Papers with SP
Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning (2022.coling-1)
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| Challenge: | Semantic parsing (SP) is a core component of modern virtual assistants like Google Assistant and Amazon Alexa. |
| Approach: | They propose a non-autoregressive (NAR) semantic parser that introduces intent conditioning on the decoder. |
| Outcome: | The proposed model reduces inference latency while maintaining competitive parsing quality. |
EMNLP: Educator-role Moral and Normative Large Language Models Profiling (2025.emnlp-main)
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| Challenge: | Existing frameworks for evaluating the ethical and moral alignment of large language models (LLMs) in educational AI are lacking. |
| Approach: | They propose a framework for a teacher-role moral and normative LLMs profiling . they extend existing scales and construct 88 teacher-specific moral dilemmas . |
| Outcome: | The proposed framework evaluates compliance and vulnerability of teacher-role LLMs under soft prompt injection. |
BanSuite: A Unified Toolkit and Software Platform for Low-Resource NLP in Bangla (2026.eacl-demo)
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Md. Abu Sayed, Faisal Ahamed Khan, Jannatul Ferdous Tuli, Nabeel Mohammed, Mohammad Ruhul Amin, Mohammad Mamun Or Rashid
| Challenge: | Existing efforts to improve Bangla's NLP performance have focused on isolated tasks such as Part-of-Speech tagging and Named Entity Recognition (NER) but comprehensive, integrated systems for core NLP tasks such Shallow Parsing and Dependency Parser are largely absent. |
| Approach: | They propose to integrate a large-scale, manually annotated Bangla Treebank with high-quality pretrained models for POS tagging, NER, shallow parsing, and dependency parse. |
| Outcome: | The proposed system achieves strong in-domain baseline performance while maintaining high efficiency in resource usage. |
Compositional Generalization in Multilingual Semantic Parsing over Wikidata (2022.tacl-1)
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| Challenge: | Semantic parsers are mostly designed for and evaluated on English resources, such as CFQ. |
| Approach: | They propose a method for creating a multilingual, parallel question-query dataset . they analyze compositional generalization of parsers in Hebrew, Kannada, Chinese, and English . |
| Outcome: | The proposed method analyzes compositional generalization of parsers in Hebrew, Kannada, Chinese, and English. |
CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing (2022.aacl-short)
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| Challenge: | Large Language Models excel at a low-resource level given limited data, but are unsuitable for runtime systems which require low latency. |
| Approach: | They propose a method to augment training data for a model 40x smaller (500M parameters) they use Alexa to generate synthetic data from Alexa 20B to augment the training set . |
| Outcome: | The proposed method improves low-resource SP on two datasets in low-source settings. |
Polyglot Semantic Parsing in APIs (N18-1)
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| 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. |
SP-10K: A Large-scale Evaluation Set for Selectional Preference Acquisition (P19-1)
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| Challenge: | Selectional Preference (SP) is a common phenomenon in human language and has been shown to be useful in many natural language processing tasks. |
| Approach: | They propose a large-scale evaluation set that provides human ratings for the plausibility of 10,000 SP pairs over five SP relations, covering 2,500 most frequent verbs, nouns, and adjectives in American English. |
| Outcome: | The proposed evaluation sets provide human ratings for plausibility of 10,000 SP pairs over five SP relations covering 2,500 most frequent verbs, nouns, and adjectives in American English. |
Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models (2026.findings-acl)
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| Challenge: | Existing reinforcement learning methods for large reasoning models suffer from excessive verbosity, known as "overthinking." Existing models penalize generated tokens to promote conciseness, but these methods encounter two challenges: they may develop hacking behavior in later stages of training by discarding reasoning steps. |
| Approach: | They propose a framework that steers large reasoning models toward more efficient reasoning . they prioritize correctness while imposing penalties for redundant steps . |
| Outcome: | The proposed framework reduces token usage by 69.7% on AIME24. |
Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees’ Dialogue to Facilitate Nurse Communication Training (2025.findings-acl)
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Keyeun Lee, Seolhee Lee, Esther Hehsun Kim, Yena Ko, Jinsu Eun, Dahee Kim, Hyewon Cho, Haiyi Zhu, Robert E. Kraut, Eunyoung E. Suh, Eun-mee Kim, Hajin Lim
| Challenge: | standardized patient (SP) simulations are costly and inflexible. |
| Approach: | They propose a framework that leverages large language models to dynamically adapt VP behavior based on trainee input. |
| Outcome: | The proposed framework reflects real-world communication skills and produces more natural and realistic interactions than existing methods. |
A Typology of Errors for User Utterances in Chatbots (2024.lrec-main)
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| Challenge: | Non-prescriptive language uses in chatbot communication create challenges for Semantic Parsing (SP). |
