{"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/17","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":17,"pages_in_order":20,"rows_per_page":100,"rows":[1601,1700],"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/16","next":"/task/natural-language-understanding/papers/18","papers":[{"url":null,"slug":"fine-tuning-bert-for-low-resource-natural","title":"Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning","date":"2020-12-04","arxiv_id":"2012.02462","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multitask-active-learning-framework-for","title":"A Multitask Active Learning Framework for Natural Language Understanding","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-straightforward-approach-to","title":"A Straightforward Approach to Narratologically Grounded Character Identification","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/climate-fever-a-dataset-for-verification-of","slug":"climate-fever-a-dataset-for-verification-of","title":"CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims","date":"2020-12-01","arxiv_id":"2012.00614","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepyang-at-semeval-2020-task-4-using-the","title":"DEEPYANG at SemEval-2020 Task 4: Using the Hidden Layer State of BERT Model for Differentiating Common Sense","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"designing-templates-for-eliciting-commonsense","title":"Designing Templates for Eliciting Commonsense Knowledge from Pretrained Sequence-to-Sequence Models","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"distill-and-replay-for-continual-language","title":"Distill and Replay for Continual Language Learning","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"document-level-neural-machine-translation-3","title":"Document-Level Neural Machine Translation Using BERT as Context Encoder","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-motion-entities-in-natural","title":"Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-syntax-and-frame-semantics-in","title":"Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"kde-senseforce-at-semeval-2020-task-4","title":"KDE SenseForce at SemEval-2020 Task 4: Exploiting BERT for Commonsense Validation and Explanation","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"langresearchlab-nc-at-fincausal-2020-task-1-a","title":"LangResearchLab_NC at FinCausal 2020, Task 1: A Knowledge Induced Neural Net for Causality Detection","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":"neighbor-contextual-information-learners-for","title":"Neighbor Contextual Information Learners for Joint Intent and Slot Prediction","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nlu-co-at-semeval-2020-task-5-nlu-svm-based","title":"NLU-Co at SemEval-2020 Task 5: NLU/SVM Based Model Apply Tocharacterise and Extract Counterfactual Items on Raw Data","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"schema-aware-semantic-reasoning-for","title":"Schema Aware Semantic Reasoning for Interpreting Natural Language Queries in Enterprise Settings","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"seeing-the-world-through-text-evaluating","title":"Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-diversity-for-natural-language","title":"Semantic Diversity for Natural Language Understanding Evaluation in Dialog Systems","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-slot-prediction-on-low-corpus-data","title":"Semantic Slot Prediction on low corpus data using finite user defined list","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"team-solomon-at-semeval-2020-task-4-be","title":"Team Solomon at SemEval-2020 Task 4: Be Reasonable: Exploiting Large-scale Language Models for Commonsense Reasoning","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-multi-intent-order-and-slot","title":"Unified Multi Intent Order and Slot Prediction using Selective Learning Propagation","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extreme-model-compression-for-on-device","title":"Extreme Model Compression for On-device Natural Language Understanding","date":"2020-11-30","arxiv_id":"2012.00124","repositories_listed":0,"syntology":null},{"url":null,"slug":"autonlu-an-on-demand-cloud-based-natural","title":"AutoNLU: An On-demand Cloud-based Natural Language Understanding System for Enterprises","date":"2020-11-26","arxiv_id":"2011.13470","repositories_listed":0,"syntology":null},{"url":null,"slug":"gunrock-2-0-a-user-adaptive-social","title":"Gunrock 2.0: A User Adaptive Social Conversational System","date":"2020-11-17","arxiv_id":"2011.08906","repositories_listed":0,"syntology":null},{"url":null,"slug":"dialog-simulation-with-realistic-variations","title":"Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems","date":"2020-11-16","arxiv_id":"2011.08243","repositories_listed":0,"syntology":null},{"url":null,"slug":"audrey-a-personalized-open-domain","title":"Audrey: A Personalized Open-Domain Conversational