Papers with Semantic Parsing
Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling (2020.acl-main)
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| Challenge: | Existing approaches to slot filling are expensive and time-consuming. |
| Approach: | They propose a Coarse-to-fine approach for cross-domain slot filling . they propose utterance templates to regularize the representation of utterrances . |
| Outcome: | The proposed model outperforms state-of-the-art approaches in slot filling . it can be applied to the cross-domain named entity recognition task . |
An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols (N18-2)
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| Challenge: | Existing efforts to annotate natural language instructions into machine-readable formats are limited. |
| Approach: | They propose to annotate a corpus of natural language instructions consisting of 622 wet lab protocols to facilitate automatic or semi-automatic conversion into a machine-readable format. |
| Outcome: | The proposed corpus can be used to facilitate automatic or semi-automatic conversion of protocols into a machine-readable format and benefit biological research. |
Towards Automated Semantic Role Labelling of Hindi-English Code-Mixed Tweets (D19-55)
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| Challenge: | a new system for semantic role labelling of Hindi-English code-mixed tweets is proposed . code-mixing is a largely observed phenomenon in colloquial usage and on social media . |
| Approach: | They propose a system for automating Semantic Role Labelling of Hindi-English code-mixed tweets. |
| Outcome: | The proposed system gives an overall accuracy of 84% for Argument Classification, a 10% increase over the existing rule-based model. |
Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling (P18-2)
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| Challenge: | Recent models that use gold predicates only use a single predicate at a time. |
| Approach: | They propose an end-to-end approach for jointly predicting all predicates, arguments spans, and the relations between them. |
| Outcome: | The proposed model can model overlapping spans across different predicates in the same output structure without gold predicate predications. |
SPM: A Split-Parsing Method for Joint Multi-Intent Detection and Slot Filling (2023.acl-industry)
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| Challenge: | Existing studies focus on utterances with a single intent, but lack the ability to assign slots to each corresponding intent. |
| Approach: | They propose a split-parsing method for joint intent detection and slot filling . they split an input sentence into multiple sub-sentences which contain a single-intent . |
| Outcome: | The proposed method improves on three multi-intent datasets on multi-tasks. |
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. |
Polyglot Semantic Role Labeling (P18-2)
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| Challenge: | Existing approaches to multilingual semantic dependency parsing treat languages independently, without exploiting similarities between semantic structures across languages. |
| Approach: | They propose to combine resources from different languages in a CoNLL 2009 shared task to build a single polyglot semantic dependency parser. |
| Outcome: | The proposed model outperforms monolingual training on a CoNLL 2009 dataset with training data from multiple languages and representations using multilingual word vectors. |
Neural-Davidsonian Semantic Proto-role Labeling (D18-1)
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| Challenge: | Existing models for semantic proto-role labeling are based on a bidirectional LSTM encoding strategy. |
| Approach: | They propose a neural model for semantic proto-role labeling using a bidirectional LSTM encoding strategy that is adapted for the task. |
| Outcome: | The proposed model achieves state-of-the-art in a sentence with a LSTM encoder and a decoder. |
Slot-Gated Modeling for Joint Slot Filling and Intent Prediction (N18-2)
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| Challenge: | Existing approaches for slot filling and intent detection have independent attention weights, but they suffer from error propagation due to their independent models. |
| Approach: | They propose a slot gate that focuses on learning the relationship between intent and slot attention vectors to obtain better semantic frame results by the global optimization. |
| Outcome: | The proposed model significantly improves sentence-level semantic frame accuracy with 4.2% and 1.9% relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively. |
Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing (2021.findings-emnlp)
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Akshat Shrivastava, Pierce Chuang, Arun Babu, Shrey Desai, Abhinav Arora, Alexander Zotov, Ahmed Aly
| Challenge: | a novel approach to map utterances to semantic frames is based on non-autoregressive parsers that shift the decoding task from text generation to span prediction. |
| Approach: | They propose a non-autoregressive, task-oriented parser which shifts the decoding task from text generation to span prediction and produces endpoints as opposed to text. |
| Outcome: | The proposed model bridges the quality gap between non-autoregressive and autoregressive parsers, achieving 87 EM on TOPv2 and shows a 70% reduction in latency and 83% reduction in memory at beam size 5 compared to prior non-regressives. |
Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing (2023.acl-long)
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| Challenge: | Existing work on interactive semantic parsing relies on human annotations to train a model . prior work relied on human-annotated feedback data, which is prohibitively expensive and not scalable . |
| Approach: | They propose a task of simulating NL feedback for interactive semantic parsing . they propose evaluators to assess the quality of the simulated feedback . |
| Outcome: | The proposed simulator can generate high-quality NL feedback to boost the error correction ability of a specific parser. |
An MRC Framework for Semantic Role Labeling (2022.coling-1)
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| Challenge: | Existing work on semantic role labels ignores the semantic connection between the two tasks . et al. (2010) defined two types of semantic roles: core roles and non-core roles. |
| Approach: | They propose to use machine reading comprehension to bridge the gap between these two tasks . they formalize predicate disambiguation as multiple-choice machine reading understanding . |
