{"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/7","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":7,"pages_in_order":20,"rows_per_page":100,"rows":[601,700],"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/6","next":"/task/natural-language-understanding/papers/8","papers":[{"url":"/paper/event-time-extraction-and-propagation-via","slug":"event-time-extraction-and-propagation-via","title":"Event Time Extraction and Propagation via Graph Attention Networks","date":"2021-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/what-ingredients-make-for-an-effective","slug":"what-ingredients-make-for-an-effective","title":"What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?","date":"2021-06-01","arxiv_id":"2106.00794","repositories_listed":1,"syntology":null},{"url":"/paper/hiddencut-simple-data-augmentation-for","slug":"hiddencut-simple-data-augmentation-for","title":"HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalization","date":"2021-05-31","arxiv_id":"2106.00149","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/hiddencut-simple-data-augmentation-for#ran","syntology_url":"https://syntology.ai/paper/2106.00149","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.00149"}},"official":{"repos":["GT-SALT/HiddenCut"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/defending-pre-trained-language-models-from","slug":"defending-pre-trained-language-models-from","title":"Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice","date":"2021-05-30","arxiv_id":"2105.14553","repositories_listed":1,"syntology":null},{"url":"/paper/towards-target-dependent-sentiment","slug":"towards-target-dependent-sentiment","title":"Towards Target-dependent Sentiment Classification in News Articles","date":"2021-05-20","arxiv_id":"2105.09660","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-gender-bias-in-natural-language-1","slug":"evaluating-gender-bias-in-natural-language-1","title":"Evaluating Gender Bias in Natural Language Inference","date":"2021-05-12","arxiv_id":"2105.05541","repositories_listed":1,"syntology":null},{"url":"/paper/news-headline-grouping-as-a-challenging-nlu-1","slug":"news-headline-grouping-as-a-challenging-nlu-1","title":"News Headline Grouping as a Challenging NLU Task","date":"2021-05-12","arxiv_id":"2105.05391","repositories_listed":1,"syntology":null},{"url":"/paper/translation-quality-assessment-a-brief-survey","slug":"translation-quality-assessment-a-brief-survey","title":"Translation Quality Assessment: A Brief Survey on Manual and Automatic Methods","date":"2021-05-05","arxiv_id":"2105.03311","repositories_listed":1,"syntology":null},{"url":"/paper/reckonition-a-nlp-based-system-for-industrial","slug":"reckonition-a-nlp-based-system-for-industrial","title":"RECKONition: a NLP-based system for Industrial Accidents at Work Prevention","date":"2021-04-29","arxiv_id":"2104.14150","repositories_listed":1,"syntology":null},{"url":"/paper/x-metra-ada-cross-lingual-meta-transfer","slug":"x-metra-ada-cross-lingual-meta-transfer","title":"X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering","date":"2021-04-20","arxiv_id":"2104.09696","repositories_listed":1,"syntology":null},{"url":"/paper/extracting-temporal-event-relation-with","slug":"extracting-temporal-event-relation-with","title":"Extracting Temporal Event Relation with Syntax-guided Graph Transformer","date":"2021-04-19","arxiv_id":"2104.09570","repositories_listed":1,"syntology":null},{"url":"/paper/americasnli-evaluating-zero-shot-natural","slug":"americasnli-evaluating-zero-shot-natural","title":"AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages","date":"2021-04-18","arxiv_id":"2104.08726","repositories_listed":1,"syntology":null},{"url":"/paper/intent-features-for-rich-natural-language","slug":"intent-features-for-rich-natural-language","title":"Intent Features for Rich Natural Language Understanding","date":"2021-04-18","arxiv_id":"2104.08701","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-active-learning-with-pretrained","slug":"bayesian-active-learning-with-pretrained","title":"On the Importance of Effectively Adapting Pretrained Language Models for Active Learning","date":"2021-04-16","arxiv_id":"2104.08320","repositories_listed":1,"syntology":null},{"url":"/paper/effect-of-vision-and-language-extensions-on","slug":"effect-of-vision-and-language-extensions-on","title":"Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language