Skip to search formSkip to main contentSkip to account menu
Neural Architectures for Named Entity Recognition
Comunicacio presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 de juny 2016.
View on ACL
Cross-lingual Language Model Pretraining
TLDR
This work proposes two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingsual language model objective.
View PDF on arXiv
Word Translation Without Parallel Data
TLDR
It is shown that a bilingual dictionary can be built between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way.
View PDF on arXiv
XNLI: Evaluating Cross-lingual Sentence Representations
TLDR
This work constructs an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus to 14 languages, including low-resource languages such as Swahili and Urdu and finds that XNLI represents a practical and challenging evaluation suite and that directly translating the test data yields the best performance among available baselines.
View on ACL
Unsupervised Machine Translation Using Monolingual Corpora Only
TLDR
This work proposes a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space and effectively learns to translate without using any labeled data.
View PDF on arXiv
Phrase-Based & Neural Unsupervised Machine Translation
TLDR
This work investigates how to learn to translate when having access to only large monolingual corpora in each language, and proposes two model variants, a neural and a phrase-based model, which are significantly better than methods from the literature, while being simpler and having fewer hyper-parameters.
View on ACL
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
TLDR
10 probing tasks designed to capture simple linguistic features of sentences are introduced and used to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of bothencoders and training methods.
View on ACL
Fader Networks: Manipulating Images by Sliding Attributes
TLDR
A new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space is introduced, which results in much simpler training schemes and nicely scales to multiple attributes.
View PDF on arXiv
The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English
TLDR
This work introduces the FLORES evaluation datasets for Nepali–English and Sinhala– English, based on sentences translated from Wikipedia, and demonstrates that current state-of-the-art methods perform rather poorly on this benchmark, posing a challenge to the research community working on low-resource MT.
View on ACL
Multiple-Attribute Text Rewriting
TLDR
This paper proposes a new model that controls several factors of variation in textual data where this condition on disentanglement is replaced with a simpler mechanism based on back-translation, and demonstrates that the fully entangled model produces better generations.
View Paper
...
...
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy, Terms of Service, and Dataset License