暂无分享,去创建一个
Anders Sogaard | Mostafa Abdou | Bastien Li'etard | Anders Søgaard | Mostafa Abdou | Bastien Liétard
[1] Georgiana Dinu,et al. Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors , 2014, ACL.
[2] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[3] Anders Søgaard,et al. On the Limitations of Unsupervised Bilingual Dictionary Induction , 2018, ACL.
[4] Claire Cardie,et al. Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings , 2020, ACL.
[5] Graham Neubig,et al. How Can We Know What Language Models Know? , 2019, Transactions of the Association for Computational Linguistics.
[6] Yejin Choi,et al. Do Neural Language Representations Learn Physical Commonsense? , 2019, CogSci.
[7] Laure Thompson,et al. The strange geometry of skip-gram with negative sampling , 2017, EMNLP.
[8] Kawin Ethayarajh,et al. How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings , 2019, EMNLP.
[9] Ilya Sutskever,et al. Language Models are Unsupervised Multitask Learners , 2019 .
[10] Marie-Francine Moens,et al. Is an Image Worth More than a Thousand Words? On the Fine-Grain Semantic Differences between Visual and Linguistic Representations , 2016, COLING.
[11] Anders Sogaard,et al. Are All Good Word Vector Spaces Isomorphic? , 2020, EMNLP.
[12] Felix Hill,et al. SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation , 2014, CL.
[13] Anders Sogaard,et al. Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color , 2021, CONLL.
[14] Dipanjan Das,et al. BERT Rediscovers the Classical NLP Pipeline , 2019, ACL.
[15] Eneko Agirre,et al. A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches , 2009, NAACL.
[16] Alexander M. Rush,et al. Commonsense Knowledge Mining from Pretrained Models , 2019, EMNLP.
[17] Hinrich Schütze,et al. Intrinsic Subspace Evaluation of Word Embedding Representations , 2016, ACL.
[18] Willem Zuidema,et al. Blackbox Meets Blackbox: Representational Similarity & Stability Analysis of Neural Language Models and Brains , 2019, BlackboxNLP@ACL.
[19] Mostafa Abdou,et al. MGAD: Multilingual Generation of Analogy Datasets , 2018, LREC.
[20] Colin Raffel,et al. How Much Knowledge Can You Pack into the Parameters of a Language Model? , 2020, EMNLP.
[21] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[22] H. Schütze,et al. Dimensions of meaning , 1992, Supercomputing '92.
[23] Sebastian Riedel,et al. Language Models as Knowledge Bases? , 2019, EMNLP.
[24] Jon Gauthier,et al. Are Distributional Representations Ready for the Real World? Evaluating Word Vectors for Grounded Perceptual Meaning , 2017, RoboNLP@ACL.
[25] Danqi Chen,et al. of the Association for Computational Linguistics: , 2001 .
[26] Satoshi Matsuoka,et al. Word Embeddings, Analogies, and Machine Learning: Beyond king - man + woman = queen , 2016, COLING.
[27] John Hewitt,et al. Designing and Interpreting Probes with Control Tasks , 2019, EMNLP.
[28] Martin Wattenberg,et al. Visualizing and Measuring the Geometry of BERT , 2019, NeurIPS.
[29] Roy Schwartz,et al. How Well Do Distributional Models Capture Different Types of Semantic Knowledge? , 2015, ACL.
[30] Gemma Boleda,et al. Distributional Semantics in Technicolor , 2012, ACL.