{"list":"other","total":55,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"1910.13461","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","created":"2019-10-29","cited_by_count":5893},{"id":"1804.07461","title":"GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding","created":"2018-04-20","cited_by_count":4899},{"id":"2001.08210","title":"Multilingual Denoising Pre-training for Neural Machine Translation","created":"2020-01-22","cited_by_count":1035},{"id":"1906.04341","title":"What Does BERT Look At? An Analysis of BERT's Attention","created":"2019-06-11","cited_by_count":1003},{"id":"1805.12471","title":"Neural Network Acceptability Judgments","created":"2018-05-31","cited_by_count":958},{"id":"2002.12327","title":"A Primer in BERTology: What we know about how BERT works","created":"2020-02-27","cited_by_count":815},{"id":"1909.03368","title":"Designing and Interpreting Probes with Control Tasks","created":"2019-09-08","cited_by_count":640},{"id":"1912.00582","title":"BLiMP: The Benchmark of Linguistic Minimal Pairs for English","created":"2019-12-02","cited_by_count":564},{"id":"1805.01070","title":"What you can cram into a single vector: Probing sentence embeddings for linguistic properties","created":"2018-05-03","cited_by_count":457},{"id":"1905.06316","title":"What do you learn from context? Probing for sentence structure in contextualized word representations","created":"2019-05-15","cited_by_count":438},{"id":"1903.08855","title":"Linguistic Knowledge and Transferability of Contextual Representations","created":"2019-03-21","cited_by_count":383},{"id":"1907.13528","title":"What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models","created":"2019-07-31","cited_by_count":320},{"id":"1812.08951","title":"Analysis Methods in Neural Language Processing: A Survey","created":"2018-12-21","cited_by_count":277},{"id":"1901.05287","title":"Assessing BERT's Syntactic Abilities","created":"2019-01-16","cited_by_count":192},{"id":"2104.06644","title":"Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little","created":"2021-04-14","cited_by_count":177},{"id":"1903.03260","title":"Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State","created":"2019-03-08","cited_by_count":138},{"id":"1906.01698","title":"Open Sesame: Getting Inside BERT's Linguistic Knowledge","created":"2019-06-04","cited_by_count":120},{"id":"1805.04623","title":"Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context","created":"2018-05-12","cited_by_count":113},{"id":"1908.04577","title":"StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding","created":"2019-08-13","cited_by_count":109},{"id":"2004.14786","title":"Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT","created":"2020-04-30","cited_by_count":109},{"id":"2102.11090","title":"Position Information in Transformers: An Overview","created":"2021-02-22","cited_by_count":103},{"id":"1602.03483","title":"Learning Distributed Representations of Sentences from Unlabelled Data","created":"2016-02-10","cited_by_count":91},{"id":"1911.12246","title":"Do Attention Heads in BERT Track Syntactic Dependencies?","created":"2019-11-27","cited_by_count":88},{"id":"2012.15180","title":"Out of Order: How Important Is The Sequential Order of Words in a Sentence in Natural Language Understanding Tasks?","created":"2020-12-30","cited_by_count":73},{"id":"2101.00010","title":"UnNatural Language Inference","created":"2020-12-30","cited_by_count":69},{"id":"2010.04903","title":"What Do Position Embeddings Learn? An Empirical Study of Pre-Trained Language Model Positional Encoding","created":"2020-10-10","cited_by_count":52},{"id":"2103.04350","title":"Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees","created":"2021-03-07","cited_by_count":51},{"id":"2106.08367","title":"What Context Features Can Transformer Language Models Use?","created":"2021-06-15","cited_by_count":50},{"id":"2003.07278","title":"A Survey on Contextual Embeddings","created":"2020-03-16","cited_by_count":48},{"id":"2008.09084","title":"Do Syntax Trees Help Pre-trained Transformers Extract Information?","created":"2020-08-20","cited_by_count":48},{"id":"1906.01603","title":"Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study","created":"2019-06-04","cited_by_count":47},{"id":"2002.00737","title":"Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction","created":"2020-01-30","cited_by_count":47},{"id":"1911.09241","title":"Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets","created":"2019-11-21","cited_by_count":46},{"id":"2101.11131","title":"CLiMP: A Benchmark for Chinese Language Model Evaluation","created":"2021-01-26","cited_by_count":46},{"id":"1903.09460","title":"Data Augmentation via Dependency Tree Morphing for Low-Resource Languages","created":"2019-03-22","cited_by_count":45},{"id":"2101.03453","title":"BERT & Family Eat Word Salad: Experiments with Text Understanding","created":"2021-01-10","cited_by_count":44},{"id":"2011.04393","title":"Positional Artefacts Propagate Through Masked Language Model Embeddings","created":"2020-11-09","cited_by_count":35},{"id":"1910.12391","title":"What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?","created":"2019-10-28","cited_by_count":30},{"id":"2106.02559","title":"Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing","created":"2021-06-04","cited_by_count":28},{"id":"1811.07033","title":"Analyzing Compositionality-Sensitivity of NLI Models","created":"2018-11-16","cited_by_count":26},{"id":"2004.00881","title":"How Furiously Can Colourless Green Ideas Sleep? Sentence Acceptability in Context","created":"2020-04-02","cited_by_count":26},{"id":"1809.01329","title":"RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency","created":"2018-09-05","cited_by_count":23},{"id":"2010.01869","title":"Linguistic Profiling of a Neural Language Model","created":"2020-10-05","cited_by_count":23},{"id":"1903.09442","title":"LINSPECTOR: Multilingual Probing Tasks for Word Representations","created":"2019-03-22","cited_by_count":22},{"id":"2002.01685","title":"Parsing as Pretraining","created":"2020-02-05","cited_by_count":19},{"id":"2105.06681","title":"DaLAJ - a dataset for linguistic acceptability judgments for Swedish: Format, baseline, sharing","created":"2021-05-14","cited_by_count":16},{"id":"2104.07578","title":"Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models","created":"2021-04-15","cited_by_count":14},{"id":"1906.05061","title":"Probing Multilingual Sentence Representations With X-Probe","created":"2019-06-12","cited_by_count":13},{"id":"1906.11511","title":"Inducing Syntactic Trees from BERT Representations","created":"2019-06-27","cited_by_count":11},{"id":"2106.02134","title":"Syntax-augmented Multilingual BERT for Cross-lingual Transfer","created":"2021-06-03","cited_by_count":11},{"id":"2006.09109","title":"How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation","created":"2020-06-16","cited_by_count":7},{"id":"1901.03438","title":"Linguistic Analysis of Pretrained Sentence Encoders with Acceptability Judgments","created":"2019-01-11","cited_by_count":4}],"next_cursor":null}