In this paper, we propose a novel neural network model called RNN Encoder‐ Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder‐Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.

/pdf/learning-phrase-representations-using-rnn-encoder-decoder-74734oka8k.pdf

Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation

In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features.

/pdf/automatic-differentiation-in-pytorch-gbuafkf7qo.pdf

Automatic differentiation in PyTorch

Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. 
In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. 
We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks.

/pdf/pytorch-an-imperative-style-high-performance-deep-learning-59vlz5u10y.pdf

We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training, tuning and applying the system to many translation tasks.

/pdf/moses-open-source-toolkit-for-statistical-machine-3pmfuwk8uc.pdf

Moses: Open Source Toolkit for Statistical Machine Translation

For many low-resource languages, spoken language resources are more likely to be annotated with translations than with transcriptions. Translated speech data is potentially valuable for documenting endangered languages or for training speech translation systems. A first step towards making use of such data would be to automatically align spoken words with their translations. We present a model that combines Dyer et al.'s reparameterization of IBM Model 2 (fast-align) and k-means clustering using Dynamic Time Warping as a distance metric. The two components are trained jointly using expectation-maximization. In an extremely low-resource scenario, our model performs significantly better than both a neural model and a strong baseline.

An Unsupervised Probability Model for Speech-to-Translation Alignment of Low-Resource Languages

In adding syntax to statistical machine translation, there is a tradeoff between taking advantage of linguistic analysis and allowing the model to exploit parallel training data with no linguistic analysis: translation quality versus coverage. A number of previous efforts have tackled this tradeoff by starting with a commitment to linguistically motivated analyses and then finding appropriate ways to soften that commitment. We present an approach that explores the tradeoff from the other direction, starting with a translation model learned directly from aligned parallel text, and then adding soft constituent-level constraints based on parses of the source language. We argue that in order for these constraints to improve translation, they must be fine-grained: the constraints should vary by constituent type, and by the type of match or mismatch with the parse. We also use a different feature weight optimization technique, capable of handling large amount of features, thus eliminating the bottleneck of feature selection. We obtain substantial improvements in performance for translation from Arabic to English.

Soft syntactic constraints for Arabic---English hierarchical phrase-based translation

We present Model Invertibility Regularization (MIR), a method that jointly trains two directional sequence alignment models, one in each direction, and takes into account the invertibility of the alignment task. By coupling the two models through their parameters (as opposed to through their inferences, as in Liang et al.’s Alignment by Agreement (ABA), and Ganchev et al.’s Posterior Regularization (PostCAT)), our method seamlessly extends to all IBMstyle word alignment models as well as to alignment without parallel data. Our proposed algorithm is mathematically sound and inherits convergence guarantees from EM. We evaluate MIR on two tasks: (1) On word alignment, applying MIR on fertility based models we attain higher F-scores than ABA and PostCAT. (2) On Japanese-to-English backtransliteration without parallel data, applied to the decipherment model of Ravi and Knight, MIR learns sparser models that close the gap in whole-name error rate by 33% relative to a model trained on parallel data, and further, beats a previous approach by Mylonakis et al.

/pdf/model-invertibility-regularization-sequence-alignment-with-p63vz538v5.pdf

Model Invertibility Regularization: Sequence Alignment With or Without Parallel Data

https://www.ling.upenn.edu/courses/cogs502/Dill2007.pdf

David Chiang

Papers

An Unsupervised Probability Model for Speech-to-Translation Alignment of Low-Resource Languages

Soft syntactic constraints for Arabic---English hierarchical phrase-based translation

Model Invertibility Regularization: Sequence Alignment With or Without Parallel Data

Computational linguistics: A new tool for exploring biopolymer structures and statistical mechanics

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing