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Found 9 papers, 4 papers with code
Log-Linear Reformulation of the Noisy Channel Model for Document-Level Neural Machine Translation
We seek to maximally use various data sources, such as parallel and monolingual data, to build an effective and efficient document-level translation system.
Fill in the Blanks: Imputing Missing Sentences for Larger-Context Neural Machine Translation
In particular, we consider three distinct approaches to generate the missing context: using random contexts, applying a copy heuristic or generating it with a language model.
Adaptive Scheduling for Multi-Task Learning
To train neural machine translation models simultaneously on multiple tasks (languages), it is common to sample each task uniformly or in proportion to dataset sizes.
Context-Aware Learning for Neural Machine Translation
By comparing performance using actual and random contexts, we show that a model trained with the proposed algorithm is more sensitive to the additional context.
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Adversarial Learning for Neural Dialogue Generation
In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances.
Ranked #1 on
Dialogue Generation
on Amazon-5
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
EmoNets: Multimodal deep learning approaches for emotion recognition in video
The task of the emotion recognition in the wild (EmotiW) Challenge is to assign one of seven emotions to short video clips extracted from Hollywood style movies.
On Using Very Large Target Vocabulary for Neural Machine Translation
The models trained by the proposed approach are empirically found to outperform the baseline models with a small vocabulary as well as the LSTM-based neural machine translation models.
