On the impact of tokenizer and parameters on N-gram based Code Analysis

Recent research shows that language models, such as n-gram models, are useful at a wide variety of software engineering tasks, e.g., code completion, bug identification, code summarisation, etc. However, such models require the appropriate set of numerous parameters. Moreover, the different ways one can read code essentially yield different models (based on the different sequences of tokens). In this paper, we focus on n- gram models and evaluate how the use of tokenizers, smoothing, unknown threshold and n values impact the predicting ability of these models. Thus, we compare the use of multiple tokenizers and sets of different parameters (smoothing, unknown threshold and n values) with the aim of identifying the most appropriate combinations. Our results show that the Modified Kneser-Ney smoothing technique performs best, while n values are depended on the choice of the tokenizer, with values 4 or 5 offering a good trade-off between entropy and computation time. Interestingly, we find that tokenizers treating the code as simple text are the most robust ones. Finally, we demonstrate that the differences between the tokenizers are of practical importance and have the potential of changing the conclusions of a given experiment

PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems

Machine Learning models are often composed of pipelines of transformations. While this design allows to efficiently execute single model components at training time, prediction serving has different requirements such as low latency, high throughput and graceful performance degradation under heavy load. Current prediction serving systems consider models as black boxes, whereby prediction-time-specific optimizations are ignored in favor of ease of deployment. In this paper, we present PRETZEL, a prediction serving system introducing a novel white box architecture enabling both end-to-end and multi-model optimizations. Using production-like model pipelines, our experiments show that PRETZEL is able to introduce performance improvements over different dimensions; compared to state-of-the-art approaches PRETZEL is on average able to reduce 99th percentile latency by 5.5x while reducing memory footprint by 25x, and increasing throughput by 4.7x.Comment: 16 pages, 14 figures, 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 201

2kenize: Tying Subword Sequences for Chinese Script Conversion

Simplified Chinese to Traditional Chinese character conversion is a common preprocessing step in Chinese NLP. Despite this, current approaches have poor performance because they do not take into account that a simplified Chinese character can correspond to multiple traditional characters. Here, we propose a model that can disambiguate between mappings and convert between the two scripts. The model is based on subword segmentation, two language models, as well as a method for mapping between subword sequences. We further construct benchmark datasets for topic classification and script conversion. Our proposed method outperforms previous Chinese Character conversion approaches by 6 points in accuracy. These results are further confirmed in a downstream application, where 2kenize is used to convert pretraining dataset for topic classification. An error analysis reveals that our method's particular strengths are in dealing with code-mixing and named entities.Comment: Accepted to ACL 202

Static malware detection Using Stacked BiLSTM and GPT-2

In recent years, cyber threats and malicious software attacks have been escalated on various platforms. Therefore, it has become essential to develop automated machine learning methods for defending against malware. In the present study, we propose stacked bidirectional long short-term memory (Stacked BiLSTM) and generative pre-trained transformer based (GPT-2) deep learning language models for detecting malicious code. We developed language models using assembly instructions extracted from .text sections of malicious and benign Portable Executable (PE) files. We treated each instruction as a sentence and each .text section as a document. We also labeled each sentence and document as benign or malicious, according to the file source. We created three datasets from those sentences and documents. The first dataset, composed of documents, was fed into a Document Level Analysis Model (DLAM) based on Stacked BiLSTM. The second dataset, composed of sentences, was used in Sentence Level Analysis Models (SLAMs) based on Stacked BiLSTM and DistilBERT, Domain Specific Language Model GPT-2 (DSLM-GPT2), and General Language Model GPT-2 (GLM-GPT2). Lastly, we merged all assembly instructions without labels for creating the third dataset; then we fed a custom pre-trained model with it. We then compared malware detection performances. The results showed that the pre-trained model improved the DSLM-GPT2 and GLM-GPT2 detection performance. The experiments showed that the DLAM, the SLAM based on DistilBERT, the DSLM-GPT2, and the GLM-GPT2 achieved 98.3%, 70.4%, 86.0%, and 76.2% F1 scores, respectively