Bot","date":"2020-11-11","arxiv_id":"2011.05910","repositories_listed":0,"syntology":null},{"url":null,"slug":"action-state-update-approach-to-dialogue","title":"Action State Update Approach to Dialogue Management","date":"2020-11-09","arxiv_id":"2011.04637","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-resource-adaptation-of-neural-nlp-models","title":"Low-Resource Adaptation of Neural NLP Models","date":"2020-11-09","arxiv_id":"2011.04372","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-query-rewriting-in","title":"Personalized Query Rewriting in Conversational AI Agents","date":"2020-11-09","arxiv_id":"2011.04748","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-commonsense-question-answering-by","title":"Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources","date":"2020-11-05","arxiv_id":"2011.02705","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-model-is-all-you-need-natural","title":"Language Model is All You Need: Natural Language Understanding as Question Answering","date":"2020-11-05","arxiv_id":"2011.03023","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-beam-an-image-captioning-approach","title":"Attention Beam: An Image Captioning Approach","date":"2020-11-03","arxiv_id":"2011.01753","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-synthetic-data-for-task-oriented","title":"Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations","date":"2020-11-03","arxiv_id":"2011.02050","repositories_listed":0,"syntology":null},{"url":null,"slug":"warped-language-models-for-noise-robust","title":"Warped Language Models for Noise Robust Language Understanding","date":"2020-11-03","arxiv_id":"2011.01900","repositories_listed":0,"syntology":null},{"url":null,"slug":"deeply-embedded-knowledge-representation","title":"Deeply Embedded Knowledge Representation & Reasoning For Natural Language Question Answering: A Practitioner’s Perspective","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dr-summarize-global-summarization-of-medical-1","title":"Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures.","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-explainable-linguistic-expressions","title":"Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"monash-summ-longsumm-20-scisummpip-an","title":"Monash-Summ@LongSumm 20 SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-resolution-annotations-for-emoji","title":"Multi-resolution Annotations for Emoji Prediction","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"palm-pre-training-an-autoencoding-1","title":"PALM: Pre-training an Autoencoding\\&Autoregressive Language Model for Context-conditioned Generation","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qadiscourse-discourse-relations-as-qa-pairs-1","title":"QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines","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":"xglue-a-new-benchmark-datasetfor-cross","title":"XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-methods-for-semi-supervised-text","title":"Bayesian Methods for Semi-supervised Text Annotation","date":"2020-10-28","arxiv_id":"2010.14872","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploration-of-nlu-disassemble-the","title":"New Approaches for Natural Language Understanding based on the Idea that Natural Language encodes both Information and its Processing Procedures","date":"2020-10-24","arxiv_id":"2010.12789","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-scalable-framework-for-learning-from","title":"A scalable framework for learning from implicit user feedback to improve natural language understanding in large-scale conversational AI systems","date":"2020-10-23","arxiv_id":"2010.12251","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-hash-embedding-for-large-vocab","title":"Learning to Embed Categorical Features without Embedding Tables for Recommendation","date":"2020-10-21","arxiv_id":"2010.10784","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-chinese-end-to-end-spoken-language","title":"Ensemble Chinese End-to-End Spoken Language Understanding for Abnormal Event Detection from audio stream","date":"2020-10-19","arxiv_id":"2010.09235","repositories_listed":0,"syntology":null},{"url":null,"slug":"scisummpip-an-unsupervised-scientific-paper","title":"SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline","date":"2020-10-19","arxiv_id":"2010.09190","repositories_listed":0,"syntology":null},{"url":null,"slug":"coda-contrast-enhanced-and-diversity-1","title":"CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding","date":"2020-10-16","arxiv_id":"2010.08670","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-shallow-phrase-structure-parsers","title":"Training Shallow Phrase Structure Parsers From Semantic Frames and Simple NLG","date":"2020-10-15","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"capt-contrastive-pre-training-for","title":"CAPT: Contrastive Pre-Training for Learning Denoised Sequence Representations","date":"2020-10-13","arxiv_id":"2010.06351","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-argument-mining-datasets-and","title":"Multilingual Argument Mining: Datasets and Analysis","date":"2020-10-13","arxiv_id":"2010.06432","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-and-reducing-gendered-correlations","title":"Measuring and Reducing Gendered Correlations in Pre-trained Models","date":"2020-10-12","arxiv_id":"2010.06032","repositories_listed":0,"syntology":null},{"url":null,"slug":"don-t-parse-insert-multilingual-semantic","title":"Don't Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding","date":"2020-10-08","arxiv_id":"2010.03714","repositories_listed":0,"syntology":null},{"url":null,"slug":"dlgnet-task-an-end-to-end-neural-network","title":"DLGNet-Task: An End-to-end Neural Network Framework for Modeling Multi-turn Multi-domain Task-Oriented Dialogue","date":"2020-10-04","arxiv_id":"2010.01693","repositories_listed":0,"syntology":null},{"url":null,"slug":"personality-trait-detection-using-bagged-svm","title":"Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles","date":"2020-10-03","arxiv_id":"2010.01309","repositories_listed":0,"syntology":null},{"url":null,"slug":"chinese-long-and-short-form-choice-exploiting","title":"Chinese Long and Short Form Choice Exploiting Neural Network Language Modeling Approaches","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-improved-model-design-for-authorship","title":"Towards Improved Model Design for Authorship Identification: A Survey on Writing Style Understanding","date":"2020-09-30","arxiv_id":"2009.14445","repositories_listed":0,"syntology":null},{"url":null,"slug":"square-semantics-based-question-answering-and","title":"SQuARE: Semantics-based Question Answering and Reasoning Engine","date":"2020-09-22","arxiv_id":"2009.10239","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-severity-of-health-states-based","title":"Assessing the Severity of Health States based on Social Media Posts","date":"2020-09-21","arxiv_id":"2009.09600","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-mention-detector-linker","title":"Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution","date":"2020-09-20","arxiv_id":"2009.09363","repositories_listed":0,"syntology":null},{"url":null,"slug":"dr-summarize-global-summarization-of-medical","title":"Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures","date":"2020-09-18","arxiv_id":"2009.08666","repositories_listed":0,"syntology":null},{"url":"/paper/question-directed-graph-attention-network-for","slug":"question-directed-graph-attention-network-for","title":"Question Directed Graph Attention Network for Numerical Reasoning over Text","date":"2020-09-16","arxiv_id":"2009.07448","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-embeddings-using-multi-task","title":"Multi-modal embeddings using multi-task learning for emotion recognition","date":"2020-09-10","arxiv_id":"2009.05019","repositories_listed":0,"syntology":null},{"url":null,"slug":"ambert-a-pre-trained-language-model-with","title":"AMBERT: A Pre-trained Language Model with Multi-Grained Tokenization","date":"2020-08-27","arxiv_id":"2008.11869","repositories_listed":0,"syntology":null},{"url":null,"slug":"discrete-word-embedding-for-logical-natural","title":"Discrete Word Embedding for Logical Natural Language Understanding","date":"2020-08-26","arxiv_id":"2008.11649","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-models-as-few-shot-learner-for-task","title":"Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems","date":"2020-08-14","arxiv_id":"2008.06239","repositories_listed":0,"syntology":null},{"url":null,"slug":"speech-to-semantics-improve-asr-and-nlu","title":"Speech To Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces","date":"2020-08-14","arxiv_id":"2008.06173","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":"from-spatial-relations-to-spatial-1","title":"From Spatial Relations to Spatial Configurations","date":"2020-07-19","arxiv_id":"2007.09557","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-an-automated-soap-note-classifying","title":"Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations","date":"2020-07-17","arxiv_id":"2007.08749","repositories_listed":0,"syntology":null},{"url":null,"slug":"hypergrid-efficient-multi-task-transformers","title":"HyperGrid: Efficient Multi-Task Transformers with Grid-wise Decomposable Hyper Projections","date":"2020-07-12","arxiv_id":"2007.05891","repositories_listed":0,"syntology":null},{"url":null,"slug":"logic-language-and-calculus","title":"Logic, Language, and Calculus","date":"2020-07-06","arxiv_id":"2007.02484","repositories_listed":0,"syntology":null},{"url":null,"slug":"pretrained-semantic-speech-embeddings-for-end","title":"Pretrained Semantic Speech Embeddings for End-to-End Spoken Language Understanding via Cross-Modal Teacher-Student