Fast Interleaved Bidirectional Sequence Generation

Independence assumptions during sequence generation can speed up inference, but parallel generation of highly inter-dependent tokens comes at a cost in quality. Instead of assuming independence between neighbouring tokens (semi-autoregressive decoding, SA), we take inspiration from bidirectional sequence generation and introduce a decoder that generates target words from the left-to-right and right-to-left directions simultaneously. We show that we can easily convert a standard architecture for unidirectional decoding into a bidirectional decoder by simply interleaving the two directions and adapting the word positions and selfattention masks. Our interleaved bidirectional decoder (IBDecoder) retains the model simplicity and training efficiency of the standard Transformer, and on five machine translation tasks and two document summarization tasks, achieves a decoding speedup of ~2x compared to autoregressive decoding with comparable quality. Notably, it outperforms left-to-right SA because the independence assumptions in IBDecoder are more felicitous. To achieve even higher speedups, we explore hybrid models where we either simultaneously predict multiple neighbouring tokens per direction, or perform multi-directional decoding by partitioning the target sequence. These methods achieve speedups to 4x–11x across different tasks at the cost of <1 BLEU or <0.5 ROUGE (on average)

Exact and Approximate Methods for Machine Translation Decoding

Statistical methods have been the major force driving the advance of machine translation in recent years. Complex models are designed to improve translation performance, but the added complexity also makes decoding more challenging. In this thesis, we focus on designing exact and approximate algorithms for machine translation decoding. More specifically, we will discuss the decoding problems for phrase-based translation models and bidirectional word alignment. The techniques explored in this thesis are Lagrangian relaxation and local search. Lagrangian relaxation based algorithms give us exact methods that have formal guarantees while being efficient in practice. We study extensions to Lagrangian relaxation that improve the convergence rate on machine translation decoding problems. The extensions include a tightening technique that adds constraints incrementally, optimality-preserving pruning to manage the search space size and utilizing the bounding properties of Lagrangian relaxation to develop an exact beam search algorithm. In addition to having the potential to improve translation accuracy, exact decoding deepens our understanding of the model that we are using, since it separates model errors from optimization errors. This leads to the question of designing models that improve the translation quality. We design a syntactic phrase-based model that incorporates a dependency language model to evaluate the fluency level of the target language. By employing local search, an approximate method, to decode this richer model, we discuss the trade-off between the complexity of a model and the decoding efficiency with the model

Probing Product Description Generation via Posterior Distillation

In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks. High-quality customer reviews can be considered as an ideal source to mine user-cared aspects. However, in reality, a large number of new products (known as long-tailed commodities) cannot gather sufficient amount of customer reviews, which brings a big challenge in the product description generation task. Existing works tend to generate the product description solely based on item information, i.e., product attributes or title words, which leads to tedious contents and cannot attract customers effectively. To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews. Specifically, we first extend the self-attentive Transformer encoder to encode product titles and attributes. Then, we apply an adaptive posterior distillation module to utilize useful review information, which integrates user-cared aspects to the generation process. Finally, we apply a Transformer-based decoding phase with copy mechanism to automatically generate the product description. Besides, we also collect a large-scare Chinese product description dataset to support our work and further research in this field. Experimental results show that our model is superior to traditional generative models in both automatic indicators and human evaluation

Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization

Scene representation networks (SRNs) have been recently proposed for compression and visualization of scientific data. However, state-of-the-art SRNs do not adapt the allocation of available network parameters to the complex features found in scientific data, leading to a loss in reconstruction quality. We address this shortcoming with an adaptively placed multi-grid SRN (APMGSRN) and propose a domain decomposition training and inference technique for accelerated parallel training on multi-GPU systems. We also release an open-source neural volume rendering application that allows plug-and-play rendering with any PyTorch-based SRN. Our proposed APMGSRN architecture uses multiple spatially adaptive feature grids that learn where to be placed within the domain to dynamically allocate more neural network resources where error is high in the volume, improving state-of-the-art reconstruction accuracy of SRNs for scientific data without requiring expensive octree refining, pruning, and traversal like previous adaptive models. In our domain decomposition approach for representing large-scale data, we train an set of APMGSRNs in parallel on separate bricks of the volume to reduce training time while avoiding overhead necessary for an out-of-core solution for volumes too large to fit in GPU memory. After training, the lightweight SRNs are used for realtime neural volume rendering in our open-source renderer, where arbitrary view angles and transfer functions can be explored. A copy of this paper, all code, all models used in our experiments, and all supplemental materials and videos are available at https://github.com/skywolf829/APMGSRN.Comment: Accepted to IEEE VIS 202

