{"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/natural-language-understanding/papers/15","list_of":"/task/natural-language-understanding","task":"Natural Language Understanding","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":15,"pages_in_order":20,"rows_per_page":100,"rows":[1401,1500],"of":1978,"counts":{"archive_papers_tagged":1978,"with_a_code_link":809,"where_syntology_ran_a_sample":185,"not_listed_spam_title":0,"listed":1978,"listed_where_code_ran":185,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":155,"every_run_a_failure_of_syntologys_instrument":30,"listed_with_a_run_with_no_instrument_failure":155,"listed_every_run_a_failure_of_syntologys_instrument":30,"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/natural-language-understanding","prev":"/task/natural-language-understanding/papers/14","next":"/task/natural-language-understanding/papers/16","papers":[{"url":null,"slug":"lonli-an-extensible-framework-for-testing","title":"LoNLI: An Extensible Framework for Testing Diverse Logical Reasoning Capabilities for NLI","date":"2021-12-04","arxiv_id":"2112.02333","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-pre-training-with-universal","title":"Multilingual Pre-training with Universal Dependency Learning","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":"towards-more-robust-natural-language","title":"Towards More Robust Natural Language Understanding","date":"2021-12-01","arxiv_id":"2112.02992","repositories_listed":0,"syntology":null},{"url":null,"slug":"dyna-babi-unlocking-babi-s-potential-with","title":"Dyna-bAbI: unlocking bAbI's potential with dynamic synthetic benchmarking","date":"2021-11-30","arxiv_id":"2112.00086","repositories_listed":0,"syntology":null},{"url":null,"slug":"simple-contrastive-representation-adversarial","title":"Simple Contrastive Representation Adversarial Learning for NLP Tasks","date":"2021-11-26","arxiv_id":"2111.13301","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-neuro-symbolic-approach-for-text","title":"A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming","date":"2021-11-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"banglabert-language-model-pretraining-and","title":"BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cblue-a-chinese-biomedical-language-1","title":"CBLUE: A Chinese Biomedical Language Understanding EvaluationBenchmark","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-and-learned-layer","title":"Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dawson-data-augmentation-using-weak","title":"DAWSON: Data Augmentation using Weak Supervision On Natural Language","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"eventbert","title":"EventBERT","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"feelsgoodman-inferring-semantics-of-twitch-1","title":"FeelsGoodMan: Inferring Semantics of Twitch Neologisms","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fewnlu-benchmarking-state-of-the-art-methods-1","title":"FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"get-your-model-puzzled-introducing-crossword","title":"Get Your Model Puzzled: Introducing Crossword-Solving as a New NLP Benchmark","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"glm-general-language-model-pretraining-with","title":"GLM: General Language Model Pretraining with Autoregressive Blank Infilling","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"idpg-an-instance-dependent-prompt-generation","title":"IDPG: An Instance-Dependent Prompt Generation Method","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"kala-knowledge-augmented-language-model","title":"KALA: Knowledge-Augmented Language Model Adaptation","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-graph-is-in-rescue-task-oriented","title":"Knowledge Graph is in Rescue: Task Oriented Dialogue System for Response Generation without NLU and DM","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"local-structure-matters-most-perturbation","title":"Local Structure Matters Most: Perturbation Study in NLU","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-position-aware-attention-mechanism-in","title":"Probing Position-Aware Attention Mechanism in Long Document Understanding","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rome-a-robust-metric-for-evaluating-natural","title":"RoMe: A Robust Metric for Evaluating Natural Language Generation","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stable-natural-language-understanding-via","title":"Stable Natural Language Understanding via Invariant Causal Constraint","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-dark-side-of-the-language-pre-trained","title":"The Dark Side of the Language: Pre-trained Transformers in the DarkNet","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"things-not-written-in-text-exploring-spatial","title":"Things not Written in Text: Exploring Spatial Commonsense from Visual Signals","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unicon-unsupervised-intent-discovery-via","title":"UNICON: Unsupervised Intent Discovery via Semantic-level Contrastive Learning","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-makes-machine-reading-comprehension","title":"What Makes Machine Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"when-more-data-hurts-a-troubling-quirk-in","title":"When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-enactivist-account-of-mind-reading-in","title":"An Enactivist account of Mind Reading in Natural Language Understanding","date":"2021-11-11","arxiv_id":"2111.06179","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-adaption-model-agnostic-meta","title":"Cross-lingual Adaption Model-Agnostic Meta-Learning for Natural Language Understanding","date":"2021-11-10","arxiv_id":"2111.05805","repositories_listed":0,"syntology":null},{"url":null,"slug":"ontology-based-question-answering-over","title":"Ontology-based question answering over corporate structured data","date":"2021-11-08","arxiv_id":"2111.04507","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-brief-survey-and-comparative-study-of-1","title":"A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution in English","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-data-relabeling-for-robust-and","title":"Collaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"framenet-like-annotation-of-olfactory","title":"FrameNet-like