Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding

Context plays an important role in human language understanding, thus it may also be useful for machines learning vector representations of language. In this paper, we explore an asymmetric encoder-decoder structure for unsupervised context-based sentence representation learning. We carefully designed experiments to show that neither an autoregressive decoder nor an RNN decoder is required. After that, we designed a model which still keeps an RNN as the encoder, while using a non-autoregressive convolutional decoder. We further combine a suite of effective designs to significantly improve model efficiency while also achieving better performance. Our model is trained on two different large unlabelled corpora, and in both cases the transferability is evaluated on a set of downstream NLP tasks. We empirically show that our model is simple and fast while producing rich sentence representations that excel in downstream tasks

Non-autoregressive Transformer-based End-to-end ASR using BERT

Transformer-based models have led to a significant innovation in various classic and practical subjects, including speech processing, natural language processing, and computer vision. On top of the transformer, the attention-based end-to-end automatic speech recognition (ASR) models have become a popular fashion in recent years. Specifically, the non-autoregressive modeling, which can achieve fast inference speed and comparable performance when compared to conventional autoregressive methods, is an emergent research topic. In the context of natural language processing, the bidirectional encoder representations from transformers (BERT) model has received widespread attention, partially due to its ability to infer contextualized word representations and to obtain superior performances of downstream tasks by performing only simple fine-tuning. In order to not only inherit the advantages of non-autoregressive ASR modeling, but also receive benefits from a pre-trained language model (e.g., BERT), a non-autoregressive transformer-based end-to-end ASR model based on BERT is presented in this paper. A series of experiments conducted on the AISHELL-1 dataset demonstrates competitive or superior results of the proposed model when compared to state-of-the-art ASR systems

Efficient Deep Speech Understanding at the Edge

In contemporary speech understanding (SU), a sophisticated pipeline is employed, encompassing the ingestion of streaming voice input. The pipeline executes beam search iteratively, invoking a deep neural network to generate tentative outputs (referred to as hypotheses) in an autoregressive manner. Periodically, the pipeline assesses attention and Connectionist Temporal Classification (CTC) scores. This paper aims to enhance SU performance on edge devices with limited resources. Adopting a hybrid strategy, our approach focuses on accelerating on-device execution and offloading inputs surpassing the device's capacity. While this approach is established, we tackle SU's distinctive challenges through innovative techniques: (1) Late Contextualization: This involves the parallel execution of a model's attentive encoder during input ingestion. (2) Pilot Inference: Addressing temporal load imbalances in the SU pipeline, this technique aims to mitigate them effectively. (3) Autoregression Offramps: Decisions regarding offloading are made solely based on hypotheses, presenting a novel approach. These techniques are designed to seamlessly integrate with existing speech models, pipelines, and frameworks, offering flexibility for independent or combined application. Collectively, they form a hybrid solution for edge SU. Our prototype, named XYZ, has undergone testing on Arm platforms featuring 6 to 8 cores, demonstrating state-of-the-art accuracy. Notably, it achieves a 2x reduction in end-to-end latency and a corresponding 2x decrease in offloading requirements

Machine Learning Models for Efficient and Robust Natural Language Processing

Natural language processing (NLP) has come of age. For example, semantic role labeling (SRL), which automatically annotates sentences with a labeled graph representing who did what to whom, has in the past ten years seen nearly 40% reduction in error, bringing it to useful accuracy. As a result, a myriad of practitioners now want to deploy NLP systems on billions of documents across many domains. However, state-of-the-art NLP systems are typically not optimized for cross-domain robustness nor computational efficiency. In this dissertation I develop machine learning methods to facilitate fast and robust inference across many common NLP tasks. First, I describe paired learning and inference algorithms for dynamic feature selection which accelerate inference in linear classifiers, the heart of the fastest NLP models, by 5-10 times. I then present iterated dilated convolutional neural networks (ID-CNNs), a distinct combination of network structure, parameter sharing and training procedures that increase inference speed by 14-20 times with accuracy matching bidirectional LSTMs, the most accurate models for NLP sequence labeling. Finally, I describe linguistically-informed self-attention (LISA), a neural network model that combines multi-head self-attention with multi-task learning to facilitate improved generalization to new domains. We show that incorporating linguistic structure in this way leads to substantial improvements over the previous state-of-the-art (syntax-free) neural network models for SRL, especially when evaluating out-of-domain. I conclude with a brief discussion of potential future directions stemming from my thesis work