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Hits 61 – 80 of 116
61
Universal Phone Recognition with a Multilingual Allophone System ...
Li, Xinjian
;
Dalmia, Siddharth
;
Li, Juncheng
. - : arXiv, 2020
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62
The Return of Lexical Dependencies: Neural Lexicalized PCFGs ...
Zhu, Hao
;
Bisk, Yonatan
;
Neubig, Graham
. - : arXiv, 2020
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63
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization ...
Hu, Junjie
;
Ruder, Sebastian
;
Siddhant, Aditya
. - : arXiv, 2020
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64
X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models ...
Jiang, Zhengbao
;
Anastasopoulos, Antonios
;
Araki, Jun
. - : arXiv, 2020
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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)
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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
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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)
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68
A Bilingual Generative Transformer for Semantic Sentence Embedding ...
Wieting, John
;
Neubig, Graham
;
Berg-Kirkpatrick, Taylor
. - : arXiv, 2019
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69
Generalized Data Augmentation for Low-Resource Translation ...
Xia, Mengzhou
;
Kong, Xiang
;
Anastasopoulos, Antonios
. - : arXiv, 2019
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70
Improving Robustness of Machine Translation with Synthetic Noise ...
Vaibhav, Vaibhav
;
Singh, Sumeet
;
Stewart, Craig
. - : arXiv, 2019
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71
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework ...
Wang, Zirui
;
Xie, Jiateng
;
Xu, Ruochen
. - : arXiv, 2019
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72
Towards Zero-resource Cross-lingual Entity Linking ...
Zhou, Shuyan
;
Rijhwani, Shruti
;
Neubig, Graham
. - : arXiv, 2019
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73
Target Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation ...
Wang, Xinyi
;
Neubig, Graham
. - : arXiv, 2019
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74
Pushing the Limits of Low-Resource Morphological Inflection ...
Anastasopoulos, Antonios
;
Neubig, Graham
. - : arXiv, 2019
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75
Self-Attentional Models for Lattice Inputs ...
Sperber, Matthias
;
Neubig, Graham
;
Pham, Ngoc-Quan
. - : arXiv, 2019
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76
Multilingual Neural Machine Translation With Soft Decoupled Encoding ...
Wang, Xinyi
;
Pham, Hieu
;
Arthur, Philip
. - : arXiv, 2019
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77
Beyond BLEU: Training Neural Machine Translation with Semantic Similarity ...
Wieting, John
;
Berg-Kirkpatrick, Taylor
;
Gimpel, Kevin
. - : arXiv, 2019
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78
Domain Adaptation of Neural Machine Translation by Lexicon Induction ...
Hu, Junjie
;
Xia, Mengzhou
;
Neubig, Graham
. - : arXiv, 2019
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79
Should All Cross-Lingual Embeddings Speak English? ...
Anastasopoulos, Antonios
;
Neubig, Graham
. - : arXiv, 2019
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80
DIRE: A Neural Approach to Decompiled Identifier Naming ...
Lacomis, Jeremy
;
Yin, Pengcheng
;
Schwartz, Edward J.
. - : arXiv, 2019
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