Papers with intent classification
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification (D19-61)
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| Challenge: | Recent studies have focused on the problem of generalizing from a few examples per category. |
| Approach: | They propose to use feature space data augmentation methods to improve intent classification performance in few-shot setting. |
| Outcome: | The proposed methods improve intent classification performance in few-shot setting beyond transfer learning approaches. |
Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems (2020.coling-industry)
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| Challenge: | a number of dialog systems have been developed to perform tasks with high accuracy on benchmarks, but there is a problem with annotated seed data. |
| Approach: | They propose a model that augments initial seed data by paraphrasing existing utterances automatically. |
| Outcome: | The proposed approach improves intent classification and slot labeling on a public dataset and with a real-world dialog system. |
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems (2021.naacl-industry)
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Tong Wang, Jiangning Chen, Mohsen Malmir, Shuyan Dong, Xin He, Han Wang, Chengwei Su, Yue Liu, Yang Liu
| Challenge: | In dialog systems, the Natural Language Understanding component makes the interpretation decision before the mentioned entities are resolved. |
| Approach: | They propose to leverage Entity Resolution (ER) features in NLU reranking to learn model weights . they propose a score distribution matching method to ensure the models are calibrated . |
| Outcome: | The proposed approach outperforms the baseline model on multiple domain evaluations. |
Benchmarking the Covariate Shift Robustness of Open-world Intent Classification Approaches (2022.aacl-short)
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| Challenge: | Existing benchmarks for open-world intent classification focus on the second aspect of the problem. |
| Approach: | They propose two datasets that include utterances useful for evaluating the robustness of open-world models to covariate shift. |
| Outcome: | The proposed datasets provide a more realistic evaluation scenario for open-world intent classification in task-oriented dialog systems. |
Controlled Data Generation via Insertion Operations for NLU (2022.naacl-industry)
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| Challenge: | a new approach to annotate live traffic is emerging to be cost-effective and efficient . manual data annotation is expensive and not preferred for meeting customer privacy expectations . |
| Approach: | They propose a targeted synthetic data generation technique by inserting tokens into a given semantic signature. |
| Outcome: | The proposed approach achieves the same accuracy as training with all available data on a voice assistant dataset. |
Delexicalized Paraphrase Generation (2020.coling-industry)
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| Challenge: | Using convolutional neural networks, we generate delexicalized sentences . 1.29% accuracy is achieved with the generated paraphrases . |
| Approach: | They propose a neural paraphrasing model that generates delexicalized sentences . they use convolutional neural networks to pool on slot values and use pointers to locate them . |
| Outcome: | The proposed model generates delexicalized sentences with high quality . it can be used for intent classification and named entity recognition tasks . |
Local-to-global learning for iterative training of production SLU models on new features (2022.naacl-industry)
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| Challenge: | In many real-world NLP systems, new data becomes available with time and there is a need to refresh the model. |
| Approach: | They propose to adapt a local-to-global learning schedule to production settings where full data is not available at initial training iterations. |
| Outcome: | The proposed model improves model error rates by 7.3% and saves up to 25% training time for individual iterations. |
Scaling Intent Understanding: A Framework for Classification with Clarification using Lightweight LLMs (2026.eacl-industry)
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| Challenge: | Proprietary large-language models (LLMs) assign intents to user utterances without addressing ambiguity. |
| Approach: | They propose a domain-agnostic framework that equips open-source LLMs with the ability to perform intent classification and generate clarification questions in case of ambiguity. |
| Outcome: | The proposed framework performs intent classification and generates clarification questions in case of ambiguity. |
Metric Learning for Dynamic Text Classification (D19-61)
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| Challenge: | Traditional text classifiers are limited to predicting over a fixed set of labels, but real-world applications require dynamic classification. |
| Approach: | They propose to replace the traditional fixed-size output layer with a learned metric space . they propose to add or remove support points in the metric and fine-tune the resulting metric . |
| Outcome: | The proposed method is robust to changes in the label space and improves performance in low data regime. |
DeepPavlov: Open-Source Library for Dialogue Systems (P18-4)
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Mikhail Burtsev, Alexander Seliverstov, Rafael Airapetyan, Mikhail Arkhipov, Dilyara Baymurzina, Nickolay Bushkov, Olga Gureenkova, Taras Khakhulin, Yuri Kuratov, Denis Kuznetsov, Alexey Litinsky, Varvara Logacheva, Alexey Lymar, Valentin Malykh, Maxim Petrov, Vadim Polulyakh, Leonid Pugachev, Alexey Sorokin, Maria Vikhreva, Marat Zaynutdinov
| Challenge: | open-source library DeepPavlov is designed for rapid development of dialogue systems. |
