Gappy Pattern Matching on GPUs for On-Demand Extraction of Hierarchical Translation Grammars

Grammars for machine translation can be materialized on demand by finding source phrases in an indexed parallel corpus and extracting their translations. This approach is limited in practical applications by the computational expense of online lookup and extraction. For phrase-based models, recent work has shown that on-demand grammar extraction can be greatly accelerated by parallelization on general purpose graphics processing units (GPUs), but these algorithms do not work for hierarchical models, which require matching patterns that contain gaps. We address this limitation by presenting a novel GPU algorithm for on-demand hierarchical grammar extraction that is at least an order of magnitude faster than a comparable CPU algorithm when processing large batches of sentences. In terms of end-to-end translation, with decoding on the CPU, we increase throughput by roughly two thirds on a standard MT evaluation dataset. The GPU necessary to achieve these improvements increases the cost of a server by about a third. We believe that GPU-based extraction of hierarchical grammars is an attractive proposition, particularly for MT applications that demand high throughput.

ARCHITECTURE, MODELS, AND ALGORITHMS FOR TEXTUAL SIMILARITY

Identifying similar pieces of texts remains one of the fundamental problems in computational linguistics. This dissertation focuses on the textual similarity measurement and identification problem by studying a variety of major tasks that share common properties, and presents our efforts to address 7 closely-related similarity tasks given over 20 public benchmarks, including paraphrase identification, answer selection for question answering, pairwise learning to rank, monolingual/cross-lingual semantic textual similarity measurement, insight extraction on biomedical literature, and high performance cross-lingual pattern matching for machine translation on GPUs. We investigate how to make textual similarity measurement more accurate with deep neural networks. Traditional approaches are either based on feature engineering which leads to disconnected solutions, or the Siamese architecture which treats inputs independently, utilizes single representation view and straightforward similarity comparison. In contrast, we focus on modeling stronger interactions between inputs and develop interaction-based neural modeling that explicitly encodes the alignments of input words or aggregated sentence representations into our models. As a result, our multiple deep neural networks show highly competitive performance on many textual similarity measurement public benchmarks we evaluated. Our multi-perspective convolutional neural networks (MPCNN) uses a multiplicity of perspectives to process input sentences with multiple parallel convolutional neural networks, is able to extract salient sentence-level features automatically at multiple granularities with different types of pooling. Our novel structured similarity layer encourages stronger input interactions by comparing local regions of both sentence representations. This model is the first example of our interaction-based neural modeling. We also provide an attention-based input interaction layer on top of the MPCNN model. The input interaction layer models a closer relationship of input words by converting two separate sentences into an inter-related sentence pair. This layer utilizes the attention mechanism in a straightforward way, and is another example of our interaction-based neural modeling. We then provide our pairwise word interaction model with very deep neural networks (PWI). This model directly encodes input word interactions with novel pairwise word interaction modeling and a novel similarity focus layer. The use of very deep architecture in this model is the first example in NLP domain for better textual similarity modeling. Our PWI model outperforms the Siamese architecture and feature engineering approach on multiple tasks, and is another example of our interaction-based neural modeling. We also focus on the question answering task with a pairwise ranking approach. Unlike traditional pointwise approach of the task, our pairwise ranking approach with the use of negative sampling focuses on modeling interactions between two pairs of question and answer inputs, then learns a relative order of the pairs to predict which answer is more relevant to the question. We demonstrate its high effectiveness against competitive previous pointwise baselines. For the insight extraction on biomedical literature task, we develop neural networks with similarity modeling for better causality/correlation relation extraction, as we convert the extraction task into a similarity measurement task. Our approach innovates in that it explicitly models the interactions among the trio: named entities, entity relations and contexts, and then measures both relational and contextual similarity among them, finally integrate both similarity evaluations into considerations for insight extraction. We also build an end-to-end system to extract insights, with human evaluations we show our system is able to extract insights with high human acceptance accuracy. Lastly, we explore how to exploit massive parallelism offered by modern GPUs for high-efficiency pattern matching. We take advantage of GPU hardware advances and develop a massive parallelism approach. We firstly work on phrase-based SMT, where we enable phrase lookup and extraction on suffix arrays to be massively parallelized and vastly many queries to be carried out in parallel. We then work on computationally expensive hierarchical SMT model, which requires matching grammar patterns that contain ''gaps''. In order to get high efficiency for the similarity identification task on GPUs, we show developing massively parallel algorithms on GPUs is the most important approach to fully utilize GPU's raw processing power, and developing compact data structures on GPUs is helpful to lower GPU's memory latency. Compared to a highly-optimized, state-of-the-art multi-threaded CPU implementation, our techniques achieve orders of magnitude improvement in terms of throughput

Massively Parallel Suffix Array Queries and On-Demand Phrase Extraction for Statistical Machine Translation Using GPUs

Translation models in statistical machine translation can be scaled to large corpora and arbitrarily-long phrases by looking up translations of source phrases “on the fly” in an indexed parallel corpus using suffix arrays. However, this can be slow because on-demand extraction of phrase tables is computationally expensive. We address this problem by developing novel algorithms for general purpose graphics processing units (GPUs), which enable suffix array queries for phrase lookup and phrase extraction to be massively parallelized. Compared to a highly-optimized, state-of-the-art serial CPU-based implementation, our techniques achieve at least an order of magnitude improvement in terms of throughput. This work demonstrates the promise of massively parallel architectures and the potential of GPUs for tackling computationallydemanding problems in statistical machine translation and language processing.

Fast machine translation on parallel and massively parallel hardware

Parallel systems have been widely adopted in the field of machine translation, because the raw computational power they offer is well suited to this computationally intensive task. However programming for parallel hardware is not trivial as it requires redesign of the existing algorithms. In my thesis I design efficient algorithms for machine translation on parallel hardware. I identify memory accesses as the biggest bottleneck to processing speed and propose novel algorithms that minimize them. I present three distinct case studies in which minimizing memory access substantially improves speed: Starting with statistical machine translation, I design a phrase table that makes decoding ten times faster on a multi-threaded CPU. Next, I design a GPU-based n-gram language model that is twice as fast per £ as a highly optimized CPU implementation. Turning to neural machine translation, I design new stochastic gradient descent techniques that make end-to-end training twice as fast. The work in this thesis has been incorporated in two popular machine translation toolkits: Moses and Marian

Quantifying Cross-lingual Semantic Similarity for Natural Language Processing Applications

Translation and cross-lingual access to information are key technologies in a global economy. Even though the quality of machine translation (MT) output is still far from the level of human translations, many real-world applications have emerged, for which MT can be employed. Machine translation supports human translators in computer-assisted translation (CAT), providing the opportunity to improve translation systems based on human interaction and feedback. Besides, many tasks that involve natural language processing operate in a cross-lingual setting, where there is no need for perfectly fluent translations and the transfer of meaning can be modeled by employing MT technology. This thesis describes cumulative work in the field of cross-lingual natural language processing in a user-oriented setting. A common denominator of the presented approaches is their anchoring in an alignment between texts in two different languages to quantify the similarity of their content