Papers with semantic parsing tasks
Generating Logical Forms from Graph Representations of Text and Entities (P19-1)
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| Challenge: | Recent approaches to semantic parsing have cast it as a sequence-to-sequence task, with strong results. |
| Approach: | They propose a Graph Neural Network architecture to incorporate information about relevant entities and their relations during parsing. |
| Outcome: | The proposed approach outperforms the state-of-the-art in several tasks without pre-training and outperformed existing approaches when combined with BERT pre-trainment. |
kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning (2024.naacl-long)
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Wenting Zhao, Ye Liu, Yao Wan, Yibo Wang, Qingyang Wu, Zhongfen Deng, Jiangshu Du, Shuaiqi Liu, Yunlong Xu, Philip Yu
| Challenge: | Recent advances in task-oriented parsing involve formulating the task as a sequence-to-sequence problem, relying on a wealth of labeled data. |
| Approach: | They propose a task-oriented parsing framework that integrates nearest-neighbor learning with a nearest-nearest approach. |
| Outcome: | The proposed model can be used to synthesize computer programs based on a natural-language prompt without additional data or specialized prompts. |
Skeletons Matter: Dynamic Data Augmentation for Text-to-Query (2025.emnlp-main)
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| 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. |
Grammar-based Decoding for Improved Compositional Generalization in Semantic Parsing (2023.findings-acl)
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| 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. |
Learning Algebraic Recombination for Compositional Generalization (2021.findings-acl)
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Chenyao Liu, Shengnan An, Zeqi Lin, Qian Liu, Bei Chen, Jian-Guang Lou, Lijie Wen, Nanning Zheng, Dongmei Zhang
| Challenge: | Neural sequence models exhibit limited compositional generalization ability in semantic parsing tasks. |
| Approach: | They propose an end-to-end neural model to learn algebraic recombination for compositional generalization. |
| Outcome: | The proposed model is based on two realistic and comprehensive compositional generalization benchmarks. |
Grammar-Constrained Neural Semantic Parsing with LR Parsers (2021.findings-acl)
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| Challenge: | a context-free grammar can be used to enforce syntactical constraints when predicting logical forms. |
| Approach: | They propose a model that uses an LR parser to maintain syntactically valid sequences throughout the decoding procedure. |
| Outcome: | The proposed model is conceptually simpler and adds less overhead during inference compared to other approaches . it is compared with existing grammar-guided decoding frameworks and is cost-effective . |
Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data (2021.findings-emnlp)
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| Challenge: | Existing methods to improve semantic parsing performance on target languages are limited. |
| Approach: | They propose a Translate-and-Fill method that produces silver training data for a multilingual semantic parser. |
| Outcome: | The proposed method produces silver training data for a multilingual parser. |
Multi-level Alignment Pretraining for Multi-lingual Semantic Parsing (2020.coling-main)
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| Challenge: | Existing methods for multilingual semantic parsing only handle monolingual parsers, while in real world applications such as Chatbot and search engine, we generally need to handle multi-lingual semanticparsing. |
| Approach: | They propose a multi-level alignment pretraining method in a unified architecture for multi-lingual semantic parsing. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a publicly avail-able multi-lingual semantic parsing dataset and a newly constructed dataset. |
Improving Compositional Generalization with Latent Structure and Data Augmentation (2022.naacl-main)
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| Challenge: | Generic unstructured neural networks struggle on out-of-distribution compositional generalization. |
| Approach: | They propose a method to recombinate examples from a model called Compositional Structure Learner and add them to a pre-trained sequence-to-sequence model. |
| Outcome: | The proposed model is even stronger than a T5-CSL ensemble on two real world compositional generalization tasks. |
XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing (2022.findings-emnlp)
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| Challenge: | Existing work focuses on English datasets, and it is unclear whether large language models can serve as competitive semantic parsers for other languages. |