| Approach: | open-source library DeepPavlov is tailored for development of conversational agents . the library prioritizes efficiency, modularity and extensibility with the goal to make it easier to develop dialogue systems from scratch . |
| Outcome: | the open-source library DeepPavlov is designed for rapid development of dialogue systems . it supports modular as well as end-to-end approaches to implementation of conversational agents . |
Practical Application of Domain Dependent Confidence Measurement for Spoken Language Understanding Systems (N18-3)
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| Challenge: | a confidence score is a scalar quantity that measures the reliability of an automatic system. |
| Approach: | They propose to use a confidence measure to evaluate the reliability of an SLU system . they build confidence models for three different types of dialogue states . |
| Outcome: | The proposed model can be used to reject low-confidence SLU results in real-world scenarios. |
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders (2024.acl-long)
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| Challenge: | Conversational systems often rely on embedding models for intent classification and intent clustering tasks. |
| Approach: | They propose a toolkit that gives a more holistic view of intent embedding models by considering three tasks– (1) intent classification, (2) intent clustering, and (3) a novel triplet task. |
| Outcome: | The proposed model improves on the linguistic dimensions while affecting performance on downstream task metrics. |
GameTox: A Comprehensive Dataset and Analysis for Enhanced Toxicity Detection in Online Gaming Communities (2025.naacl-short)
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| Challenge: | Existing methods to detect toxic behavior in online gaming environments are limited by utterance-level annotation. |
| Approach: | They propose to annotate game chat utterances for toxicity detection through intent classification and slot filling. |
| Outcome: | The proposed model improves the detection of toxic speech in online gaming environments and reveals limitations of current models. |
Industry Scale Semi-Supervised Learning for Natural Language Understanding (2021.naacl-industry)
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| Challenge: | Obtaining human annotation is expensive and time-consuming process. |
| Approach: | They propose a semi-supervised learning pipeline which leverages millions of unlabeled examples to improve natural language understanding tasks. |
| Outcome: | The proposed pipeline can be used to improve natural language understanding tasks. |
Distilling Multilingual Transformers into CNNs for Scalable Intent Classification (2022.emnlp-industry)
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| Challenge: | Existing multilingual models for voice assistants are limited by their prohibitive inference time and limited performance. |
| Approach: | They propose to distill and deploy multilingual Transformer models for voice assistants using a teacher-student framework that uses teacher-trained models to supervise student model training. |
| Outcome: | The proposed model outperforms a teacher model trained on unlabelled data and achieves equivalent performance. |
Zero-Shot-BERT-Adapters: a Zero-Shot Pipeline for Unknown Intent Detection (2023.findings-emnlp)
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| Challenge: | Intent discovery remains a crucial task in natural language processing . identifying novel, unseen intents remains one of the biggest challenges in this field . |
| Approach: | They propose a multi-language approach to intent discovery using Adapters and a Transformer architecture. |
| Outcome: | The proposed pipeline outperforms baselines in two zero-shot settings for intent classification and unseen intent discovery. |
Privacy Preserving Data Selection for Bias Mitigation in Speech Models (2025.acl-industry)
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Alkis Koudounas, Eliana Pastor, Vittorio Mazzia, Manuel Giollo, Thomas Gueudre, Elisa Reale, Luca Cagliero, Sandro Cumani, Luca De Alfaro, Elena Baralis, Daniele Amberti
| Challenge: | Existing methods for identifying subgroups raise privacy concerns and gather sensitive information at runtime might be impractical. |
| Approach: | They propose a method to identify and train underperforming subgroups and train a model to predict if an utterance belongs to these subgroup. |
| Outcome: | The proposed method reduces biases and improves performance on intent classification and automatic speech recognition tasks. |
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble (2022.findings-emnlp)
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| Challenge: | Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience. |
| Approach: | They propose a framework that encourages intermediate features to learn layer-specialized representations and assembles them implicitly into a single representation to absorb rich information in the pre-trained language model. |
| Outcome: | The proposed framework is significantly more effective than previous studies in intent classification and OOD datasets. |
Few-shot Intent Classification and Slot Filling with Retrieved Examples (2021.naacl-main)
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| Challenge: | Existing methods for few-shot learning are based on labeled examples, but they are non-trivial . few-sshot learning is challenging due to the imbalance in the amount of data between the source and target domains. |
| Approach: | They propose retrieval-based methods for intent classification and slot filling tasks . they use a batch-softmax objective to learn similar contextualized representations for spans . |
| Outcome: | The proposed method outperforms previous systems on the CLINC and SNIPS benchmarks. |
A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasks (2024.emnlp-industry)
