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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
TLDR
Qualitatively, the proposed RNN Encoder‐Decoder model learns a semantically and syntactically meaningful representation of linguistic phrases. Expand
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
TLDR
It is shown how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks. Expand
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. Expand
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
TLDR
An architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts using a single BiLSTM encoder with a shared byte-pair encoding vocabulary for all languages, coupled with an auxiliary decoder and trained on publicly available parallel corpora. Expand
Very Deep Convolutional Networks for Text Classification
TLDR
This work presents a new architecture (VDCNN) for text processing which operates directly at the character level and uses only small convolutions and pooling operations, and is able to show that the performance of this model increases with the depth. Expand
Continuous space language models
TLDR
Highly efficient learning algorithms are described that enable the use of training corpora of several hundred million words and it is shown that this approach can be incorporated into a large vocabulary continuous speech recognizer using a lattice rescoring framework at a very low additional processing time. Expand
MLQA: Evaluating Cross-lingual Extractive Question Answering
TLDR
This work presents MLQA, a multi-way aligned extractive QA evaluation benchmark intended to spur research in this area, and evaluates state-of-the-art cross-lingual models and machine-translation-based baselines onMLQA. Expand
On Using Monolingual Corpora in Neural Machine Translation
TLDR
This work investigates how to leverage abundant monolingual corpora for neural machine translation to improve results for En-Fr and En-De translation and extends to high resource languages such as Cs-En and De-En. Expand
Neural Probabilistic Language Models
TLDR
This work proposes to fight the curse of dimensionality by learning a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences, and incorporates this new language model into a state-of-the-art speech recognizer of conversational speech. Expand
Very Deep Convolutional Networks for Natural Language Processing
TLDR
This work presents a new architecture for text processing which operates directly on the character level and uses only small convolutions and pooling operations, and is able to show that the performance of this model increases with the depth. Expand
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