Models","date":"2021-04-16","arxiv_id":"2104.08066","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":4,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/effect-of-vision-and-language-extensions-on#ran","syntology_url":"https://syntology.ai/paper/2104.08066","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.08066"}},"official":{"repos":["alab-nii/eval_vl_glue"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/empowering-news-recommendation-with-pre","slug":"empowering-news-recommendation-with-pre","title":"Empowering News Recommendation with Pre-trained Language Models","date":"2021-04-15","arxiv_id":"2104.07413","repositories_listed":1,"syntology":null},{"url":"/paper/xtreme-r-towards-more-challenging-and-nuanced","slug":"xtreme-r-towards-more-challenging-and-nuanced","title":"XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation","date":"2021-04-15","arxiv_id":"2104.07412","repositories_listed":1,"syntology":null},{"url":"/paper/k-plug-knowledge-injected-pre-trained-1","slug":"k-plug-knowledge-injected-pre-trained-1","title":"K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce","date":"2021-04-14","arxiv_id":"2104.06960","repositories_listed":1,"syntology":null},{"url":"/paper/targeted-adversarial-training-for-natural","slug":"targeted-adversarial-training-for-natural","title":"Targeted Adversarial Training for Natural Language Understanding","date":"2021-04-12","arxiv_id":"2104.05847","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-training-as-stackelberg-game-an","slug":"adversarial-training-as-stackelberg-game-an","title":"Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach","date":"2021-04-11","arxiv_id":"2104.04886","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adversarial-training-as-stackelberg-game-an#ran","syntology_url":"https://syntology.ai/paper/2104.04886","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.04886"}},"official":{"repos":["SimiaoZuo/Stackelberg-Adv"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/intent-detection-and-slot-filling-for","slug":"intent-detection-and-slot-filling-for","title":"Intent Detection and Slot Filling for Vietnamese","date":"2021-04-05","arxiv_id":"2104.02021","repositories_listed":1,"syntology":null},{"url":"/paper/how-certain-is-your-transformer","slug":"how-certain-is-your-transformer","title":"How Certain is Your Transformer?","date":"2021-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/is-the-understanding-of-explicit-discourse","slug":"is-the-understanding-of-explicit-discourse","title":"Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?","date":"2021-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/low-resource-language-modelling-of-south","slug":"low-resource-language-modelling-of-south","title":"Low-Resource Language Modelling of South African Languages","date":"2021-04-01","arxiv_id":"2104.00772","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-relational-encoding-in-language","slug":"rethinking-relational-encoding-in-language","title":"Structure Inducing Pre-Training","date":"2021-03-18","arxiv_id":"2103.10334","repositories_listed":1,"syntology":null},{"url":"/paper/reweighting-augmented-samples-by-minimizing-1","slug":"reweighting-augmented-samples-by-minimizing-1","title":"Reweighting Augmented Samples by Minimizing the Maximal Expected Loss","date":"2021-03-16","arxiv_id":"2103.08933","repositories_listed":1,"syntology":null},{"url":"/paper/multilingual-code-switching-for-zero-shot","slug":"multilingual-code-switching-for-zero-shot","title":"Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling","date":"2021-03-13","arxiv_id":"2103.07792","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multilingual-code-switching-for-zero-shot#ran","syntology_url":"https://syntology.ai/paper/2103.07792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.07792"}},"official":{"repos":["jitinkrishnan/Multilingual-ZeroShot-SlotFilling"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/empathetic-bert2bert-conversational-model","slug":"empathetic-bert2bert-conversational-model","title":"Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data","date":"2021-03-07","arxiv_id":"2103.04353","repositories_listed":1,"syntology":null},{"url":"/paper/syntax-bert-improving-pre-trained","slug":"syntax-bert-improving-pre-trained","title":"Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees","date":"2021-03-07","arxiv_id":"2103.04350","repositories_listed":1,"syntology":null},{"url":"/paper/transformer-is-all-you-need-multimodal","slug":"transformer-is-all-you-need-multimodal","title":"UniT: Multimodal Multitask Learning with a Unified Transformer","date":"2021-02-22","arxiv_id":"2102.10772","repositories_listed":1,"syntology":null},{"url":"/paper/training-vision-transformers-for-image","slug":"training-vision-transformers-for-image","title":"Training Vision Transformers for Image Retrieval","date":"2021-02-10","arxiv_id":"2102.05644","repositories_listed":1,"syntology":null},{"url":"/paper/confusion2vec-2-0-enriching-ambiguous-spoken","slug":"confusion2vec-2-0-enriching-ambiguous-spoken","title":"Confusion2vec 2.0: Enriching Ambiguous Spoken Language Representations with Subwords","date":"2021-02-03","arxiv_id":"2102.02270","repositories_listed":1,"syntology":null},{"url":"/paper/commonsense-knowledge-mining-from-term","slug":"commonsense-knowledge-mining-from-term","title":"Commonsense Knowledge Mining from Term Definitions","date":"2021-02-01","arxiv_id":"2102.00651","repositories_listed":1,"syntology":null},{"url":"/paper/fine-tuning-bert-based-models-for-plant","slug":"fine-tuning-bert-based-models-for-plant","title":"Fine-tuning BERT-based models for Plant Health Bulletin Classification","date":"2021-01-29","arxiv_id":"2102.00838","repositories_listed":1,"syntology":null},{"url":"/paper/lsoie-a-large-scale-dataset-for-supervised","slug":"lsoie-a-large-scale-dataset-for-supervised","title":"LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction","date":"2021-01-27","arxiv_id":"2101.11177","repositories_listed":1,"syntology":null},{"url":"/paper/visualmrc-machine-reading-comprehension-on","slug":"visualmrc-machine-reading-comprehension-on","title":"VisualMRC: Machine Reading Comprehension on Document Images","date":"2021-01-27","arxiv_id":"2101.11272","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-transitivity-in-neural-nli-models","slug":"exploring-transitivity-in-neural-nli-models","title":"Exploring Transitivity in Neural NLI Models through Veridicality","date":"2021-01-26","arxiv_id":"2101.10713","repositories_listed":1,"syntology":null},{"url":"/paper/spark-nlp-natural-language-understanding-at","slug":"spark-nlp-natural-language-understanding-at","title":"Spark NLP: Natural Language Understanding at Scale","date":"2021-01-26","arxiv_id":"2101.10848","repositories_listed":1,"syntology":null},{"url":"/paper/romebert-robust-training-of-multi-exit-bert","slug":"romebert-robust-training-of-multi-exit-bert","title":"RomeBERT: Robust Training of Multi-Exit BERT","date":"2021-01-24","arxiv_id":"2101.09755","repositories_listed":1,"syntology":null},{"url":"/paper/training-multilingual-pre-trained-language","slug":"training-multilingual-pre-trained-language","title":"Training Multilingual Pre-trained Language Model with Byte-level Subwords","date":"2021-01-23","arxiv_id":"2101.09469","repositories_listed":1,"syntology":null},{"url":"/paper/multi-sense-embeddings-through-a-word-sense","slug":"multi-sense-embeddings-through-a-word-sense","title":"Multi-sense embeddings through a word sense disambiguation process","date":"2021-01-21","arxiv_id":"2101.08700","repositories_listed":1,"syntology":null},{"url":"/paper/joint-energy-based-model-training-for-better","slug":"joint-energy-based-model-training-for-better","title":"Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models","date":"2021-01-18","arxiv_id":"2101.06829","repositories_listed":1,"syntology":null},{"url":"/paper/banglabert-combating-embedding-barrier-for","slug":"banglabert-combating-embedding-barrier-for","title":"BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla","date":"2021-01-01","arxiv_id":"2101.00204","repositories_listed":1,"syntology":null},{"url":"/paper/k-plug-knowledge-injected-pre-trained","slug":"k-plug-knowledge-injected-pre-trained","title":"K-PLUG: KNOWLEDGE-INJECTED PRE-TRAINED LANGUAGE MODEL FOR NATURAL LANGUAGE UNDERSTANDING AND