{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/intent-classification/papers/3","list_of":"/task/intent-classification","task":"Intent Classification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":4,"rows_per_page":100,"rows":[201,300],"of":344,"counts":{"archive_papers_tagged":344,"with_a_code_link":113,"where_syntology_ran_a_sample":17,"not_listed_spam_title":0,"listed":344,"listed_where_code_ran":17,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":14,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":14,"listed_every_run_a_failure_of_syntologys_instrument":3,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/intent-classification","prev":"/task/intent-classification/papers/2","next":"/task/intent-classification/papers/4","papers":[{"url":null,"slug":"strategies-to-improve-few-shot-learning-for","title":"Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"alexa-teacher-model-pretraining-and","title":"Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems","date":"2022-06-15","arxiv_id":"2206.07808","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-for-intent-classification-2","title":"Data Augmentation for Intent Classification","date":"2022-06-12","arxiv_id":"2206.05790","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-meta-learning-paradigm-for-zero-shot","title":"A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism","date":"2022-06-05","arxiv_id":"2206.02179","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-building-spoken-language-understanding","title":"On Building Spoken Language Understanding Systems for Low Resourced Languages","date":"2022-05-25","arxiv_id":"2205.12818","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-advantages-of-dense-vector-to","title":"Exploring the Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Out-of-Scope Detection Tasks","date":"2022-05-18","arxiv_id":"2205.09021","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-intent-classification-in-the","title":"Fine-grained Intent Classification in the Legal Domain","date":"2022-05-06","arxiv_id":"2205.03509","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-pre-trained-transformers-robust-in-intent","title":"Are Pre-trained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-practical-utility-of","title":"Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"knn-contrastive-learning-for-out-of-domain","title":"KNN-Contrastive Learning for Out-of-Domain Intent Classification","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-distillation-meets-few-shot","title":"Knowledge Distillation Meets Few-Shot Learning: An Approach for Few-Shot Intent Classification Within and Across Domains","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"label-errors-in-banking77","title":"Label Errors in BANKING77","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"building-an-asr-error-robust-spoken-virtual","title":"Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data","date":"2022-04-11","arxiv_id":"2204.05183","repositories_listed":0,"syntology":null},{"url":null,"slug":"three-module-modeling-for-end-to-end-spoken","title":"Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-trained DNN-HMM-Based Acoustic-Phonetic Model","date":"2022-04-07","arxiv_id":"2204.03315","repositories_listed":0,"syntology":null},{"url":null,"slug":"quick-starting-dialog-systems-with-paraphrase","title":"Quick Starting Dialog Systems with Paraphrase Generation","date":"2022-04-06","arxiv_id":"2204.02546","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-textual-out-of-domain-detection","title":"Towards Textual Out-of-Domain Detection without In-Domain Labels","date":"2022-03-22","arxiv_id":"2203.11396","repositories_listed":0,"syntology":null},{"url":null,"slug":"bi-directional-joint-neural-networks-for","title":"Bi-directional Joint Neural Networks for Intent Classification and Slot Filling","date":"2022-02-26","arxiv_id":"2202.13079","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-data-augmentation-method-for-intent","title":"A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets","date":"2022-02-21","arxiv_id":"2202.10137","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-bert-meets-quantum-temporal-convolution","title":"When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing","date":"2022-02-17","arxiv_id":"2203.03550","repositories_listed":0,"syntology":null},{"url":null,"slug":"mslam-massively-multilingual-joint-pre","title":"mSLAM: Massively multilingual joint pre-training for speech and text","date":"2022-02-03","arxiv_id":"2202.01374","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-approach-to-integrate-human","title":"A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot","date":"2021-12-31","arxiv_id":"2201.02735","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-nlu-with-vector-projection-distance","title":"Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF","date":"2021-12-09","arxiv_id":"2112.04999","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-intent-classification-for","title":"Multi-modal Intent Classification for