The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction

With the advent of deep learning, research in many areas of machine learning is converging towards the same set of methods and models. For example, long short-term memory networks are not only popular for various tasks in natural language processing (NLP) such as speech recognition, machine translation, handwriting recognition, syntactic parsing, etc., but they are also applicable to seemingly unrelated fields such as robot control, time series prediction, and bioinformatics. Recent advances in contextual word embeddings like BERT boast with achieving state-of-the-art results on 11 NLP tasks with the same model. Before deep learning, a speech recognizer and a syntactic parser used to have little in common as systems were much more tailored towards the task at hand. At the core of this development is the tendency to view each task as yet another data mapping problem, neglecting the particular characteristics and (soft) requirements tasks often have in practice. This often goes along with a sharp break of deep learning methods with previous research in the specific area. This work can be understood as an antithesis to this paradigm. We show how traditional symbolic statistical machine translation models can still improve neural machine translation (NMT) while reducing the risk for common pathologies of NMT such as hallucinations and neologisms. Other external symbolic models such as spell checkers and morphology databases help neural grammatical error correction. We also focus on language models that often do not play a role in vanilla end-to-end approaches and apply them in different ways to word reordering, grammatical error correction, low-resource NMT, and document-level NMT. Finally, we demonstrate the benefit of hierarchical models in sequence-to-sequence prediction. Hand-engineered covering grammars are effective in preventing catastrophic errors in neural text normalization systems. Our operation sequence model for interpretable NMT represents translation as a series of actions that modify the translation state, and can also be seen as derivation in a formal grammar.EPSRC grant EP/L027623/1 EPSRC Tier-2 capital grant EP/P020259/

Transformer-based NMT : modeling, training and implementation

International trade and industrial collaborations enable countries and regions to concentrate their developments on specific industries while making the most of other countries' specializations, which significantly accelerates global development. However, globalization also increases the demand for cross-region communication. Language barriers between many languages worldwide create a challenge for achieving deep collaboration between groups speaking different languages, increasing the need for translation. Language technology, specifically, Machine Translation (MT) holds the promise to enable communication between languages efficiently in real-time with minimal costs. Even though nowadays computers can perform computation in parallel very fast, which provides machine translation users with translations with very low latency, and although the evolution from Statistical Machine Translation (SMT) to Neural Machine Translation (NMT) with the utilization of advanced deep learning algorithms has significantly boosted translation quality, current machine translation algorithms are still far from accurately translating all input. Thus, how to further improve the performance of state-of-the-art NMT algorithm remains a valuable open research question which has received a wide range of attention. In the research presented in this thesis, we first investigate the long-distance relation modeling ability of the state-of-the-art NMT model, the Transformer. We propose to learn source phrase representations and incorporate them into the Transformer translation model, aiming to enhance its ability to capture long-distance dependencies well. Second, though previous work (Bapna et al., 2018) suggests that deep Transformers have difficulty in converging, we empirically find that the convergence of deep Transformers depends on the interaction between the layer normalization and residual connections employed to stabilize its training. We conduct a theoretical study about how to ensure the convergence of Transformers, especially for deep Transformers, and propose to ensure the convergence of deep Transformers by putting the Lipschitz constraint on its parameter initialization. Finally, we investigate how to dynamically determine proper and efficient batch sizes during the training of the Transformer model. We find that the gradient direction gets stabilized with increasing batch size during gradient accumulation. Thus we propose to dynamically adjust batch sizes during training by monitoring the gradient direction change within gradient accumulation, and to achieve a proper and efficient batch size by stopping the gradient accumulation when the gradient direction starts to fluctuate. For our research in this thesis, we also implement our own NMT toolkit, the Neutron implementation of the Transformer and its variants. In addition to providing fundamental features as the basis of our implementations for the approaches presented in this thesis, we support many advanced features from recent cutting-edge research work. Implementations of all our approaches in this thesis are also included and open-sourced in the toolkit. To compare with previous approaches, we mainly conducted our experiments on the data from the WMT 14 English to German (En-De) and English to French (En-Fr) news translation tasks, except when studying the convergence of deep Transformers, where we alternated the WMT 14 En-Fr task with the WMT 15 Czech to English (Cs-En) news translation task to compare with Bapna et al. (2018). The sizes of these datasets vary from medium (the WMT 14 En-De, ~ 4.5M sentence pairs) to very large (the WMT 14 En-Fr, ~ 36M sentence pairs), thus we suggest our approaches help improve the translation quality between popular language pairs which are widely used and have sufficient data.China Scholarship Counci