GENERATION","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/coreference-reasoning-in-machine-reading","slug":"coreference-reasoning-in-machine-reading","title":"Coreference Reasoning in Machine Reading Comprehension","date":"2020-12-31","arxiv_id":"2012.15573","repositories_listed":1,"syntology":null},{"url":"/paper/unnatural-language-inference","slug":"unnatural-language-inference","title":"UnNatural Language Inference","date":"2020-12-30","arxiv_id":"2101.00010","repositories_listed":1,"syntology":null},{"url":"/paper/sit3-code-summarization-with-structure","slug":"sit3-code-summarization-with-structure","title":"Code Summarization with Structure-induced Transformer","date":"2020-12-29","arxiv_id":"2012.14710","repositories_listed":1,"syntology":null},{"url":"/paper/medal-medical-abbreviation-disambiguation","slug":"medal-medical-abbreviation-disambiguation","title":"MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining","date":"2020-12-27","arxiv_id":"2012.13978","repositories_listed":1,"syntology":null},{"url":"/paper/my-teacher-thinks-the-world-is-flat","slug":"my-teacher-thinks-the-world-is-flat","title":"My Teacher Thinks The World Is Flat! Interpreting Automatic Essay Scoring Mechanism","date":"2020-12-27","arxiv_id":"2012.13872","repositories_listed":1,"syntology":null},{"url":"/paper/cskg-the-commonsense-knowledge-graph","slug":"cskg-the-commonsense-knowledge-graph","title":"CSKG: The CommonSense Knowledge Graph","date":"2020-12-21","arxiv_id":"2012.11490","repositories_listed":1,"syntology":null},{"url":"/paper/parsinlu-a-suite-of-language-understanding","slug":"parsinlu-a-suite-of-language-understanding","title":"ParsiNLU: A Suite of Language Understanding Challenges for Persian","date":"2020-12-11","arxiv_id":"2012.06154","repositories_listed":1,"syntology":null},{"url":"/paper/infusing-finetuning-with-semantic","slug":"infusing-finetuning-with-semantic","title":"Infusing Finetuning with Semantic Dependencies","date":"2020-12-10","arxiv_id":"2012.05395","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-analysis-of-existing-systems-and","slug":"an-empirical-analysis-of-existing-systems-and","title":"An empirical analysis of existing systems and datasets toward general simple question answering","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/intra-correlation-encoding-for-chinese","slug":"intra-correlation-encoding-for-chinese","title":"Intra-Correlation Encoding for Chinese Sentence Intention Matching","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/stil-simultaneous-slot-filling-translation-1","slug":"stil-simultaneous-slot-filling-translation-1","title":"STIL - Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using mBART on MultiATIS++","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/braid-weaving-symbolic-and-statistical","slug":"braid-weaving-symbolic-and-statistical","title":"Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations","date":"2020-11-26","arxiv_id":"2011.13354","repositories_listed":1,"syntology":null},{"url":"/paper/glge-a-new-general-language-generation","slug":"glge-a-new-general-language-generation","title":"GLGE: A New General Language Generation Evaluation Benchmark","date":"2020-11-24","arxiv_id":"2011.11928","repositories_listed":1,"syntology":null},{"url":"/paper/fact-level-extractive-summarization-with","slug":"fact-level-extractive-summarization-with","title":"Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT","date":"2020-11-19","arxiv_id":"2011.09739","repositories_listed":1,"syntology":null},{"url":"/paper/a-sequence-to-sequence-approach-to-dialogue","slug":"a-sequence-to-sequence-approach-to-dialogue","title":"A Sequence-to-Sequence Approach to Dialogue State Tracking","date":"2020-11-18","arxiv_id":"2011.09553","repositories_listed":1,"syntology":null},{"url":"/paper/meta-learning-for-natural-language","slug":"meta-learning-for-natural-language","title":"Meta-Learning for Natural Language Understanding under Continual Learning Framework","date":"2020-11-03","arxiv_id":"2011.01452","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-contrastive-learning-for-pre-1","slug":"supervised-contrastive-learning-for-pre-1","title":"Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning","date":"2020-11-03","arxiv_id":"2011.01403","repositories_listed":1,"syntology":null},{"url":"/paper/adapting-pretrained-transformer-to-lattices","slug":"adapting-pretrained-transformer-to-lattices","title":"Adapting