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| Challenge: | Virtual assistants often can handle only a limited scope of intents. |
| Approach: | They propose to combine out-of-scope (OOS) rejection with intent classification . they propose to regularize cross-entropy loss with an in-scope embedding reconstruction loss . |
| Outcome: | The proposed method achieves a 1-4% improvement in the area under the precision-recall curve for rejecting out-of-sample instances without compromising intent classification performance. |
Explainable Abuse Detection as Intent Classification and Slot Filling (2022.tacl-1)
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| Challenge: | Existing models learn what abuse is from labeled examples and base their predictions on spurious cues. |
| Approach: | They propose a machine-friendly representation of the policy that moderators wish to enforce by breaking it down into intents and slots. |
| Outcome: | The proposed model breaks down a social media policy into intents and slots and provides a rationale for its decisions. |
MTSI-BERT: A Session-aware Knowledge-based Conversational Agent (2020.lrec-1)
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| Challenge: | Several models have been published achieving promising results in all the major linguistic tasks. |
| Approach: | They propose to exploit a BERT-based model to handle multi-turn conversations . they propose to use PuffBot to monitor asthma patients . |
| Outcome: | The proposed model can handle multi-turn conversations, a type of conversations that differs from single-turn by the presence of multiple related interactions. |
Prompt Perturbation Consistency Learning for Robust Language Models (2024.findings-eacl)
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Yao Qiang, Subhrangshu Nandi, Ninareh Mehrabi, Greg Ver Steeg, Anoop Kumar, Anna Rumshisky, Aram Galstyan
| Challenge: | Large language models have demonstrated impressive performance on a number of natural language processing tasks, such as question answering and text summarization. |
| Approach: | They propose a method to reduce the performance drop of large language models by regularizing the divergence between losses from clean and perturbed samples. |
| Outcome: | The proposed approach recovers on average 59% and 69% of the performance drop for IC and SF tasks while using ten times fewer augmented data samples. |
Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System (2021.naacl-main)
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| Challenge: | Text classification is usually studied by labeling texts with relevant categories from a predefined set. |
| Approach: | They propose a task where a system incrementally handles multiple rounds of new classes . they propose two entailment approaches, ENTAILMENT and HYBRID, which show promise . |
| Outcome: | The proposed task is based on a few-shot text classification task in the NLP domain. |
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness (2023.emnlp-main)
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| Challenge: | generative large language models (LLMs) are replacing human workers for some tasks . crowdsourcing has several downsides: 1) the workforce is costly, 2) output quality is difficult to achieve, and 3) there are overheads related to the design and organization of the process. |
| Approach: | They investigate whether ChatGPT-created paraphrases are more diverse and robust . they use a crowdsourcing tool to collect training or validation examples . |
| Outcome: | The proposed models are more diverse and robust than the existing models. |
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection (2021.findings-emnlp)
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| Challenge: | Existing methods to detect out-of-domain (OOD) inputs are limited and lack data. |
| Approach: | They propose a new architecture that extends Prototypical Networks to process in-domain and OOD sentences via Mutual Information Maximization objective. |
| Outcome: | The proposed method significantly improves performance up to 20% for OOD detection in low resource settings of text classification. |
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems (2021.emnlp-main)
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| Challenge: | Large-scale pre-trained language models have shown promising results for few-shot learning in task-oriented dialog (ToD) systems. |
| Approach: | They propose a self-training approach that iteratively labels the most confident unlabeled data to train a stronger Student model. |
| Outcome: | The proposed approach improves state-of-the-art pre-trained models in few-shot learning scenarios for task-oriented dialog (ToD) systems when only a small number of labeled data are available. |
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding (2021.naacl-main)
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Rob van der Goot, Ibrahim Sharaf, Aizhan Imankulova, Ahmet Üstün, Marija Stepanović, Alan Ramponi, Siti Oryza Khairunnisa, Mamoru Komachi, Barbara Plank
| Challenge: | Lack of publicly available evaluation data for low-resource languages limits progress in SLU . despite advances in neural modeling for slot and intent detection, datasets for SLU remain limited. |
| Approach: | They propose a joint learning approach with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. |
| Outcome: | The proposed model can learn English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. |
Interactive Classification by Asking Informative Questions (2020.acl-main)
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| Challenge: | Existing methods for intent classification rely on a single user input and do not interact with the user to reduce ambiguity and improve the final prediction. |
| Approach: | They propose a limited form of interaction to natural language intent classification . they add binary or multi-choice questions to the system to ask missing information . |
