Publications

Generating GitHub Repository Descriptions: A Comparison of Manual and Automated Approaches

Jazlyn Hellman

Eunbee Jang

Christoph Treude

Chenzhun Huang

Jin Guo

Given the vast number of repositories hosted on GitHub, project discovery and retrieval have become increasingly important for GitHub users.… (see more) Repository descriptions serve as one of the first points of contact for users who are accessing a repository. However, repository owners often fail to provide a high-quality description; instead, they use vague terms, the purpose of the repository is poorly explained, or the description is omitted entirely. In this work, we examine the current practice of writing GitHub repository descriptions. Our investigation leads to the proposal of the LSP (Language, Software technology, and Purpose) template to formulate good descriptions for GitHub repositories that are clear, concise, and informative. To understand the extent to which current automated techniques can support generating repository descriptions, we compare the performance of state-of-the-art text summarization methods on this task. Finally, our user study with GitHub users reveals that automated summarization can adequately be used for default description generation for GitHub repositories, while the descriptions which follow the LSP template offer the most effective instrument for communicating with GitHub users.

2021-10-25

ArXiv (preprint)

Stringency of containment and closures on the growth of SARS-CoV-2 in Canada prior to accelerated vaccine roll-out

D. Vickers

S. Baral

Sharmistha Mishra

J. Kwong

M. Sundaram

Alan W. Katz

Andrew J. Calzavara

Mathieu Maheu-Giroux

David Buckeridge

Tyler Williamson

2021-10-21

International Journal of Infectious Diseases (published)

Real-M: Towards Speech Separation on Real Mixtures

Cem (Yusuf) Subakan

Mirco Ravanelli

Samuele Cornell

François Grondin

In recent years, deep learning based source separation has achieved impressive results. Most studies, however, still evaluate separation mod… (see more)els on synthetic datasets, while the performance of state-of-the-art techniques on in-the-wild speech data remains an open question. This paper contributes to fill this gap in two ways. First, we release the REAL-M dataset, a crowd-sourced corpus of real-life mixtures. Secondly, we address the problem of performance evaluation of real-life mixtures, where the ground truth is not available. We bypass this issue by carefully designing a blind Scale-Invariant Signal-to-Noise Ratio (SI-SNR) neural estimator. Through a user study, we show that our estimator reliably evaluates the separation performance on real mixtures, i.e. we observe that the performance predictions of the SI-SNR estimator correlate well with human opinions. Moreover, when evaluating popular speech separation models, we observe that the performance trends predicted by our estimator on the REAL-M dataset closely follow the performance trends achieved on synthetic benchmarks.

2021-10-20

ArXiv (preprint)

An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models

Nicholas Meade

Elinor Poole-Dayan

Recent work has shown pre-trained language models capture social biases from the large amounts of text they are trained on. This has attract… (see more)ed attention to developing techniques that mitigate such biases. In this work, we perform an empirical survey of five recently proposed bias mitigation techniques: Counterfactual Data Augmentation (CDA), Dropout, Iterative Nullspace Projection, Self-Debias, and SentenceDebias. We quantify the effectiveness of each technique using three intrinsic bias benchmarks while also measuring the impact of these techniques on a model’s language modeling ability, as well as its performance on downstream NLU tasks. We experimentally find that: (1) Self-Debias is the strongest debiasing technique, obtaining improved scores on all bias benchmarks; (2) Current debiasing techniques perform less consistently when mitigating non-gender biases; And (3) improvements on bias benchmarks such as StereoSet and CrowS-Pairs by using debiasing strategies are often accompanied by a decrease in language modeling ability, making it difficult to determine whether the bias mitigation was effective.