Assistive Robots with Large-scale Naturalistic Datasets","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-pre-finetuning-for-zero-shot-cross","title":"Multi-task pre-finetuning for zero-shot cross lingual transfer","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-explainable-dialogue-system","title":"Towards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"training-data-reduction-for-multilingual","title":"Training data reduction for multilingual Spoken Language Understanding systems","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"class-embeddings-for-improved-out-of-scope","title":"Class Embeddings for Improved Out-of-Scope Detection in Intent Classification","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-for-intent-classification","title":"Data Augmentation for Intent Classification with Generic Large Language Models","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-pre-training-for-plug-and-play-1","title":"Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-spoken-language-understanding-systems-for","title":"On Spoken Language Understanding Systems for Low Resourced Languages","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-better-citation-intent-classification","title":"Towards Better Citation Intent Classification","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/a-fine-tuned-wav2vec-2-0-hubert-benchmark-for","slug":"a-fine-tuned-wav2vec-2-0-hubert-benchmark-for","title":"A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding","date":"2021-11-04","arxiv_id":"2111.02735","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-intent-classification-by-gauging","title":"Few-Shot Intent Classification by Gauging Entailment Relationship Between Utterance and Semantic Label","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-paraphrase-generation-for","title":"Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"not-so-fast-classifier-accuracy-and-entropy","title":"Not So Fast, Classifier – Accuracy and Entropy Reduction in Incremental Intent Classification","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-explicit-joint-and-supervised-contrastive","title":"An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling","date":"2021-10-26","arxiv_id":"2110.13691","repositories_listed":0,"syntology":null},{"url":null,"slug":"intent-classification-using-pre-trained","title":"Intent Classification Using Pre-trained Language Agnostic Embeddings For Low Resource Languages","date":"2021-10-18","arxiv_id":"2110.09264","repositories_listed":0,"syntology":null},{"url":null,"slug":"mmiu-dataset-for-visual-intent-understanding","title":"MMIU: Dataset for Visual Intent Understanding in Multimodal Assistants","date":"2021-10-13","arxiv_id":"2110.06416","repositories_listed":0,"syntology":null},{"url":null,"slug":"narle-natural-language-models-using","title":"NaRLE: Natural Language Models using Reinforcement Learning with Emotion Feedback","date":"2021-10-05","arxiv_id":"2110.02148","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-adversarial-networks-based-on","title":"Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications","date":"2021-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-teacher-student-learning-approach","title":"Exploring Teacher-Student Learning Approach for Multi-lingual Speech-to-Intent Classification","date":"2021-09-28","arxiv_id":"2109.13486","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-few-shot-intent","title":"Semi-Supervised Few-Shot Intent Classification and Slot Filling","date":"2021-09-17","arxiv_id":"2109.08754","repositories_listed":0,"syntology":null},{"url":null,"slug":"effectiveness-of-pre-training-for-few-shot","title":"Effectiveness of Pre-training for Few-shot Intent Classification","date":"2021-09-13","arxiv_id":"2109.05782","repositories_listed":0,"syntology":null},{"url":null,"slug":"cins-comprehensive-instruction-for-few-shot","title":"CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems","date":"2021-09-10","arxiv_id":"2109.04645","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-regular-expressions-with-neural","title":"Integrating Regular Expressions with Neural Networks via DFA","date":"2021-09-07","arxiv_id":"2109.02882","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-model-for-intent-and-entity-recognition","title":"Joint model for intent and entity recognition","date":"2021-09-07","arxiv_id":"2109.03221","repositories_listed":0,"syntology":null},{"url":null,"slug":"infobert-zero-shot-approach-to-natural","title":"InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-single-example-can-improve-zero-shot-data","title":"A Single Example Can Improve Zero-Shot Data Generation","date":"2021-08-16","arxiv_id":"2108.06991","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-meta-learning-for-cross","title":"Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"single-example-can-improve-zero-shot-data","title":"Single Example Can Improve Zero-Shot Data Generation","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-text-classification-via-contrastive","title":"Improved Text