Pretrained Transformer to Lattices for Spoken Language Understanding","date":"2020-11-02","arxiv_id":"2011.00780","repositories_listed":1,"syntology":null},{"url":"/paper/arbml-democritizing-arabic-natural-language","slug":"arbml-democritizing-arabic-natural-language","title":"ARBML: Democritizing Arabic Natural Language Processing Tools","date":"2020-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/detecting-entailment-in-code-mixed-hindi","slug":"detecting-entailment-in-code-mixed-hindi","title":"Detecting Entailment in Code-Mixed Hindi-English Conversations","date":"2020-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/integrating-task-specific-information-into","slug":"integrating-task-specific-information-into","title":"Integrating Task Specific Information into Pretrained Language Models for Low Resource Fine Tuning","date":"2020-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pre-training-text-to-text-transformers-for","slug":"pre-training-text-to-text-transformers-for","title":"Pre-training Text-to-Text Transformers for Concept-centric Common Sense","date":"2020-10-24","arxiv_id":"2011.07956","repositories_listed":1,"syntology":null},{"url":"/paper/towards-interpretable-natural-language","slug":"towards-interpretable-natural-language","title":"Towards Interpretable Natural Language Understanding with Explanations as Latent Variables","date":"2020-10-24","arxiv_id":"2011.05268","repositories_listed":1,"syntology":{"n":14,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":12,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 12 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/towards-interpretable-natural-language#ran","syntology_url":"https://syntology.ai/paper/2011.05268","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.05268"}},"official":{"repos":["JamesHujy/ELV"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":12,"ran_from_kinds":["official"]}}},{"url":"/paper/evidential-sparsification-of-multimodal","slug":"evidential-sparsification-of-multimodal","title":"Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders","date":"2020-10-19","arxiv_id":"2010.09164","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":3,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/evidential-sparsification-of-multimodal#ran","syntology_url":"https://syntology.ai/paper/2010.09164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.09164"}},"official":{"repos":["sisl/EvidentialSparsification"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/risawoz-a-large-scale-multi-domain-wizard-of","slug":"risawoz-a-large-scale-multi-domain-wizard-of","title":"RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling","date":"2020-10-17","arxiv_id":"2010.08738","repositories_listed":1,"syntology":null},{"url":"/paper/improving-constituency-parsing-with-span","slug":"improving-constituency-parsing-with-span","title":"Improving Constituency Parsing with Span Attention","date":"2020-10-15","arxiv_id":"2010.07543","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/improving-constituency-parsing-with-span#ran","syntology_url":"https://syntology.ai/paper/2010.07543","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.07543"}},"official":{"repos":["cuhksz-nlp/SAPar"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pretrained-language-models-for-dialogue","slug":"pretrained-language-models-for-dialogue","title":"Pretrained Language Models for Dialogue Generation with Multiple Input Sources","date":"2020-10-15","arxiv_id":"2010.07576","repositories_listed":1,"syntology":null},{"url":"/paper/incorporating-bert-into-parallel-sequence","slug":"incorporating-bert-into-parallel-sequence","title":"Incorporating BERT into Parallel Sequence Decoding with Adapters","date":"2020-10-13","arxiv_id":"2010.06138","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/incorporating-bert-into-parallel-sequence#ran","syntology_url":"https://syntology.ai/paper/2010.06138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.06138"}},"official":{"repos":["lemmonation/abnet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/probing-for-multilingual-numerical","slug":"probing-for-multilingual-numerical","title":"Probing