Learning","date":"2020-07-03","arxiv_id":"2007.01836","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-semantic-web-framework-for-automated-smart","title":"A Semantic Web Framework for Automated Smart Assistants: COVID-19 Case Study","date":"2020-07-01","arxiv_id":"2007.00747","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-wikipedia-categories-improve-masked","title":"Can Wikipedia Categories Improve Masked Language Model Pretraining?","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"climbing-towards-nlu-on-meaning-form-and","title":"Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"copybert-a-unified-approach-to-question","title":"CopyBERT: A Unified Approach to Question Generation with Self-Attention","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"curriculum-learning-for-natural-language","title":"Curriculum Learning for Natural Language Understanding","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hypernymy-detection-for-low-resource","title":"Hypernymy Detection for Low-Resource Languages via Meta Learning","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"intermediate-task-transfer-learning-with-1","title":"Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"programming-in-natural-language-with-fuse","title":"Programming in Natural Language with fuSE: Synthesizing Methods from Spoken Utterances Using Deep Natural Language Understanding","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"recursive-template-based-frame-generation-for","title":"Recursive Template-based Frame Generation for Task Oriented Dialog","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-alternate-representations-of-text-for","title":"Using Alternate Representations of Text for Natural Language Understanding","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"would-you-rather-a-new-benchmark-for-learning","title":"Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"conscious-intelligence-requires-lifelong","title":"Conscious Intelligence Requires Lifelong Autonomous Programming For General Purposes","date":"2020-06-30","arxiv_id":"2007.00001","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-machine-translation-for-paraphrase","title":"Neural Machine Translation For Paraphrase Generation","date":"2020-06-25","arxiv_id":"2006.14223","repositories_listed":0,"syntology":null},{"url":null,"slug":"chatbot-a-conversational-agent-employed-with","title":"Chatbot: A Conversational Agent employed with Named Entity Recognition Model using Artificial Neural Network","date":"2020-06-19","arxiv_id":"2007.04248","repositories_listed":0,"syntology":null},{"url":null,"slug":"consolidating-commonsense-knowledge","title":"Consolidating Commonsense Knowledge","date":"2020-06-10","arxiv_id":"2006.06114","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-for-training-dialog-models","title":"Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors","date":"2020-06-10","arxiv_id":"2006.05635","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-loss-application-to-entity-relation","title":"Semantic Loss Application to Entity Relation Recognition","date":"2020-06-07","arxiv_id":"2006.04031","repositories_listed":0,"syntology":null},{"url":null,"slug":"conversational-machine-comprehension-a","title":"Conversational Machine Comprehension: a Literature Review","date":"2020-06-01","arxiv_id":"2006.00671","repositories_listed":0,"syntology":null},{"url":null,"slug":"noise-robust-named-entity-understanding-for","title":"Noise Robust Named Entity Understanding for Voice Assistants","date":"2020-05-29","arxiv_id":"2005.14408","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-intent-inference-for-web-search-and","title":"User Intent Inference for Web Search and Conversational Agents","date":"2020-05-28","arxiv_id":"2005.13808","repositories_listed":0,"syntology":null},{"url":null,"slug":"syntactic-structure-distillation-pretraining","title":"Syntactic Structure Distillation Pretraining For Bidirectional Encoders","date":"2020-05-27","arxiv_id":"2005.13482","repositories_listed":0,"syntology":null},{"url":null,"slug":"history-aware-question-answering-in-a-blocks","title":"History-Aware Question Answering in a Blocks World Dialogue System","date":"2020-05-26","arxiv_id":"2005.12501","repositories_listed":0,"syntology":null},{"url":null,"slug":"refining-implicit-argument-annotation-for","title":"Refining Implicit Argument Annotation for UCCA","date":"2020-05-26","arxiv_id":"2005.12889","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-complex-kbqa-system-using-multiple","title":"A Complex KBQA System using Multiple Reasoning Paths","date":"2020-05-22","arxiv_id":"2005.10970","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-discovery-of-novel-intents-domains","title":"Automatic Discovery of Novel Intents & Domains from Text Utterances","date":"2020-05-22","arxiv_id":"2006.01208","repositories_listed":0,"syntology":null}],"record_sha256":"9facc139bc274945ff3c06aa20adb5de834a85b2c08a3ff3afeef95c4a8549bb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}