Classification via Contrastive Adversarial Training","date":"2021-07-21","arxiv_id":"2107.10137","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-natural-language-understanding","title":"End-to-End Natural Language Understanding Pipeline for Bangla Conversational Agents","date":"2021-07-12","arxiv_id":"2107.05541","repositories_listed":0,"syntology":null},{"url":null,"slug":"word-free-spoken-language-understanding-for","title":"Word-Free Spoken Language Understanding for Mandarin-Chinese","date":"2021-07-01","arxiv_id":"2107.00186","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-the-generalization-for-intent","title":"Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU","date":"2021-06-28","arxiv_id":"2106.14464","repositories_listed":0,"syntology":null},{"url":null,"slug":"conda-a-contextual-dual-annotated-dataset-for","title":"CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection","date":"2021-06-11","arxiv_id":"2106.06213","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-nlu-reranking-using-entity","title":"Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems","date":"2021-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-inductive-node-classification-across","title":"Meta-Inductive Node Classification across Graphs","date":"2021-05-14","arxiv_id":"2105.06725","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-text-and-label-generation-for-spoken","title":"Joint Text and Label Generation for Spoken Language Understanding","date":"2021-05-11","arxiv_id":"2105.05052","repositories_listed":0,"syntology":null},{"url":null,"slug":"fuzzy-classification-of-multi-intent","title":"Fuzzy Classification of Multi-intent Utterances","date":"2021-04-22","arxiv_id":"2104.10830","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-s-the-context-long-context-nlm","title":"Adapting Long Context NLM for ASR Rescoring in Conversational Agents","date":"2021-04-21","arxiv_id":"2104.11070","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-for-voice-assistant-nlu","title":"Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase","date":"2021-04-16","arxiv_id":"2104.08268","repositories_listed":0,"syntology":null},{"url":"/paper/integration-of-pre-trained-networks-with","slug":"integration-of-pre-trained-networks-with","title":"Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding","date":"2021-04-15","arxiv_id":"2104.07253","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-intent-classification-and-slot","title":"Few-shot Intent Classification and Slot Filling with Retrieved Examples","date":"2021-04-12","arxiv_id":"2104.05763","repositories_listed":0,"syntology":null},{"url":null,"slug":"intent-recognition-and-unsupervised-slot","title":"Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems","date":"2021-04-03","arxiv_id":"2104.01287","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-domain-specific-representations","title":"The impact of domain-specific representations on BERT-based multi-domain spoken language understanding","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"industry-scale-semi-supervised-learning-for","title":"Industry Scale Semi-Supervised Learning for Natural Language Understanding","date":"2021-03-29","arxiv_id":"2103.15871","repositories_listed":0,"syntology":null},{"url":null,"slug":"nubot-embedded-knowledge-graph-with-rasa","title":"NUBOT: Embedded Knowledge Graph With RASA Framework for Generating Semantic Intents Responses in Roman Urdu","date":"2021-02-20","arxiv_id":"2102.10410","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-acoustic-and-linguistic-embeddings","title":"Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification","date":"2021-02-15","arxiv_id":"2102.07370","repositories_listed":0,"syntology":null},{"url":null,"slug":"protoda-efficient-transfer-learning-for-few","title":"ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification","date":"2021-01-28","arxiv_id":"2101.11753","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-joint-intent-detection-and-slot","title":"A survey of joint intent detection and slot-filling models in natural language understanding","date":"2021-01-20","arxiv_id":"2101.08091","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-character-representation-enhanced-on-device","title":"A character representation enhanced on-device Intent Classification","date":"2021-01-12","arxiv_id":"2101.04456","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-fluent-query-reformulations-with","title":"Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning","date":"2020-12-18","arxiv_id":"2012.10033","repositories_listed":0,"syntology":null},{"url":null,"slug":"generation-of-complex-database-queries-and","title":"Generation of complex database queries and API calls from natural language utterances","date":"2020-12-15","arxiv_id":"2012.08146","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-multiple-asr-hypotheses-to-boost-i18n","title":"Using multiple ASR hypotheses to boost i18n NLU performance","date":"2020-12-07","arxiv_id":"2012.04099","repositories_listed":0,"syntology":null},{"url":null,"slug":"delexicalized-paraphrase-generation","title":"Delexicalized