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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 6 Hits 61 – 80 of 116 61 Universal Phone Recognition with a Multilingual Allophone System ... Li, Xinjian; Dalmia, Siddharth; Li, Juncheng. - : arXiv, 2020 BASE Show details 62 The Return of Lexical Dependencies: Neural Lexicalized PCFGs ... Zhu, Hao; Bisk, Yonatan; Neubig, Graham. - : arXiv, 2020 BASE Show details 63 XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization ... Hu, Junjie; Ruder, Sebastian; Siddhant, Aditya. - : arXiv, 2020 BASE Show details 64 X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models ... Jiang, Zhengbao; Anastasopoulos, Antonios; Araki, Jun. - : arXiv, 2020 BASE Show details 65 AlloVera: a multilingual allophone database Mortensen, David,; Li, Xinjian; Littell, Patrick... In: LREC 2020: 12th Language Resources and Evaluation Conference ; https://halshs.archives-ouvertes.fr/halshs-02527046 ; LREC 2020: 12th Language Resources and Evaluation Conference, European Language Resources Association, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/ (2020) BASE Show details 66 How Can We Know What Language Models Know? Jiang, Zhengbao; Xu, Frank F.; Araki, Jun; Neubig, Graham In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 423-438 (2020) (2020) Abstract: Recent work has presented intriguing results examining the knowledge contained in language models (LMs) by having the LM fill in the blanks of prompts such as “ Obama is a __ by profession”. These prompts are usually manually created, and quite possibly sub-optimal; another prompt such as “ Obama worked as a __ ” may result in more accurately predicting the correct profession. Because of this, given an inappropriate prompt, we might fail to retrieve facts that the LM does know, and thus any given prompt only provides a lower bound estimate of the knowledge contained in an LM. In this paper, we attempt to more accurately estimate the knowledge contained in LMs by automatically discovering better prompts to use in this querying process. Specifically, we propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different prompts. Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy from 31.1% to 39.6%, providing a tighter lower bound on what LMs know. We have released the code and the resulting LM Prompt And Query Archive (LPAQA) at https://github.com/jzbjyb/LPAQA . Keyword: Computational linguistics. Natural language processing; P98-98.5 URL: https://doaj.org/article/861ecb5d6ec2467287cf263aa94e6a75 https://doi.org/10.1162/tacl_a_00324 BASE Hide details 67 Improving Candidate Generation for Low-resource Cross-lingual Entity Linking Zhou, Shuyan; Rijhwani, Shruti; Wieting, John... In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 109-124 (2020) (2020) BASE Show details 68 A Bilingual Generative Transformer for Semantic Sentence Embedding ... Wieting, John; Neubig, Graham; Berg-Kirkpatrick, Taylor. - : arXiv, 2019 BASE Show details 69 Generalized Data Augmentation for Low-Resource Translation ... Xia, Mengzhou; Kong, Xiang; Anastasopoulos, Antonios. - : arXiv, 2019 BASE Show details 70 Improving Robustness of Machine Translation with Synthetic Noise ... Vaibhav, Vaibhav; Singh, Sumeet; Stewart, Craig. - : arXiv, 2019 BASE Show details 71 Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework ... Wang, Zirui; Xie, Jiateng; Xu, Ruochen. - : arXiv, 2019 BASE Show details 72 Towards Zero-resource Cross-lingual Entity Linking ... Zhou, Shuyan; Rijhwani, Shruti; Neubig, Graham. - : arXiv, 2019 BASE Show details 73 Target Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation ... Wang, Xinyi; Neubig, Graham. - : arXiv, 2019 BASE Show details 74 Pushing the Limits of Low-Resource Morphological Inflection ... Anastasopoulos, Antonios; Neubig, Graham. - : arXiv, 2019 BASE Show details 75 Self-Attentional Models for Lattice Inputs ... Sperber, Matthias; Neubig, Graham; Pham, Ngoc-Quan. - : arXiv, 2019 BASE Show details 76 Multilingual Neural Machine Translation With Soft Decoupled Encoding ... Wang, Xinyi; Pham, Hieu; Arthur, Philip. - : arXiv, 2019 BASE Show details 77 Beyond BLEU: Training Neural Machine Translation with Semantic Similarity ... Wieting, John; Berg-Kirkpatrick, Taylor; Gimpel, Kevin. - : arXiv, 2019 BASE Show details 78 Domain Adaptation of Neural Machine Translation by Lexicon Induction ... Hu, Junjie; Xia, Mengzhou; Neubig, Graham. - : arXiv, 2019 BASE Show details 79 Should All Cross-Lingual Embeddings Speak English? ... Anastasopoulos, Antonios; Neubig, Graham. - : arXiv, 2019 BASE Show details 80 DIRE: A Neural Approach to Decompiled Identifier Naming ... Lacomis, Jeremy; Yin, Pengcheng; Schwartz, Edward J.. - : arXiv, 2019 BASE Show details Page: 1 2 3 4 5 6

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