Annotation of Olfactory Information in Texts","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-select-one-among-all-an-empirical","title":"How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-role-of-corpus-ordering-in-language","title":"On the Role of Corpus Ordering in Language Modeling","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-denoised-auto-encoding-in-language","title":"Rethinking Denoised Auto-Encoding in Language Pre-Training","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-training-with-few-shot-rationalization-1","title":"Self-training with Few-shot Rationalization","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-intent-discovery-with","title":"Semi-supervised Intent Discovery with Contrastive Learning","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nxmtransformer-semi-structured-sparsification","title":"NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM","date":"2021-10-28","arxiv_id":"2110.15766","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-stage-voice-application-recommender","title":"Two-stage Voice Application Recommender System for Unhandled Utterances in Intelligent Personal Assistant","date":"2021-10-19","arxiv_id":"2110.09877","repositories_listed":0,"syntology":null},{"url":null,"slug":"newsalyze-effective-communication-of-person","title":"Newsalyze: Effective Communication of Person-Targeting Biases in News Articles","date":"2021-10-18","arxiv_id":"2110.09158","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-multi-task-representation-for","title":"Collaborative Multi-Task Representation for Natural Language Understanding","date":"2021-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-does-data-corruption-affect-natural","title":"How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets","date":"2021-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pro-kd-progressive-distillation-by-following","title":"Pro-KD: Progressive Distillation by Following the Footsteps of the Teacher","date":"2021-10-16","arxiv_id":"2110.08532","repositories_listed":0,"syntology":null},{"url":null,"slug":"tableformer-robust-transformer-modeling-for","title":"TableFormer: Robust Transformer Modeling for Table-Text Encoding","date":"2021-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-do-compressed-large-language-models","title":"Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding","date":"2021-10-16","arxiv_id":"2110.08419","repositories_listed":0,"syntology":null},{"url":null,"slug":"neurips-2021-competition-iglu-interactive","title":"NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment","date":"2021-10-13","arxiv_id":"2110.06536","repositories_listed":0,"syntology":null},{"url":null,"slug":"autonlu-detecting-root-causing-and-fixing-nlu","title":"AutoNLU: Detecting, root-causing, and fixing NLU model errors","date":"2021-10-12","arxiv_id":"2110.06384","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-user-perception-of-speech","title":"Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric","date":"2021-10-11","arxiv_id":"2110.05376","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-inductive-bias-of-in-context-learning-1","title":"The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design","date":"2021-10-09","arxiv_id":"2110.04541","repositories_listed":0,"syntology":null},{"url":"/paper/lidsnet-a-lightweight-on-device-intent","slug":"lidsnet-a-lightweight-on-device-intent","title":"LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network","date":"2021-10-06","arxiv_id":"2110.15717","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":"a-review-of-text-style-transfer-using-deep","title":"A Review of Text Style Transfer using Deep Learning","date":"2021-09-30","arxiv_id":"2109.15144","repositories_listed":0,"syntology":null},{"url":null,"slug":"logic-pre-training-of-language-models","title":"Logic Pre-Training of Language Models","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pseudo-knowledge-distillation-towards","title":"Pseudo Knowledge Distillation: Towards Learning Optimal Instance-specific Label Smoothing Regularization","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-attention-with-learning-to-hash","title":"Sparse Attention with Learning to Hash","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"variance-pruning-pruning-language-models-via","title":"Variance Pruning: Pruning Language Models via Temporal Neuron Variance","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-pause-information-for-more-accurate","title":"Using Pause Information for More Accurate Entity Recognition","date":"2021-09-27","arxiv_id":"2109.13222","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-distillation-with-noisy-labels-for","title":"Knowledge Distillation with Noisy Labels for Natural Language Understanding","date":"2021-09-21","arxiv_id":"2109.10147","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-dynamic-based-data-filtering-may-not","title":"Training Dynamic based data filtering may not work for NLP datasets","date":"2021-09-19","arxiv_id":"2109.09191","repositories_listed":0,"syntology":null},{"url":null,"slug":"coda21-evaluating-language-understanding","title":"CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuned-transformers-show-clusters-of","title":"Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers","date":"2021-09-17","arxiv_id":"2109.08406","repositories_listed":0,"syntology":null},{"url":null,"slug":"flipda-effective-and-robust-data-augmentation-1","title":"FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"remixers-a-mixer-transformer-architecture","title":"Remixers: A Mixer-Transformer Architecture with Compositional Operators for Natural Language Understanding","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-training-with-few-shot-rationalization","title":"Self-training with Few-shot Rationalization: Teacher Explanations Aid Student in Few-shot NLU","date":"2021-09-17","arxiv_id":"2109.08259","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":"what-makes-reading-comprehension-questions-1","title":"What Makes Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/the-unreasonable-effectiveness-of-the","slug":"the-unreasonable-effectiveness-of-the","title":"The Unreasonable Effectiveness