Paraphrase Generation","date":"2020-12-04","arxiv_id":"2012.02763","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-efficient-paraphrase-generation-to","title":"Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-learning-of-spoken-language","title":"Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-to-classify-intent-and-slot","title":"Meta learning to classify intent and slot labels with noisy few shot examples","date":"2020-11-30","arxiv_id":"2012.07516","repositories_listed":0,"syntology":null},{"url":null,"slug":"acoustics-based-intent-recognition-using","title":"Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages","date":"2020-11-07","arxiv_id":"2011.03646","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-studies-of-institutional-federated","title":"Empirical Studies of Institutional Federated Learning For Natural Language Processing","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-out-of-scope-detection-in-intent","title":"Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-feature-mining-for-constraint-based","title":"Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"recent-neural-methods-on-slot-filling-and","title":"Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey","date":"2020-11-01","arxiv_id":"2011.00564","repositories_listed":0,"syntology":null},{"url":null,"slug":"sciwing-a-software-toolkit-for-scientific-1","title":"SciWING– A Software Toolkit for Scientific Document Processing","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-binarized-neural-networks-for-text","title":"End to End Binarized Neural Networks for Text Classification","date":"2020-10-11","arxiv_id":"2010.05223","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-unpaired-text-data-for-training","title":"Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems","date":"2020-10-08","arxiv_id":"2010.04284","repositories_listed":0,"syntology":null},{"url":null,"slug":"yi-chong-jie-he-hua-yu-wei-biao-qian-zhu-yi","title":"一种结合话语伪标签注意力的人机对话意图分类方法(A Human-machine Dialogue Intent Classification Method using Utterance Pseudo Label Attention)","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-learning-with-common-sense-1","title":"Zero-Shot Learning with Common Sense Knowledge Graphs","date":"2020-09-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"augmented-natural-language-for-generative","title":"Augmented Natural Language for Generative Sequence Labeling","date":"2020-09-15","arxiv_id":"2009.13272","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-lstm-and-bert-for-small","title":"A Comparison of LSTM and BERT for Small Corpus","date":"2020-09-11","arxiv_id":"2009.05451","repositories_listed":0,"syntology":null},{"url":null,"slug":"emora-an-inquisitive-social-chatbot-who-cares","title":"Emora: An Inquisitive Social Chatbot Who Cares For You","date":"2020-09-10","arxiv_id":"2009.04617","repositories_listed":0,"syntology":null},{"url":null,"slug":"simple-is-better-lightweight-data","title":"Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification","date":"2020-09-08","arxiv_id":"2009.03695","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-modelling-of-cyber-activities-and","title":"Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors","date":"2020-08-26","arxiv_id":"2008.11400","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-balancing-for-boosting-performance-of","title":"Data balancing for boosting performance of low-frequency classes in Spoken Language Understanding","date":"2020-08-06","arxiv_id":"2008.02603","repositories_listed":0,"syntology":null},{"url":"/paper/improving-end-to-end-speech-to-intent","slug":"improving-end-to-end-speech-to-intent","title":"Improving End-to-End Speech-to-Intent Classification with Reptile","date":"2020-08-05","arxiv_id":"2008.01994","repositories_listed":0,"syntology":null},{"url":null,"slug":"kbot-a-knowledge-graph-based-chatbot-for","title":"KBot: a Knowledge graph based chatBot for natural language understanding over linked data","date":"2020-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-adversarial-training-in-self","title":"Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification","date":"2020-07-29","arxiv_id":"2007.15072","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-on-the-influence-of-architecture","title":"A Study on the Influence of Architecture Complexity of RNNs for Intent Classification in E-Commerce Chats in Bahasa Indonesia","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-intent-classification-in-an-e","title":"Improving Intent Classification in an E-commerce Voice Assistant by Using Inter-Utterance Context","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iit-gandhinagar-at-semeval-2020-task-9-code","title":"IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection","date":"2020-06-25","arxiv_id":"2006.14465","repositories_listed":0,"syntology":null}],"record_sha256":"31a0c90b3ae88e0a1dbeee45a46f1cfa12e80d058ee6178dc1c4eafc6720b6ab","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}