| Approach: | They propose a typology based on a sample of non-prescriptive language uses mined from a domain-specific chatbot log and a framework for automatically mapping these errors to the typology. |
| Outcome: | The proposed typology can be extended to include new classes of errors across different domains and user demographics. |
Self-prompted Chain-of-Thought on Large Language Models for Open-domain Multi-hop Reasoning (2023.findings-emnlp)
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| Challenge: | Existing open-domain question-answering methods lack quality assurance . existing methods lack scalability and poor diversity, hindering LLMs' capabilities . |
| Approach: | They propose an open-domain multi-hop reasoning framework to answer multi-choice questions . they propose an adaptive sampler for in-context selection and self-prompted inference . |
| Outcome: | The proposed framework surpasses the existing SOTA methods on large-scale datasets and doubles the zero-shot performance of small-scale LLMs. |
From Phrases to Subgraphs: Fine-Grained Semantic Parsing for Knowledge Graph Question Answering (2025.findings-acl)
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| Challenge: | Existing approaches to knowledge graph question answering (KGQA) face semantic misalignment and reasoning noise. |
| Approach: | They propose a fine-grained semantic parsing framework for KGQA that maps natural language queries to executable logical forms. |
| Outcome: | The proposed framework achieves 18.5% performance improvement over the SOTA on a multi-hop CWQ dataset. |
CONAN-MT-SP: A Spanish Corpus for Counternarrative Using GPT Models (2024.lrec-main)
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María Estrella Vallecillo Rodríguez, Maria Victoria Cantero Romero, Isabel Cabrera De Castro, Arturo Montejo Ráez, María Teresa Martín Valdivia
| Challenge: | a new study evaluates the performance of GPT-based models to generate CNs for hate speech in Spanish . a growing number of social interactions through digital platforms have led to inappropriate behavior . |
| Approach: | They propose to use GPT-based models to generate CNs for Hate Speech in Spanish . they use the DeepL API to automatically translate the HS segment into Spanish based on the original CN pairs translated into spanish . |
| Outcome: | The proposed models outperform human models in most instances, the authors say . the results will be made available to the research community . |
Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns (2022.acl-long)
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| Challenge: | Existing work to predict gaze patterns during naturalistic reading has not been conducted on general text characteristics. |
| Approach: | They propose to use two eye-tracking corpora of naturalistic reading and two language models to test their performance. |
| Outcome: | The proposed models predict eye-tracking measures during naturalistic reading and language processing. |
Multimodal Contextualized Semantic Parsing from Speech (2024.acl-long)
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| 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 . |
KaeDe: Progressive Generation of Logical Forms via Knowledge-Aware Question Decomposition for Improved KBQA (2025.findings-emnlp)
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| Challenge: | Existing methods for answering natural language questions are difficult to generate . lack of a logical form for complex graphs can negatively impact overall performance . |
| Approach: | They propose a generate-then-retrieve method that converts questions into structured LF queries . they propose to combine knowledge-aware question decomposition and progressive LF generation . |
| Outcome: | The proposed method achieves state-of-the-art (SOTA) performance on WebQuestionSP and ComplexWebQuestions benchmarks. |
SORTIE: Dependency-Aware Symbolic Reasoning for Logical Data-to-text Generation (2023.findings-acl)
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| Challenge: | Existing studies on logical data-to-text generation rely on neural language models to generate the final table description, but they have difficulty working out key entities in the description. |
| Approach: | They propose a symbolic reasoning framework that reasons out each entity in the table description with a table-compatible programming language. |
| Outcome: | The proposed framework outperforms existing methods on three datasets and three backbones with an absolute improvement of 5.7%11.5% on SP-Acc. |
CoLA: A Choice Leakage Attack Framework to Expose Privacy Risks in Subset Training (2026.acl-long)
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| Challenge: | Existing threat models underestimate subset-training privacy risks because of the scale of modern datasets. |
| Approach: | They propose a unified framework for analyzing privacy leakage in subset selection based on side-channel metadata from the subset process or via the outputs of the target model. |
| Outcome: | The proposed framework analyzes privacy leakage in subset selection based on two different scenarios . |
How Sememic Components Can Benefit Link Prediction for Lexico-Semantic Knowledge Graphs? (2025.emnlp-main)
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| Challenge: | Existing methods to predict missing triples in Knowledge Graphs are limited by semantic information. |
| Approach: | They propose a method to leverage sememe knowledge to enhance LP . LP is a technique that integrates structural and textual information into a Knowledge Graph . |
| Outcome: | The proposed method improves LP performance in English and Chinese . it improves on WN18RR, HN7 and CWN5, respectively . |
Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement (2023.findings-emnlp)
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Zhiheng Xi, Senjie Jin, Yuhao Zhou, Rui Zheng, Songyang Gao, Jia Liu, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing prompting methods have been used to enhance multistep reasoning capabilities of large language models, but they have overlooked the potential of formulating higher-quality problems. |
| Approach: | They propose a method that starts from the problem side and refines problems to be more comprehensible and solvable for models. |
| Outcome: | The proposed method achieves notable and consistent effectiveness on five reasoning benchmarks across different models. |
Improving Continual Pre-training Through Seamless Data Packing (2025.findings-acl)
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| Challenge: | Empirical evaluations across various model architectures and corpus domains demonstrate the effectiveness of our method, outperforming baselines in 99% of all settings. |
| Approach: | They propose a method that uses a sliding window technique to pack data before continual pre-training to preserve contextual information and enhance model performance. |
| Outcome: | Empirical evaluations across various model architectures and corpus domains demonstrate the effectiveness of the proposed method outperforming baselines in 99% of settings. |
Cross-lingual Back-Parsing: Utterance Synthesis from Meaning Representation for Zero-Resource Semantic Parsing (2024.emnlp-main)
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| 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. |
Mitigating Data Scarcity in Semantic Parsing across Languages with the Multilingual Semantic Layer and its Dataset (2024.findings-acl)
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Abelardo Carlos Martinez Lorenzo, Pere-Lluís Huguet Cabot, Karim Ghonim, Lu Xu, Hee-Soo Choi, Alberte Fernández-Castro, Roberto Navigli
| Challenge: | Large Language Models (LLMs) have advanced significantly in understanding human text, but semantic representations remain crucial for various applications. |
| Approach: | They introduce a multilingual semantic layer which decouples from disambiguation and external inventories and simplifies the task. |
| Outcome: | The proposed model reduces performance gap between languages and annotators by enabling them to understand semantic relations between concepts in any language. |
LLMs Can Simulate Standardized Patients via Agent Coevolution (2025.acl-long)
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Zhuoyun Du, LujieZheng LujieZheng, Renjun Hu, Yuyang Xu, Xiawei Li, Ying Sun, Wei Chen, Jian Wu, Haolei Cai, Haochao Ying
| Challenge: | Training medical personnel using standardized patients (SPs) remains a complex challenge, necessitating extensive domain expertise and role-specific practice. |
| Approach: | They propose a simulated patient framework that allows patient agents to simulate diagnostic process through multi-turn dialogues. |
| Outcome: | The proposed framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference after evolving over 200 cases for 10 hours with excellent generalizability. |
Leveraging Linguistically Enhanced Embeddings for Open Information Extraction (2024.lrec-main)
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| Challenge: | Open Information Extraction (OIE) is a structure prediction task in NLP that aims to extract structured n-ary tuples from free text. |
| Approach: | They propose to leverage linguistic features with a Seq2Seq PLM for OIE to improve performance. |
| Outcome: | The proposed methods give any neural OIE architecture the key performance boost from both PLMs and linguistic features in one go. |
SQUAB: Evaluating LLM robustness to Ambiguous and Unanswerable Questions in Semantic Parsing (2025.emnlp-main)
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| Challenge: | Practical user questions often deviate from ideal conditions, challenging the applicability of existing benchmarks. |
| Approach: | They propose an automatic dataset generator of Ambiguous and Unanswerable questions that generates complex, annotated SP tests using a blend of SQL and LLM capabilities. |
| Outcome: | The proposed framework reduces test generation costs by up to 99% while aligning with real-world question patterns. |
Morpheme Sense Disambiguation: A New Task Aiming for Understanding the Language at Character Level (2024.lrec-main)
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| Challenge: | Morphemes are a strong linguistic feature to capture lexical semantics, but lack of morpheme-informed resources and the expense of manual annotations hinder morphme-enhanced methods. |
| Approach: | They propose a task of Morpheme Sense Disambiguation with two subtasks in-text and in-word to generalize morpheme features on more tasks. |
| Outcome: | The proposed tasks are based on two morpheme-annotated datasets for Chinese . the best model yields a promising precision of 77.66% on in-text and 88.19% on in word . |
Human or LLM as Standardized Patients? A Comparative Study in Medical Education (2026.acl-long)
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| Challenge: | Standardized patients (VSPs) are indispensable for clinical skills training but remain expensive and difficult to scale. |
| Approach: | They propose a multi-agent VSP framework that separates case-grounded information disclosure from response generation to support stable, inquiry-conditioned patient behavior. |
| Outcome: | The proposed framework more closely matches human SP behavior than existing VSPs, particularly in case consistency and controlled disclosure. |