| Outcome: | The proposed method can be bootstrapped without interaction data and is scalable to two domains. |
Attentively Embracing Noise for Robust Latent Representation in BERT (2020.coling-main)
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| Challenge: | Modern digital personal assistants interact with users through voice . high error rates still prevail in the widespread adoption of speech technology . |
| Approach: | They propose to extract more robust latent representations for noisy ASR text classification using transformer tokens and attentive embracement layer and multi-head attention layer. |
| Outcome: | The proposed model significantly outperforms the baseline model on the Chatbot and Snips corpora for intent classification with ASR error. |
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding (2024.naacl-long)
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| Challenge: | End-to-end learning models require large volume of speech data with intent labels . however, models are sensitive to inconsistencies between training and evaluation conditions . |
| Approach: | They propose a module-based approach to learn intent in a noisy-channel model . they correlate error patterns between clean and noisy ASR transcripts . |
| Outcome: | The proposed method outperforms existing methods and improves in noisy environments. |
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System (2022.acl-long)
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| Challenge: | Existing pre-trained language models often form a cascaded generation problem . this can lead to error accumulation across different sub-tasks and greater data annotation overhead. |
| Approach: | They propose a plug-and-play model for task-oriented dialogue that learns primary TOD task completion skills from heterogeneous dialog corpora. |
| Outcome: | The proposed model learns primary TOD task completion skills from heterogeneous dialog corpora. |
Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes (2020.emnlp-main)
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| Challenge: | Existing methods for intent classification use one-class classification or inverse dictionary. |
| Approach: | They propose to represent class labels as a vector space where word graphs are mapped . they use inverse dictionary to take in account inter-class similarities provided by repeated occurrences . |
| Outcome: | The proposed method beats the state-of-the-art method in the Larson dataset by about 31 percentage points. |
Intent Classification and Slot Filling for Privacy Policies (2021.acl-long)
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| Challenge: | Sentences written in privacy policies explain privacy practices and the constituent text spans convey further specific information. |
| Approach: | They propose an English corpus of 5,250 intent and 11,788 slot annotations . they propose two alternative neural approaches to model the corpus as a sequence-to-sequence learning task. |
| Outcome: | The proposed corpus predicts intent classification and slot filling, while the sequence tagging method outperforms slot filler by a large margin. |
Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque (2020.lrec-1)
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| Challenge: | a new approach to task-oriented dialog systems is proposed for less-resourced languages . the authors compare training data projection with direct model transfer strategies . |
| Approach: | They propose to project existing annotations in rich-resource languages and use NMT to project them. |
| Outcome: | The proposed system outperforms models trained on Basque projected data in intent classification and slot filling tasks. |
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation (N19-1)
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| Challenge: | Previous work focused on generating semantically similar paraphrases without considering diversity. |
| Approach: | They propose a method to obtain highly diverse paraphrases without compromising on paraphrasing quality by using monotone submodular function maximization. |
| Outcome: | The proposed method is effective on multiple tasks such as intent classification and paraphrase recognition. |
End-to-End Slot Alignment and Recognition for Cross-Lingual NLU (2020.emnlp-main)
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| Challenge: | Existing methods to expand an NLU system to new languages use machine translation with slot label projection from source to translated utterances. |
| Approach: | They propose a novel end-to-end model that learns to align and predict target slot labels jointly for cross-lingual transfer. |
| Outcome: | The proposed model outperforms a simple label projection method on most languages and achieves competitive performance to the more complex, state-of-the-art projection method with only half the training time. |
ltzGLUE: Luxembourgish General Language Understanding Evaluation (2026.findings-acl)
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Alistair Plum, Felicia Körner, Anne-Marie Lutgen, Laura Bernardy, Fred Philippy, Emilia Milano, Nils Rehlinger, Cedric Lothritz, Tharindu Ranasinghe, Barbara Plank, Christoph Purschke
| Challenge: | ltzGLUE is the first official NLU benchmark for Luxembourgish (LTZ) based on the popular GLUE benchmark for English. |
| Approach: | They propose a new natural language understanding (NLU) benchmark for Luxembourgish based on the popular GLUE benchmark for English. |
| Outcome: | The proposed model performs well across many languages and is based on the GLUE benchmark for English. |
Main Predicate and Their Arguments as Explanation Signals For Intent Classification (2025.naacl-long)
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| Challenge: | Intent classification is crucial for conversational agents, and deep learning models perform well in this area due to the lack of suitable benchmark data. |