| Approach: | They propose a framework that learns to retrieve relevant English exemplars for a given query to construct prompts. |
| Outcome: | The proposed framework learns to retrieve relevant English exemplars for a given query to construct prompts. |
Broad-Coverage Semantic Parsing as Transduction (D19-1)
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| Challenge: | Existing approaches to broad-coverage semantic parsing are not applicable to all frameworks because of the lack of explicit alignments between tokens in the sentence and nodes in the semantic graph. |
| Approach: | They propose a transduction parsing paradigm that unifies different broad-coverage semantic parsers into a paradigm that leverages multiple attention mechanisms to build meaning representation. |
| Outcome: | The proposed approach improves state-of-the-art on AMR, SDP and UCCA and is competitive with the state- of-the art on SDP. |
Few-Shot Semantic Parsing with Language Models Trained on Code (2022.naacl-main)
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| 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. |
Learning to Retrieve Iteratively for In-Context Learning (2024.emnlp-main)
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Yunmo Chen, Tongfei Chen, Harsh Jhamtani, Patrick Xia, Richard Shin, Jason Eisner, Benjamin Van Durme
| 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. |
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base (2022.acl-long)
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Shulin Cao, Jiaxin Shi, Liangming Pan, Lunyiu Nie, Yutong Xiang, Lei Hou, Juanzi Li, Bin He, Hanwang Zhang
| Challenge: | Existing benchmarks for Complex KBQA lack compositional reasoning capabilities . Existing methods for Complex questions are poor in diversity or scale . |
| Approach: | They propose a compositional programming language to represent the reasoning process of complex questions. |
| Outcome: | The proposed dataset includes around 120K diverse natural language questions . it provides a compositional and interpretable programming language to represent the reasoning process of complex questions based on the proposed model . |
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (D18-1)
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Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, Dragomir Radev
| Challenge: | Existing datasets for semantic parsing are too small in terms of number of programs for training modern data-intensive models. |
| Approach: | They propose a large-scale complex and cross-domain semantic parsing task for a database . they use a dataset with 10,181 questions and 5,693 unique complex SQL queries . |
| Outcome: | The proposed task is different from previous tasks because it uses the same database and program . the best model achieves only 9.7% exact matching accuracy on a database split setting. |
Predicting generalization performance with correctness discriminators (2024.findings-emnlp)
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| Challenge: | Existing models estimate accuracy of models on unlabeled test data, but they hide their own uncertainty. |
| Approach: | They propose a model that establishes upper and lower bounds on the accuracy without requiring gold labels for the unseen data. |
| Outcome: | The proposed model establishes upper and lower bounds on accuracy without requiring gold labels for the unseen data. |
STARE at the Structure: Steering ICL Exemplar Selection with Structural Alignment (2025.emnlp-main)
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| 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. |
Incorporating Lexical and Syntactic Knowledge for Unsupervised Cross-Lingual Transfer (2024.lrec-main)
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| Challenge: | Unsupervised cross-lingual transfer is a process of transferring knowledge between languages without explicit supervision. |
| Approach: | They propose a framework that combines lexical and syntactic knowledge to enhance learning . they use a code-switching technique to implicitly teach lexica and a syntaktic-based graph attention network to help encode syntakic structure. |
| Outcome: | The proposed framework outperforms baselines of zero-shot cross-lingual transfer with 1.0 3.7 points on text classification, named entity recognition, and semantic parsing tasks. |
ZeLa: Advancing Zero-Shot Multilingual Semantic Parsing with Large Language Models and Chain-of-Thought Strategies (2024.lrec-main)
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| Challenge: | Existing approaches to augment multilingual datasets with labeled English data are lacking in annotated data. |
| Approach: | They propose a framework to augment English data and then use it to train parsers . they propose to use multilingual chain-of-thought prompting techniques to augment other languages' data . |
| Outcome: | The proposed framework augments English data in other languages and trains them with no demonstration samples in target languages. |