CORLEONE - Core Linguistic Entity Online Extraction

This report presents CORLEONE (Core Linguistic Entity Online Extraction) - a pool of loosely coupled general-purpose basic lightweight linguistic processing resources, which can be independently used to identify core linguistic entities and their features in free texts. Currently, CORLEONE consists of five processing resources: (a) a basic tokenizer, (b) a tokenizer which performs fine-grained token classification, (c) a component for performing morphological analysis, and (d) a memory-efficient database-like dictionary look-up component, and (e) sentence splitter. Linguistic resources for several languages are provided. Additionally, CORLEONE includes a comprehensive library of string distance metrics relevant for the task of name variant matching. CORLEONE has been developed in the Java programming language and heavily deploys state-of-the-art finite-state techniques. Noteworthy, CORLEONE components are used as basic linguistic processing resources in ExPRESS, a pattern matching engine based on regular expressions over feature structures and in the real-time news event extraction system, which were developed by the Web Mining and Intelligence Group of the Support to External Security Unit of IPSC. This report constitutes an end-user guide for COLREONE and provides scientifically interesting details of how it was implemented.JRC.G.2-Support to external securit

Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation

For a complicated algorithm, its implementation by a human programmer usually starts with outlining a rough control flow followed by iterative enrichments, eventually yielding carefully generated syntactic structures and variables in a hierarchy. However, state-of-the-art large language models generate codes in a single pass, without intermediate warm-ups to reflect the structured thought process of "outline-then-detail". Inspired by the recent success of chain-of-thought prompting, we propose ChainCoder, a program synthesis language model that generates Python code progressively, i.e. from coarse to fine in multiple passes. We first decompose source code into layout frame components and accessory components via abstract syntax tree parsing to construct a hierarchical representation. We then reform our prediction target into a multi-pass objective, each pass generates a subsequence, which is concatenated in the hierarchy. Finally, a tailored transformer architecture is leveraged to jointly encode the natural language descriptions and syntactically aligned I/O data samples. Extensive evaluations show that ChainCoder outperforms state-of-the-arts, demonstrating that our progressive generation eases the reasoning procedure and guides the language model to generate higher-quality solutions. Our codes are available at: https://github.com/VITA-Group/ChainCoder.Comment: Accepted in ICML 202

Sub-Character Tokenization for Chinese Pretrained Language Models

Tokenization is fundamental to pretrained language models (PLMs). Existing tokenization methods for Chinese PLMs typically treat each character as an indivisible token. However, they ignore the unique feature of the Chinese writing system where additional linguistic information exists below the character level, i.e., at the sub-character level. To utilize such information, we propose sub-character (SubChar for short) tokenization. Specifically, we first encode the input text by converting each Chinese character into a short sequence based on its glyph or pronunciation, and then construct the vocabulary based on the encoded text with sub-word tokenization. Experimental results show that SubChar tokenizers have two main advantages over existing tokenizers: 1) They can tokenize inputs into much shorter sequences, thus improving the computational efficiency. 2) Pronunciation-based SubChar tokenizers can encode Chinese homophones into the same transliteration sequences and produce the same tokenization output, hence being robust to all homophone typos. At the same time, models trained with SubChar tokenizers perform competitively on downstream tasks. We release our code at https://github.com/thunlp/SubCharTokenization to facilitate future work.Comment: This draft supersedes the previous version named "SHUOWEN-JIEZI: Linguistically Informed Tokenizers For Chinese Language Model Pretraining

Ensemble Learning on Deep Neural Networks for Image Caption Generation

abstract: Capturing the information in an image into a natural language sentence is considered a difficult problem to be solved by computers. Image captioning involves not just detecting objects from images but understanding the interactions between the objects to be translated into relevant captions. So, expertise in the fields of computer vision paired with natural language processing are supposed to be crucial for this purpose. The sequence to sequence modelling strategy of deep neural networks is the traditional approach to generate a sequential list of words which are combined to represent the image. But these models suffer from the problem of high variance by not being able to generalize well on the training data. The main focus of this thesis is to reduce the variance factor which will help in generating better captions. To achieve this, Ensemble Learning techniques have been explored, which have the reputation of solving the high variance problem that occurs in machine learning algorithms. Three different ensemble techniques namely, k-fold ensemble, bootstrap aggregation ensemble and boosting ensemble have been evaluated in this thesis. For each of these techniques, three output combination approaches have been analyzed. Extensive experiments have been conducted on the Flickr8k dataset which has a collection of 8000 images and 5 different captions for every image. The bleu score performance metric, which is considered to be the standard for evaluating natural language processing (NLP) problems, is used to evaluate the predictions. Based on this metric, the analysis shows that ensemble learning performs significantly better and generates more meaningful captions compared to any of the individual models used.Dissertation/ThesisMasters Thesis Software Engineering 201