| Approach: | They propose a technique to augment text samples from intent classification datasets with word-level explanations by marking main predicates and their arguments as explanation signals. |
| Outcome: | The proposed method augments text samples from intent classification datasets with word-level explanations. |
CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training (2023.findings-acl)
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| Challenge: | Speech-to-text training and language model distillation are used to bridge the representations between speech and text. |
| Approach: | They propose a pre-training paradigm that integrates speech and text into a single frame-to-token alignment. |
| Outcome: | The proposed paradigm outperforms the state-of-the-art model on intent classification and slot filling tasks. |
CAPE: Context-Aware Private Embeddings for Private Language Learning (2021.emnlp-main)
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| Challenge: | Existing methods to obtain text representations or embeddings with these models encoding personally identifiable information may lead to privacy leaks. |
| Approach: | They propose a novel approach which combines differential privacy and adversarial learning to preserve privacy during training of embeddings. |
| Outcome: | The proposed approach reduces private information leakage by 3% over the current method. |
Introducing Semantics into Speech Encoders (2023.acl-long)
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Derek Xu, Shuyan Dong, Changhan Wang, Suyoun Kim, Zhaojiang Lin, Bing Liu, Akshat Shrivastava, Shang-Wen Li, Liang-Hsuan Tseng, Guan-Ting Lin, Alexei Baevski, Hung-yi Lee, Yizhou Sun, Wei Wang
| Challenge: | Existing self-supervised speech encoders contain primarily acoustic rather than semantic information. |
| Approach: | They propose a task-agnostic unsupervised way to incorporate semantic information from large language model (LLM) systems into self-supervised speech encoders without labeled audio transcriptions. |
| Outcome: | The proposed approach improves spoken language understanding (SLU) performance by over 5% on intent classification (IC), with modest gains in named entity resolution (NER) and slot filling (SF), and spoken question answering (SQA) score by over 22%. |
Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness (2020.emnlp-main)
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Stefan Larson, Anthony Zheng, Anish Mahendran, Rishi Tekriwal, Adrian Cheung, Eric Guldan, Kevin Leach, Jonathan K. Kummerfeld
| Challenge: | Recent work on dialog has found that crowdsourced data can have limited diversity as workers tend to write simple variations from prompts. |
| Approach: | They propose a general approach for guiding workers to write more diverse text by iteratively constraining their writing. |
| Outcome: | The proposed approach improves performance on dialog tasks and improves on existing datasets. |
On Collaborating Small and Large Models For Few-shot Intent Detection (2025.findings-emnlp)
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| Challenge: | Existing methods for few-shot intent detection face high inference cost and label interference. |
| Approach: | They propose a framework that integrates a small prediction model with a large language model for FSID. |
| Outcome: | The proposed framework outperforms existing methods on three benchmark datasets. |
Data Query Language and Corpus Tools for Slot-Filling and Intent Classification Data (2020.lrec-1)
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| Challenge: | Typical machine learning approaches require large amounts of training data . Managing training data can be cumbersome without dedicated tools . |
| Approach: | They propose a toolkit for analyzing slot-filling and intent classification corpora . they propose 'Query Language' for searching such corporan and tools for understanding structure . |
| Outcome: | The proposed toolkit can be used to uncover interesting and surprising insights. |
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery (2023.findings-emnlp)
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| Challenge: | Intent-related tasks are typically modeled as separate tasks, but a unified approach is proposed . INTENDD uses an entirely unsupervised contrastive learning strategy for representation learning . |
| Approach: | They propose a unified approach to identifying intents from dialogue utterances . they propose an unsupervised contrastive learning strategy for representation learning . |
| Outcome: | The proposed approach outperforms baselines on three intent-related tasks on multiple datasets. |
New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark (2024.lrec-main)
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| Challenge: | Intent classification and slot-filling tasks are essential tasks of Spoken Language Understanding (SLU). |
| Approach: | They propose to use a MEDIA SLU dataset to train a multilingual model to achieve both tasks jointly. |
| Outcome: | The proposed model can be trained on multiple datasets including the MEDIA dataset and extends to more tasks and use cases. |
TestNUC: Enhancing Test-Time Computing Approaches and Scaling through Neighboring Unlabeled Data Consistency (2025.acl-long)
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Henry Peng Zou, Zhengyao Gu, Yue Zhou, Yankai Chen, Weizhi Zhang, Liancheng Fang, Yibo Wang, Yangning Li, Kay Liu, Philip S. Yu
| Challenge: | Test-time computing approaches that leverage additional computational resources during inference have been proven effective in enhancing large language model performance. |
| Approach: | They propose a linearly scaling approach that leverages local consistency of neighboring unlabeled data to improve test-time predictions. |
| Outcome: | The proposed approach outperforms baseline methods such as prompting and self-consistency across eight datasets and performs robustly across embedding models. |