubig

Compositional Generalization in Dependency Parsing (2022.acl-long)

Challenge:	Semantic parsing is the task of transducing natural language utterances into machine executable meaning representations (e.g., Python code).
Approach:	They propose to rerank an n-best list of predicted MRs and use features to fix observed problems with baseline models to improve parser performance.
Outcome:	The proposed method outperforms the best published neural parser on four datasets and improves the baseline parsing performance by 5.7% and 2.9%.

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Emily Goodwin, Siva Reddy, Timothy O’Donnell, Dzmitry Bahdanau

Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning (2024.emnlp-main)

Challenge:	Compositionality is the ability to combine familiar units like words into novel phrases and sentences.
Approach:	They introduce a set of dependency parses for Compositional Freebase Queries (CFQ) they analyze the behaviour of a state-of-the-art dependency parser on the CFQ dataset .
Outcome:	The proposed dependency parser performs lower on the most challenging splits with the highest compound divergence.

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Zhili Shen, Pavlos Vougiouklis, Chenxin Diao, Kaustubh Vyas, Yuanyi Ji, Jeff Pan

Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing (2023.acl-long)

Challenge:	Existing methods for text-to-SQL semantic parsing are limited to retrieving schemata based on a single query.
Approach:	They propose a text-to-sql semantic parser that uses abstract syntax trees to select few-shot examples for retrieval-augmented generation.
Outcome:	The proposed model can generate approximated versions of SQL queries in parallel, and shows improvements over state-of-the-art benchmarks.

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Maxwell Crouse, Pavan Kapanipathi, Subhajit Chaudhury, Tahira Naseem, Ramon Fernandez Astudillo, Achille Fokoue, Tim Klinger

World Knowledge for Abstract Meaning Representation Parsing (L18-1)

Challenge:	Compositional generalization is a key feature of human intelligence and has been identified as a major point of weakness in neural methods for semantic parsing.
Approach:	They propose a neural parsing generation method that constructs logical forms from the bottom up, beginning from the logical form’s leaves.
Outcome:	The proposed method outperforms general-purpose parsers on a CFQ dataset and two other Text-to-SQL datasets while also being competitive with parser that have been tailored to each task.

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Charles Welch, Jonathan K. Kummerfeld, Song Feng, Rada Mihalcea

Generating Data for Symbolic Language with Large Language Models (2023.emnlp-main)

Challenge:	Abstract Meaning Representation (AMR) parsers are based on annotated graphs, but there is still room for improvement .
Approach:	They examine the role played by world knowledge in parsing errors in a state-of-the-art parser . they examine the effects of different types of world knowledge on parsers .
Outcome:	The proposed model improves on multiple fine-grained metrics, including a 6% increase in named entity F-score, and provides insight into the potential of world knowledge for future work in Abstract Meaning Representation parsing.

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Jiacheng Ye, Chengzu Li, Lingpeng Kong, Tao Yu

SPOT: Zero-Shot Semantic Parsing Over Property Graphs (2025.findings-acl)

Challenge:	Large language models (LLMs) bring performance and complexity, but they incur a large computational cost in practice.
Approach:	They propose a task-based model which uses large language models to generate symbolic language data by an informative prompt and agreement-based verifier.
Outcome:	The proposed model can generate symbolic language data with a few human demonstrations and saves a considerable amount of inference effort.

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Francesco Cazzaro, Justin Kleindienst, Sofia Márquez Gomez, Ariadna Quattoni

Fixed Input Parameterization for Efficient Prompting (2023.findings-acl)

Challenge:	Knowledge Graphs (KGs) are becoming increasingly popular as a means of storing structured data.
Approach:	They propose a method to generate training data for semantic parsing over Property Graphs without human annotations by matching tree patterns to the KG and paraphrasing the query program with an LLM.
Outcome:	The proposed method generates training data for parsing over Property Graphs without human annotations on two property graph benchmarks utilizing the Cypher query language.

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Eunbi Choi, Yongrae Jo, Joel Jang, Joonwon Jang, Minjoon Seo

Semantic Captioning: Benchmark Dataset and Graph-Aware Few-Shot In-Context Learning for SQL2Text (2025.coling-main)

Challenge:	Recent studies have shown that attaching prompts to the input is effective at conditioning Language Models (LMs) however, prompts are always included in the input text during inference, thus incurring substantial computational and memory overhead.
Approach:	They propose to inject a fixed prompt into the parameters of an LM to be an efficient alternative to attaching fixed prompts to the input.
Outcome:	The proposed method can be up to 280 times more efficient in terms of FLOPs than previous approaches.