2021-10-16

ArXiv (preprint)

Lifelong Topological Visual Navigation

Rey Reza Wiyatno

Anqi Xu

Liam Paull

Commonly, learning-based topological navigation approaches produce a local policy while preserving some loose connectivity of the space thro… (see more)ugh a topological map. Nevertheless, spurious or missing edges in the topological graph often lead to navigation failure. In this work, we propose a sampling-based graph building method, which results in sparser graphs yet with higher navigation performance compared to baseline methods. We also propose graph maintenance strategies that eliminate spurious edges and expand the graph as needed, which improves lifelong navigation performance. Unlike controllers that learn from fixed training environments, we show that our model can be fine-tuned using only a small number of collected trajectory images from a real-world environment where the agent is deployed. We demonstrate successful navigation after fine-tuning on real-world environments, and notably show significant navigation improvements over time by applying our lifelong graph maintenance strategies.

2021-10-16

ArXiv (preprint)

The Power of Prompt Tuning for Low-Resource Semantic Parsing

Nathan Schucher

Harm de Vries

Prompt tuning has recently emerged as an effective method for adapting pre-trained language models to a number of language understanding and… (see more) generation tasks. In this paper, we investigate prompt tuning for semantic parsing—the task of mapping natural language utterances onto formal meaning representations. On the low-resource splits of Overnight and TOPv2, we find that a prompt tuned T5-xl significantly outperforms its fine-tuned counterpart, as well as strong GPT-3 and BART baselines. We also conduct ablation studies across different model scales and target representations, finding that, with increasing model scale, prompt tuned T5 models improve at generating target representations that are far from the pre-training distribution.

2021-10-16

ArXiv (preprint)

Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining

Andreas Madsen

Nicholas Meade

Vaibhav Adlakha

To explain NLP models a popular approach is to use importance measures, such as attention, which inform input tokens are important for makin… (see more)g a prediction. However, an open question is how well these explanations accurately reflect a model's logic, a property called faithfulness. To answer this question, we propose Recursive ROAR, a new faithfulness metric. This works by recursively masking allegedly important tokens and then retraining the model. The principle is that this should result in worse model performance compared to masking random tokens. The result is a performance curve given a masking-ratio. Furthermore, we propose a summarizing metric using relative area-between-curves (RACU), which allows for easy comparison across papers, models, and tasks. We evaluate 4 different importance measures on 8 different datasets, using both LSTM-attention models and RoBERTa models. We find that the faithfulness of importance measures is both model-dependent and task-dependent. This conclusion contradicts previous evaluations in both computer vision and faithfulness of attention literature.

2021-10-15

ArXiv (preprint)

Evaluation of real-life use of Point-Of-Care Rapid Antigen TEsting for SARS-CoV-2 in schools for outbreak control (EPOCRATES)

A. Blanchard

Marc Desforges

A. Labbé

C. Nguyen

Y. Petit

Derek Besner

Kate A. Zinszer

Olivier Séguin

Zineb Laghdir

K. Adams

Marie-ève Benoit

Ghislain Leduc

Jean Longtin

Ioannis. Ragoussis

David Buckeridge

Caroline Quach

We evaluated the use of rapid antigen detection tests (RADT) for the diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-… (see more)2) infection in school settings to determine RADT performance characteristics compared to PCR. Methods: We did a real-world, prospective observational cohort study where recruited high-school students and staff from two high-schools in Montreal (Canada) were followed from January 25th to June 10th, 2021. Twenty-five percent of asymptomatic participants were tested weekly by RADT (nasal) and PCR (gargle). Class contacts of a case were tested. Symptomatic participants were tested by RADT (nasal) and PCR (nasal and gargle). The number of cases/outbreak and number of outbreaks were compared to other high schools in the same area. Results: Overall, 2,099 students and 286 school staff members consented to participate. The overall RADT specificity varied from 99.8 to 100%, with a lower sensitivity, varying from 28.6% in asymptomatic to 83.3% in symptomatic participants. The number of outbreaks was not different in the 2 participating schools compared to other high schools in the same area, but included a greater proportion of asymptomatic cases. Returning students to school after a 7-day quarantine, with a negative PCR on D6-7 after exposure, did not lead to subsequent outbreaks, as shown by serial testing. Of cases for whom the source was known, 37 of 57 (72.5%) were secondary to household transmission, 13 (25%) to intra-school transmission and one to community contacts between students in the same school. Conclusion: RADT did not perform well as a screening tool in asymptomatic individuals. Reinforcing policies for symptom screening when entering schools and testing symptomatic individuals with RADT on the spot may avoid subsequent significant exposures in class.