| Outcome: | The proposed framework achieves state-of-the-art or comparable results to previous work . it uses the descriptions of candidate senses of a given predicate as options to select the correct sense . |
Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization (P19-1)
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| 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. |
CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling (2020.findings-emnlp)
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| Challenge: | Existing methods for training one model on multiple languages outperform monolingual baselines for low resource languages. |
| Approach: | They propose a method to combine training data from multiple languages to create a shared representation space for the model. |
| Outcome: | The proposed method outperforms monolingual and polyglot training on low resource languages. |
Label Definitions Improve Semantic Role Labeling (2022.naacl-main)
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| Challenge: | Existing work on semantic role labeling treats symbolic labels as symbolic . labeled data is costly and often lacking in many tasks, domains, and languages. |
| Approach: | They propose to retrieve and leverage semantic role labels from annotation guidelines . argument classification is at the core of Semantic Role Labeling . |
| Outcome: | The proposed model achieves state-of-the-art on a CoNLL09 dataset injected with label definitions given the predicate senses. |
The Robotic Surgery Procedural Framebank (2022.lrec-1)
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| Challenge: | Surgical practice has steadily improved thanks to the support of the approaches made available by observational science. |
| Approach: | They propose to extract from robot-surgical texts verbs and nouns that describe surgical actions and extend PropBank frames by adding any of new lemmas, frames or role sets required to cover missing lemae. |
| Outcome: | The proposed resource can be used to train and evaluate Semantic Role Labeling (SRL) systems in a fine-grained domain setting. |
CraftAssist Instruction Parsing: Semantic Parsing for a Voxel-World Assistant (2020.acl-main)
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| 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. |
Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling (2020.coling-main)
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| Challenge: | Existing joint models for intent detection and slot filling show insufficient robustness . however, some small changes of inputs can fool the models to produce wrong predictions . |
| Approach: | They propose a joint adversarial training model that generates adversarials to attack the joint model and trains the model to defend against the adversarial examples. |
| Outcome: | The proposed model achieves significantly higher scores and improves robustness on two datasets. |
Slot Transferability for Cross-domain Slot Filling (2021.findings-acl)
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| Challenge: | Existing work on slot filling uses labeled data from source domains to train a model for target domains. |
| Approach: | They propose a model-agnostic Slot Transferability Measure (STM) to evaluate the transferability from a source slot to a target slot. |
| Outcome: | The proposed method outperforms state-of-the-art models on multiple datasets and models. |
NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction (2021.naacl-main)
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Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, Ahmed Hassan Awadallah
| 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. |
Joint Slot Filling and Intent Detection via Capsule Neural Networks (P19-1)
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| Challenge: | Existing models that label slots and detect intent do not preserve hierarchical relationship between words, slots, and intents. |
| Approach: | They propose a capsule-based neural network model which performs slot filling and intent detection via a dynamic routing-by-agreement schema. |
| Outcome: | The proposed model performs better than existing models and existing models on real-world datasets. |
A Split-and-Recombine Approach for Follow-up Query Analysis (D19-1)
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| Challenge: | Context-dependent semantic parsing has proved to be an important but challenging task. |
| Approach: | They propose to perform follow-up query analysis to restate context-dependent queries with contextual information. |
| Outcome: | The proposed approach outperforms the state-of-the-art by nearly 8% on the FollowUp dataset . the extensibility of STAR on the SQA dataset is also promising . |
MRRL: Modifying the Reference via Reinforcement Learning for Non-Autoregressive Joint Multiple Intent Detection and Slot Filling (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive models for multiple intent detection and slot filling have limited overall accuracy due to multi-modality problem and lack of alignment between correct predictions. |
| Approach: | They propose a method for multiple intent detection and slot filling that introduces a modifier and employs reinforcement learning to modify the reference. |
| Outcome: | The proposed method outperforms the previous best approach by 3.6 overall accuracy on MixATIS dataset. |
Neuralizing Regular Expressions for Slot Filling (2021.emnlp-main)
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| Challenge: | Existing methods to integrate neural networks and symbolic rules have their merits and weaknesses. |
| Approach: | They propose to integrate regular expressions into neural networks for a slot filling task . they use finite-state transducers to convert regular expression into a neural network . their model has superior zero-shot and few-shot performance . |
| Outcome: | The proposed model outperforms rules in zero-shot and few-shot scenarios and is competitive when training data is available. |
LCAN: A Label-Aware Contrastive Attention Network for Multi-Intent Recognition and Slot Filling in Task-Oriented Dialogue Systems (2025.findings-emnlp)
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| Challenge: | Multi-intent utterances processing remains a persistent challenge due to intricate intent-slot dependencies and semantic ambiguities. |
| Approach: | They propose a label-aware contrastive attention network (LCAN) that integrates label-based attention and contrastive learning strategies to improve semantic understanding and generalization in multi-intent scenarios. |
| Outcome: | The proposed model improves intent recognition and slot filling performance in multi-intent dialogue systems. |