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Ali Al Lawati, Jason Lucas, Prasenjit Mitra

Learning Programmatic Idioms for Scalable Semantic Parsing (D19-1)

Challenge:	Large Language Models (LLMs) have shown remarkable performance in various NLP tasks, including semantic parsing, which translates natural language into formal code representations.
Approach:	They propose a semantic captioning task to repurpose semantic parsing datasets for semantic captions.
Outcome:	The proposed model outperforms random selection and other methods by 39% on BLEU score.

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Srinivasan Iyer, Alvin Cheung, Luke Zettlemoyer

Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study (D19-1)

Challenge:	In state-of-the-art semantic parsers map natural language instructions to source code . idioms improve the accuracy of semantic parses, allowing for faster decoding .
Approach:	They propose an iterative method to extract code idioms from large source code corpora . they use most-frequent subtrees of their syntax trees to train semantic parsers to apply them .
Outcome:	The proposed method improves the state-of-the-art semantic parsers' accuracy and training time by more than 50%.

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Ziyu Yao, Yu Su, Huan Sun, Wen-tau Yih

Rule-KBQA: Rule-Guided Reasoning for Complex Knowledge Base Question Answering with Large Language Models (2025.coling-main)

Challenge:	Existing semantic parsing technologies are not well-suited for use in real-world applications.
Approach:	They propose a model-based intelligent agent that generates a clarification question in natural language . they propose 'interactive semantic parsing' with a human user in the loop .
Outcome:	The proposed approach improves both parsing accuracy and user confidence . it is demonstrated on two text-to-SQL datasets with different state-of-the-art parsers .

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Zhiqiang Zhang, Liqiang Wen, Wen Zhao

Learning Relational Decomposition of Queries for Question Answering from Tables (2024.acl-long)

Challenge:	Existing methods for knowledge base question answering lack grammaticality, faithfulness, and controllability due to hallucinations in the reasoning process.
Approach:	They propose a framework that employs learned rules to guide the generation of logical forms.
Outcome:	The proposed method achieves competitive results on standard KBQA datasets.

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Raphaël Mouravieff, Benjamin Piwowarski, Sylvain Lamprier

Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models (2024.acl-long)

Challenge:	Existing approaches to Table Question-Answering focus on generating answers directly from inputs, but there are limitations when executing numerical operations.
Approach:	They propose to imitate a restricted subset of SQL-like algebraic operations and use them to generate a query.
Outcome:	The proposed methods bridge the gap between semantic parsing and direct answering methods and offer valuable insights into which types of operations should be predicted by a generative architecture and which should be executed by an external algorithm.

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Guanming Xiong, Junwei Bao, Wen Zhao

An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing (2020.emnlp-main)

Challenge:	Knowledge base question answering (KBQA) is a challenging task, particularly in parsing intricate questions into executable logical forms.
Approach:	They propose a framework to generate logical forms through direct interaction with knowledge bases (KBs) by annotating a dataset with step-wise reasoning processes.
Outcome:	The proposed framework achieves competitive results on the WebQuestionsSP, ComplexWebQuestIONS, KQA Pro, and MetaQA datasets with a minimal number of examples (shots). Importantly, the proposed model supports manual intervention, allowing for the iterative refinement of LLM outputs.

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Martin Schmitt, Sahand Sharifzadeh, Volker Tresp, Hinrich Schütze

Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database (2022.emnlp-main)

Challenge:	Knowledge graphs (KGs) vary greatly from one domain to another, resulting in a lack of domain-specific parallel graph-text data.
Approach:	They propose an unsupervised approach to graph-to-text generation and text-to graph knowledge extraction using WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome.
Outcome:	The proposed approach outperforms baselines on WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome.

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Ye Liu, Semih Yavuz, Rui Meng, Dragomir Radev, Caiming Xiong, Yingbo Zhou

Question Answering

Challenge:	Existing approaches on semantic parsing suffer from exponential growth of logical form candidates and can hardly generalize to unseen data.
Approach:	They propose a unified semantic parser for question answering on KB and DB . they define the primitive as the essential element in their framework .
Outcome:	The proposed framework can predict logical forms by altering and composing top-ranked primitives with different operations.