for Multilingual Numerical Understanding in Transformer-Based Language Models","date":"2020-10-13","arxiv_id":"2010.06666","repositories_listed":1,"syntology":null},{"url":"/paper/joint-semantic-analysis-with-document-level","slug":"joint-semantic-analysis-with-document-level","title":"Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards","date":"2020-10-12","arxiv_id":"2010.05567","repositories_listed":1,"syntology":null},{"url":"/paper/texthide-tackling-data-privacy-in-language","slug":"texthide-tackling-data-privacy-in-language","title":"TextHide: Tackling Data Privacy in Language Understanding Tasks","date":"2020-10-12","arxiv_id":"2010.06053","repositories_listed":1,"syntology":null},{"url":"/paper/counterfactually-augmented-snli-training-data","slug":"counterfactually-augmented-snli-training-data","title":"Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data","date":"2020-10-09","arxiv_id":"2010.04762","repositories_listed":1,"syntology":{"n":16,"n_ran":16,"n_constructed":0,"n_ran_checked":15,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/counterfactually-augmented-snli-training-data#ran","syntology_url":"https://syntology.ai/paper/2010.04762","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.04762"}},"official":{"repos":["nyu-mll/CNLI-generalization"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pragmatically-informative-color-generation-by","slug":"pragmatically-informative-color-generation-by","title":"Pragmatically Informative Color Generation by Grounding Contextual Modifiers","date":"2020-10-09","arxiv_id":"2010.04372","repositories_listed":1,"syntology":null},{"url":"/paper/dual-inference-for-improving-language","slug":"dual-inference-for-improving-language","title":"Dual Inference for Improving Language Understanding and Generation","date":"2020-10-08","arxiv_id":"2010.04246","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-study-of-tokenization-strategies","slug":"an-empirical-study-of-tokenization-strategies","title":"An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks","date":"2020-10-06","arxiv_id":"2010.02534","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/an-empirical-study-of-tokenization-strategies#ran","syntology_url":"https://syntology.ai/paper/2010.02534","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.02534"}},"official":{"repos":["kakaobrain/kortok"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/bert-knows-punta-cana-is-not-just-beautiful","slug":"bert-knows-punta-cana-is-not-just-beautiful","title":"BERT Knows Punta Cana is not just beautiful, it's gorgeous: Ranking Scalar Adjectives with Contextualised Representations","date":"2020-10-06","arxiv_id":"2010.02686","repositories_listed":1,"syntology":null},{"url":"/paper/qadiscourse-discourse-relations-as-qa-pairs","slug":"qadiscourse-discourse-relations-as-qa-pairs","title":"QADiscourse -- Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines","date":"2020-10-06","arxiv_id":"2010.02815","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-generalize-for-sequential","slug":"learning-to-generalize-for-sequential","title":"Learning to Generalize for Sequential Decision Making","date":"2020-10-05","arxiv_id":"2010.02229","repositories_listed":1,"syntology":null},{"url":"/paper/self-training-improves-pre-training-for","slug":"self-training-improves-pre-training-for","title":"Self-training Improves Pre-training for Natural Language Understanding","date":"2020-10-05","arxiv_id":"2010.02194","repositories_listed":1,"syntology":null},{"url":"/paper/stil-simultaneous-slot-filling-translation","slug":"stil-simultaneous-slot-filling-translation","title":"STIL -- Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using mBART on MultiATIS++","date":"2020-10-02","arxiv_id":"2010.00760","repositories_listed":1,"syntology":null},{"url":"/paper/neural-rst-based-evaluation-of-discourse","slug":"neural-rst-based-evaluation-of-discourse","title":"Neural RST-based Evaluation of Discourse Coherence","date":"2020-09-30","arxiv_id":"2009.14463","repositories_listed":1,"syntology":null},{"url":"/paper/dialoglue-a-natural-language-understanding","slug":"dialoglue-a-natural-language-understanding","title":"DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue","date":"2020-09-28","arxiv_id":"2009.13570","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adversarial-fine-tuning-as-an","slug":"domain-adversarial-fine-tuning-as-an","title":"Domain