2021-10-14

medRxiv (preprint)

Compositional Generalization in Dependency Parsing

Emily D. Goodwin

Timothy O'Donnell

Dzmitry Bahdanau

Compositionality— the ability to combine familiar units like words into novel phrases and sentences— has been the focus of intense inter… (see more)est in artificial intelligence in recent years. To test compositional generalization in semantic parsing, Keysers et al. (2020) introduced Compositional Freebase Queries (CFQ). This dataset maximizes the similarity between the test and train distributions over primitive units, like words, while maximizing the compound divergence: the dissimilarity between test and train distributions over larger structures, like phrases. Dependency parsing, however, lacks a compositional generalization benchmark. In this work, we introduce a gold-standard set of dependency parses for CFQ, and use this to analyze the behaviour of a state-of-the art dependency parser (Qi et al., 2020) on the CFQ dataset. We find that increasing compound divergence degrades dependency parsing performance, although not as dramatically as semantic parsing performance. Additionally, we find the performance of the dependency parser does not uniformly degrade relative to compound divergence, and the parser performs differently on different splits with the same compound divergence. We explore a number of hypotheses for what causes the non-uniform degradation in dependency parsing performance, and identify a number of syntactic structures that drive the dependency parser’s lower performance on the most challenging splits.

2021-10-13

ArXiv (preprint)

Sarath Chandar Anbil Parthipan

Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers

Gabriele Prato

Simon Guiroy

Ethan Caballero

Irina Rish

Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly… (see more) in the light of recent breakthroughs achieved by large-scale pre-trained models such as GPT-3, CLIP and DALL-e. Accurately predicting the neural network performance with increasing resources such as data, compute and model size provides a more comprehensive evaluation of different approaches across multiple scales, as opposed to traditional point-wise comparisons of fixed-size models on fixed-size benchmarks, and, most importantly, allows for focus on the best-scaling, and thus most promising in the future, approaches. In this work, we consider a challenging problem of few-shot learning in image classification, especially when the target data distribution in the few-shot phase is different from the source, training, data distribution, in a sense that it includes new image classes not encountered during training. Our current main goal is to investigate how the amount of pre-training data affects the few-shot generalization performance of standard image classifiers. Our key observations are that (1) such performance improvements are well-approximated by power laws (linear log-log plots) as the training set size increases, (2) this applies to both cases of target data coming from either the same or from a different domain (i.e., new classes) as the training data, and (3) few-shot performance on new classes converges at a faster rate than the standard classification performance on previously seen classes. Our findings shed new light on the relationship between scale and generalization.

2021-10-13

ArXiv (preprint)

openreview.net

Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations

Pau Rodriguez

Massimo Caccia

Alexandre Lacoste

Lee Zamparo

Issam Hadj Laradji

Laurent Charlin

David Vazquez

Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying… (see more) more reliable machine-learning systems. In computer vision applications, generative counterfactual methods indicate how to perturb a model’s input to change its prediction, providing details about the model’s decision-making. Current methods tend to generate trivial counterfactuals about a model’s decisions, as they often suggest to exaggerate or remove the presence of the attribute being classified. For the machine learning practitioner, these types of counterfactuals offer little value, since they provide no new information about undesired model or data biases. In this work, we identify the problem of trivial counterfactual generation and we propose DiVE to alleviate it. DiVE learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss to uncover multiple valuable explanations about the model’s prediction. Further, we introduce a mechanism to prevent the model from producing trivial explanations. Experiments on CelebA and Synbols demonstrate that our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods. Code is available at https://github.com/ElementAI/beyond-trivial-explanations.

2021-10-10

2021 IEEE/CVF International Conference on Computer Vision (ICCV) (published)