Semantic Parsing for English as a Second Language (2020.acl-main)

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Yuanyuan Zhao, Weiwei Sun, Junjie Cao, Xiaojun Wan

Controllable Semantic Parsing via Retrieval Augmentation (2021.emnlp-main)

Challenge:	Existing studies on domain adaptation in NLP focus on learning challenges at the syntax-semantics interface during second language acquisition.
Approach:	They propose to use English Resource Grammar and TLE to parse ESL data using a reranking model to evaluate the quality of the annotations.
Outcome:	The proposed model can obtain a very promising quality in comparison to human annotations.

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Panupong Pasupat, Yuan Zhang, Kelvin Guu

Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing (2020.acl-main)

Challenge:	a mechanism for enacting behavior changes without expensive model re-training would be preferable.
Approach:	They propose a controllable semantic parser that retrieves related exemplars from a retrieval index and augments them to the query.
Outcome:	The proposed model can parse queries in a new domain, adapt predictions toward specified patterns, or adapt to new semantic schemas without re-training the model.

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Ruisheng Cao, Su Zhu, Chenyu Yang, Chen Liu, Rao Ma, Yanbin Zhao, Lu Chen, Kai Yu

Constrained Language Models Yield Few-Shot Semantic Parsers (2021.emnlp-main)

Challenge:	Existing semantic parsing frameworks rely on nontrivial human labor to generate canonical utterances.
Approach:	They propose a framework that uses an unsupervised paraphrase model to parse canonical utterances.
Outcome:	The proposed framework is effective and compatible with supervised training.

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Richard Shin, Christopher Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, Benjamin Van Durme

Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing (2022.emnlp-main)

Challenge:	Large pretrained language models excel at generating natural language, but they are not efficient for task specific semantic parsing.
Approach:	They propose to use large pretrained language models as few-shot semantic parsers . they paraphrase inputs into a controlled sublanguage resembling English .
Outcome:	The proposed model can generate surprisingly accurate models on multiple tasks with minimal code and data.

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Linlu Qiu, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, Kristina Toutanova

Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations (2024.emnlp-main)

Challenge:	Pre-trained language models struggle on out-of-distribution compositional generalization . recent work shows considerable improvements on many NLP tasks from model scaling .
Approach:	They evaluate encoder-decoder models up to 11B parameters and decoder-only models up 540B parameters . they compare scaling curves for fine-tuning, prompt tuning, and in-context learning methods .
Outcome:	The proposed scaling methods improve compositional generalization on many tasks . fine-tuning generally has flat or negative scaling curves on out-of-distribution compositional . larger models are better at modeling the syntax of the output space, the study finds .

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Matthias Lindemann, Alexander Koller, Ivan Titov

Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms (2023.acl-long)

Challenge:	Inductive biases play a critical role in NLP, especially in learning from limited data and generalizing systematically outside of the training distribution.
Approach:	They propose to strengthen the structural inductive bias of a Transformer by intermediate pre-training to perform syntactic transformations of dependency trees given a description of the transformation.
Outcome:	The proposed model can perform syntactic transformations and generalize semantic parsing with attention heads that keep track of which syntaktic transformation needs to be applied to which token.

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Tianshu Zhang, Changchang Liu, Wei-Han Lee, Yu Su, Huan Sun

Learning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context (2020.acl-main)

Challenge:	Neural semantic parsers have achieved remarkable performance in recent years, but they are data-hungry and require annotators to have intimate knowledge of formal programs.
Approach:	They propose a task where multiple clients collaboratively train one global model without sharing their semantic parsing data.
Outcome:	The proposed model improves performance on three widely adopted FL algorithms (FedAvg, FedOPT and FedProx) and clients with smaller datasets enjoy faster performance.

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Shashank Srivastava, Oleksandr Polozov, Nebojsa Jojic, Christopher Meek

Augmenting Reasoning Capabilities of LLMs with Graph Structures in Knowledge Base Question Answering (2024.findings-emnlp)

Challenge:	a new direction for semantic parsing that models explanations to demonstrations is proposed . bottom-up approach to generating logical forms is complicated in domains with rich composition .
Approach:	They propose a new direction for semantic parsing that models explanations in a context . they use inverse semantics to reason backwards from observed demonstrations .
Outcome:	The proposed approach shows better task completion rates than a baseline method . it is competitive with exploration-and-demonstration based methods, but requires no exploration of environment .