Adversarial Fine-Tuning as an Effective Regularizer","date":"2020-09-28","arxiv_id":"2009.13366","repositories_listed":1,"syntology":null},{"url":"/paper/towards-extracting-absolute-event-timelines","slug":"towards-extracting-absolute-event-timelines","title":"Towards Extracting Absolute Event Timelines From English Clinical Reports","date":"2020-09-28","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-brief-survey-and-comparative-study-of","slug":"a-brief-survey-and-comparative-study-of","title":"A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution","date":"2020-09-27","arxiv_id":"2009.12721","repositories_listed":1,"syntology":null},{"url":"/paper/ape210k-a-large-scale-and-template-rich","slug":"ape210k-a-large-scale-and-template-rich","title":"Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems","date":"2020-09-24","arxiv_id":"2009.11506","repositories_listed":1,"syntology":null},{"url":"/paper/vector-projection-network-for-few-shot-slot","slug":"vector-projection-network-for-few-shot-slot","title":"Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding","date":"2020-09-21","arxiv_id":"2009.09568","repositories_listed":1,"syntology":null},{"url":"/paper/domain-knowledge-empowered-structured-neural","slug":"domain-knowledge-empowered-structured-neural","title":"Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction","date":"2020-09-15","arxiv_id":"2009.07373","repositories_listed":1,"syntology":null},{"url":"/paper/is-this-sentence-valid-an-arabic-dataset-for","slug":"is-this-sentence-valid-an-arabic-dataset-for","title":"Is this sentence valid? An Arabic Dataset for Commonsense Validation","date":"2020-08-25","arxiv_id":"2008.10873","repositories_listed":1,"syntology":null},{"url":"/paper/how-to-evaluate-your-dialogue-system-probe","slug":"how-to-evaluate-your-dialogue-system-probe","title":"How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for Token-level Evaluation Metrics","date":"2020-08-24","arxiv_id":"2008.10427","repositories_listed":1,"syntology":null},{"url":"/paper/visualsem-a-high-quality-knowledge-graph-for","slug":"visualsem-a-high-quality-knowledge-graph-for","title":"VisualSem: A High-quality Knowledge Graph for Vision and Language","date":"2020-08-20","arxiv_id":"2008.09150","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-guarantees-for-de-identifying-text","slug":"privacy-guarantees-for-de-identifying-text","title":"Privacy Guarantees for De-identifying Text Transformations","date":"2020-08-07","arxiv_id":"2008.03101","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-for-deep-models-in","slug":"self-supervised-learning-for-deep-models-in","title":"Self-supervised Learning for Large-scale Item Recommendations","date":"2020-07-25","arxiv_id":"2007.12865","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-learning-for-deep-models-in#ran","syntology_url":"https://syntology.ai/paper/2007.12865","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.12865"}},"official":null}},{"url":"/paper/mono-vs-multilingual-transformer-based-models","slug":"mono-vs-multilingual-transformer-based-models","title":"Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks","date":"2020-07-19","arxiv_id":"2007.09757","repositories_listed":1,"syntology":null},{"url":"/paper/towards-debiasing-sentence-representations-1","slug":"towards-debiasing-sentence-representations-1","title":"Towards Debiasing Sentence Representations","date":"2020-07-16","arxiv_id":"2007.08100","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-debiasing-sentence-representations-1#ran","syntology_url":"https://syntology.ai/paper/2007.08100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.08100"}},"official":{"repos":["pliang279/sent_debias"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/calling-out-bluff-attacking-the-robustness-of","slug":"calling-out-bluff-attacking-the-robustness-of","title":"Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems","date":"2020-07-14","arxiv_id":"2007.06796","repositories_listed":1,"syntology":null}],"record_sha256":"925e2c0f7101cca9176a59333c9e60113bade5e697441d4a0a5223f8cb7eafbc","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}