of the Baseline: Discussing SVMs in Legal Text Classification","date":"2021-09-15","arxiv_id":"2109.07234","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-adaptive-pre-training-and-self-training","title":"Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding","date":"2021-09-14","arxiv_id":"2109.06466","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradts-a-gradient-based-automatic-auxiliary","title":"GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks","date":"2021-09-13","arxiv_id":"2109.05748","repositories_listed":0,"syntology":null},{"url":null,"slug":"prior-omission-of-dissimilar-source-domain-s","title":"Prior Omission of Dissimilar Source Domain(s) for Cost-Effective Few-Shot Learning","date":"2021-09-11","arxiv_id":"2109.05234","repositories_listed":0,"syntology":null},{"url":null,"slug":"proto-a-neural-cocktail-for-generating","title":"Proto: A Neural Cocktail for Generating Appealing Conversations","date":"2021-09-06","arxiv_id":"2109.02513","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-self-debiasing-framework-for","title":"End-to-End Self-Debiasing Framework for Robust NLU Training","date":"2021-09-05","arxiv_id":"2109.02071","repositories_listed":0,"syntology":null},{"url":null,"slug":"error-detection-in-large-scale-natural","title":"Error Detection in Large-Scale Natural Language Understanding Systems Using Transformer Models","date":"2021-09-04","arxiv_id":"2109.01754","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximating-a-zulu-gf-concrete-syntax-with","title":"Approximating a Zulu GF concrete syntax with a neural network for natural language understanding","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"does-knowledge-help-general-nlu-an-empirical","title":"Does Knowledge Help General NLU? An Empirical Study","date":"2021-09-01","arxiv_id":"2109.00563","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":"/paper/asr-glue-a-new-multi-task-benchmark-for-asr","slug":"asr-glue-a-new-multi-task-benchmark-for-asr","title":"ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding","date":"2021-08-30","arxiv_id":"2108.13048","repositories_listed":0,"syntology":null},{"url":null,"slug":"walnut-a-benchmark-on-weakly-supervised","title":"WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding","date":"2021-08-28","arxiv_id":"2108.12603","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-heuristics-and-learning-in-a","title":"Integrating Heuristics and Learning in a Computational Architecture for Cognitive Trading","date":"2021-08-27","arxiv_id":"2108.12333","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-sentence-ordering-method-using-bert","title":"A New Sentence Ordering Method Using BERT Pretrained Model","date":"2021-08-26","arxiv_id":"2108.11994","repositories_listed":0,"syntology":null},{"url":null,"slug":"sauce-truncated-sparse-document-signature-bit","title":"SAUCE: Truncated Sparse Document Signature Bit-Vectors for Fast Web-Scale Corpus Expansion","date":"2021-08-26","arxiv_id":"2108.11948","repositories_listed":0,"syntology":null},{"url":null,"slug":"taming-the-beast-learning-to-control-neural","title":"Taming the Beast: Learning to Control Neural Conversational Models","date":"2021-08-24","arxiv_id":"2108.10561","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-bert-encoding-and-sentence-level","title":"Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering","date":"2021-08-24","arxiv_id":"2108.10986","repositories_listed":0,"syntology":null},{"url":null,"slug":"feelsgoodman-inferring-semantics-of-twitch","title":"FeelsGoodMan: Inferring Semantics of Twitch Neologisms","date":"2021-08-18","arxiv_id":"2108.08411","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-multiple-intent-detection-and-slot-1","title":"Joint Multiple Intent Detection and Slot Filling via Self-distillation","date":"2021-08-18","arxiv_id":"2108.08042","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-investigation-of-the-in-effectiveness-of-1","title":"An Investigation of the (In)effectiveness of Counterfactually Augmented Data","date":"2021-08-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"point-discriminative-learning-for","title":"Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis","date":"2021-08-04","arxiv_id":"2108.02104","repositories_listed":0,"syntology":null},{"url":null,"slug":"adept-an-adjective-dependent-plausibility","title":"ADEPT: An Adjective-Dependent Plausibility Task","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-aware-sentence-encoder-in-bert","title":"Structure-aware Sentence Encoder in Bert-Based Siamese Network","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"texsmart-a-system-for-enhanced-natural","title":"TexSmart: A System for Enhanced Natural Language Understanding","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"verb-metaphor-detection-via-contextual","title":"Verb Metaphor Detection via Contextual Relation Learning","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"demystifying-neural-language-models","title":"Local Structure Matters Most: Perturbation Study in NLU","date":"2021-07-29","arxiv_id":"2107.13955","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-effectiveness-of-intermediate-task","title":"The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding","date":"2021-07-21","arxiv_id":"2107.09931","repositories_listed":0,"syntology":null},{"url":null,"slug":"argument-linking-a-survey-and-forecast","title":"Argument Linking: A Survey and Forecast","date":"2021-07-18","arxiv_id":"2107.08523","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-dp-sgd-mechanism-for-large-scale","title":"An Efficient DP-SGD Mechanism for Large Scale NLP Models","date":"2021-07-14","arxiv_id":"2107.14586","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":"scientia-potentia-est-on-the-role-of","title":"Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation","date":"2021-07-01","arxiv_id":"2107.00281","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-spoken-language-understanding-2","title":"On joint training with interfaces for spoken language understanding","date":"2021-06-30","arxiv_id":"2106.15919","repositories_listed":0,"syntology":null}],"record_sha256":"8a860fabe5ac2859f2027052523fd50b0646e96af39d9059a4e873755698f36c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}