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Hao Wang, Jun Yang, Jing Xu, Ruanmin Cao, HaoYu Wang

Extrinsic Evaluation of Machine Translation Metrics (2023.acl-long)

Challenge:	Recent work uses Large Language Models (LLMs) for semantic parsing to address Knowledge Base Question Answering tasks.
Approach:	They propose a framework that augments reasoning capabilities of LLMs with Graph Structures in Knowledge Base Question Answering to retrieve question-related graph structures.
Outcome:	The proposed framework outperforms existing methods on GrailQA and WebQSP under the few-shot setting.

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Nikita Moghe, Tom Sherborne, Mark Steedman, Alexandra Birch

COGS: A Compositional Generalization Challenge Based on Semantic Interpretation (2020.emnlp-main)

Challenge:	MT metrics are widely used to distinguish the quality of machine translation systems across relatively large test sets.
Approach:	They evaluate the segment-level performance of the most widely used MT metrics by correlating them with how useful they are for downstream tasks.
Outcome:	The MT metrics are widely used to distinguish the quality of machine translation systems across relatively large test sets.

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Najoung Kim, Tal Linzen

Explainability & Interpretability in NLP

Challenge:	Natural language is characterized by compositionality: meaning of complex expressions is constructed from the meanings of its constituent parts.
Approach:	They propose a semantic parsing dataset based on a fragment of English to assess compositional generalization abilities.
Outcome:	The proposed model can generalize meanings in a given sentence in 96–99% of the tests, but generalization accuracy is lower and the generalization sensitivity is higher.

Treepiece: Faster Semantic Parsing via Tree Tokenization (2023.findings-emnlp)

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Sid Wang, Akshat Shrivastava, Aleksandr Livshits

Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking (2023.findings-emnlp)

Challenge:	Autoregressive (AR) encoder-decoder neural networks are slow in sequential prediction of natural language to machine-readable parse trees.
Approach:	They propose a technique that tokenizes a parse tree into subtrees and generates one subtrea per decoding step.
Outcome:	The proposed approach shows 4.6 times faster decoding speed and comparable speed but significantly higher accuracy compared to non-autoregressive (NAR) models.

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Yuxiang Wu, Guanting Dong, Weiran Xu

Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing (2020.acl-main)

Challenge:	Existing methods for zero-shot Dialogue State Tracking have focused on domaintransfers and have not yielded satisfactory results.
Approach:	They propose a new In-Context Learning method to introduce additional updating strategies in zero-shot DST by leveraging powerful Large Language Models and translating the original dialogue to JSON through semantic parsing as an intermediate state.
Outcome:	The proposed method outperforms existing zero-shot DST methods on MultiWOZ, showing significant improvements in JGA and slot accuracy compared to existing methods.

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Alane Suhr, Ming-Wei Chang, Peter Shaw, Kenton Lee

TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data (2020.acl-main)

Challenge:	Existing evaluation datasets such as Spider are used to support cross-database semantic parsing . XSP systems that map natural language utterances to SQL queries are evaluated on databases unseen during training.
Approach:	They propose a setup that uses eight well-studied datasets to evaluate cross-database semantic parsing systems.
Outcome:	The proposed system performs well on spider, but struggles to generalize to the repurposed set.

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Pengcheng Yin, Graham Neubig, Wen-tau Yih, Sebastian Riedel

STARE at the Structure: Steering ICL Exemplar Selection with Structural Alignment (2025.emnlp-main)

Challenge:	Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language understanding tasks.
Approach:	They propose a pretrained language model that jointly learns representations for NL sentences and (semi-)structured tables.
Outcome:	The proposed model performs best on the weakly-supervised semantic parsing benchmark WikiTableQuestions while performing competitively on the text-to-SQL dataset Spider.

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Jiaqian Li, Qisheng Hu, Jing Li, Wenya Wang

Evaluating Structural Generalization in Neural Machine Translation (2024.findings-acl)

Challenge:	Existing methods for incontext learning often overlook structural alignment, leading to poor generalization and suboptimal performance.
Approach:	They propose a two-stage exemplar selection strategy that achieves a strong balance between efficiency, generalizability and performance.
Outcome:	The proposed method outperforms baselines on semantic parsing tasks on four benchmarks.

