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Learning to rank: from pairwise approach to listwise approach
It is proposed that learning to rank should adopt the listwise approach in which lists of objects are used as 'instances' in learning, and introduces two probability models, respectively referred to as permutation probability and top k probability, to define a listwise loss function for learning.
MASS: Masked Sequence to Sequence Pre-training for Language Generation
This work proposes MAsked Sequence to Sequence pre-training (MASS) for the encoder-decoder based language generation tasks, which achieves the state-of-the-art accuracy on the unsupervised English-French translation, even beating the early attention-based supervised model.
FastSpeech: Fast, Robust and Controllable Text to Speech
A novel feed-forward network based on Transformer to generate mel-spectrogram in parallel for TTS is proposed, which speeds up mel-Spectrogram generation by 270x and the end-to-end speech synthesis by 38x and is called FastSpeech.
LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval
This paper has constructed a benchmark dataset referred to as LETOR, derived the LETOR data from the existing data sets widely used in IR, namely, OHSUMED and TREC data and provided the results of several state-ofthe-arts learning to rank algorithms on the data.
LETOR: A benchmark collection for research on learning to rank for information retrieval
The details of the LETOR collection are described and it is shown how it can be used in different kinds of researches, and several state-of-the-art learning to rank algorithms on LETOR are compared.
Neural Architecture Optimization
Experiments show that the architecture discovered by this simple and efficient method to automatic neural architecture design based on continuous optimization is very competitive for image classification task on CIFAR-10 and language modeling task on PTB, outperforming or on par with the best results of previous architecture search methods with a significantly reduction of computational resources.
Introducing LETOR 4.0 Datasets
LETOR is a package of benchmark data sets for research on LEarning TO Rank, which contains standard features, relevance judgments, data partitioning, evaluation tools, and several baselines. Version…
Dual Learning for Machine Translation
Experiments show that dual-NMT works very well on English ↔ French translation; especially, by learning from monolingual data, it achieves a comparable accuracy to NMT trained from the full bilingual data for the French-to-English translation task.
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
FastSpeech 2 is proposed, which addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS by directly training the model with ground-truth target instead of the simplified output from teacher, and introducing more variation information of speech as conditional inputs.
Incorporating BERT into Neural Machine Translation
A new algorithm named BERT-fused model is proposed, in which BERT is first used to extract representations for an input sequence, and then the representations are fused with each layer of the encoder and decoder of the NMT model through attention mechanisms.