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Ryoma Kumon, Daiki Matsuoka, Hitomi Yanaka

Ambiguous Learning from Retrieval: Towards Zero-shot Semantic Parsing (2023.acl-long)

Challenge:	Existing studies have focused on compositional generalization with semantic parsing, but it remains unclear to what extent models can translate sentences that require structural generalization.
Approach:	They construct a machine translation dataset that measures compositional generalization with control of words and sentence structures.
Outcome:	The proposed model struggle more in structural generalization than in compositional generalization.

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Shan Wu, Chunlei Xin, Hongyu Lin, Xianpei Han, Cao Liu, Jiansong Chen, Fan Yang, Guanglu Wan, Le Sun

When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems (2022.emnlp-main)

Challenge:	Existing neural semantic parsers require a large amount of training data which is expensive and difficult to obtain.
Approach:	They propose a framework for a supervised retrieval system based on pretrained language models . they propose ambiguous supervision to improve the precision and coverage of the task .
Outcome:	The proposed approach outperforms state-of-the-art zero-shot parsing methods in ambiguous supervision.

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Elias Stengel-Eskin, Emmanouil Antonios Platanios, Adam Pauls, Sam Thomson, Hao Fang, Benjamin Van Durme, Jason Eisner, Yu Su

Challenge:	In natural language understanding systems, users’ evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space.
Approach:	They propose to use a small set of new symbols to build broad-coverage NLU systems.
Outcome:	The proposed model is based on two prototypical NLU tasks: intent recognition and semantic parsing.

Cross-lingual Back-Parsing: Utterance Synthesis from Meaning Representation for Zero-Resource Semantic Parsing (2024.emnlp-main)

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Deokhyung Kang, Seonjeong Hwang, Yunsu Kim, Gary Lee

Compositional Generalization without Trees using Multiset Tagging and Latent Permutations (2023.acl-long)

Challenge:	Existing approaches to extend semantic parsing (SP) beyond English are challenging due to the complex slot alignment step after translation.
Approach:	They propose a method to enhance cross-lingual transfer for SP by utilizing mPLMs.
Outcome:	The proposed method synthesizes target language utterances from source meaning representations while maintaining high slot value alignment rates.

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Matthias Lindemann, Alexander Koller, Ivan Titov

Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization (2021.emnlp-main)

Challenge:	Seq2seq models struggle with compositional generalization in semantic parsing, i.e. generalizing to unseen compositions or deeper recursion of phenomena that the model handles correctly in isolation.
Approach:	They propose a new way of parameterizing and predicting permutations by combining input tokens with multisets of output tokens and a method to backpropagate through the solver.
Outcome:	The proposed model outperforms pretrained models and prior work on realistic semantic parsing tasks that require generalization to longer examples.

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Inbar Oren, Jonathan Herzig, Jonathan Berant

Interactive Semantic Parsing with Reinforcement Learning for Knowledge Graph Reasoning (2026.findings-acl)

Challenge:	Recent research shows that automatic generation of synthetic utterance-program pairs can alleviate the first problem, but its potential for the second has thus far been under-explored.
Approach:	They propose to generate synthetic utterance-program pairs for improving compositional generalization in semantic parsing by using structurally-diverse examples.
Outcome:	The proposed approach leads to dramatic improvements in compositional generalization and moderate improvements in the traditional i.i.d setup.

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Yurun Song, Xiangqing Shen, Jianfei Yu, Rui Xia

Soft Well-Formed Semantic Parsing with Score-Based Selection (2024.lrec-main)

Challenge:	Existing approaches to improve LLM reliability rely on factual hallucinations . Existing methods rely only on graph traversal, resulting in imprecise retrieval and heavy post-processing burdens.
Approach:	They propose a framework that integrates knowledge Graphs as structured, high-fidelity buffers to enhance LLM reliability.
Outcome:	The proposed framework allows logical constraints to be dynamically interleaved with graph search while optimizing via reinforcement learning with only final answer feedback eliminates the need for gold program annotations.

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Jiangming Liu

Execution as Verification: Fine-Grained Self-Correcting Reasoning for Complex KBQA (2026.acl-long)

Challenge:	Semantic parsing is the task of translating natural language into a structured, formal semantic representation that can be interpreted by machines.
Approach:	They propose a score-based method to select well-formed outputs from candidates generated by beam search algorithms.
Outcome:	The proposed method reduces the number of ill-formed outputs and improves F1 scores in English.

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Minghan Zhang, Zhen Yang, Haodong Zou, Jie Chen, Zhen Duan, Shu Zhao