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Publications
A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge
We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice … (voir plus)of Plausible Alternatives (COPA). Problem instances from these tasks require diverse, complex forms of inference and knowledge to solve. Our method uses a knowledge-hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine. It extracts and classifies knowledge from the returned results and weighs it to make a resolution. Our approach improves F1 performance on the WSC by 0.16 over the previous best and is competitive with the state-of-the-art on COPA, demonstrating its general applicability.
2018-06-01
North American Chapter of the Association for Computational Linguistics (publié)
We present an approach to event coreference resolution by developing a general framework for clustering that uses supervised representation … (voir plus)learning. We propose a neural network architecture with novel Clustering-Oriented Regularization (CORE) terms in the objective function. These terms encourage the model to create embeddings of event mentions that are amenable to clustering. We then use agglomerative clustering on these embeddings to build event coreference chains. For both within- and cross-document coreference on the ECB+ corpus, our model obtains better results than models that require significantly more pre-annotated information. This work provides insight and motivating results for a new general approach to solving coreference and clustering problems with representation learning.
2018-06-01
Proceedings of the Seventh Joint Conference on Lexical and
Computational Semantics (publié)
Software and systems traceability is widely accepted as an essential element for supporting many software development tasks. Today's version… (voir plus) control systems provide inbuilt features that allow developers to tag each commit with one or more issue ID, thereby providing the building blocks from which project-wide traceability can be established between feature requests, bug fixes, commits, source code, and specific developers. However, our analysis of six open source projects showed that on average only 60% of the commits were linked to specific issues. Without these fundamental links the entire set of project-wide links will be incomplete, and therefore not trustworthy. In this paper we address the fundamental problem of missing links between commits and issues. Our approach leverages a combination of process and text-related features characterizing issues and code changes to train a classifier to identify missing issue tags in commit messages, thereby generating the missing links. We conducted a series of experiments to evaluate our approach against six open source projects and showed that it was able to effectively recommend links for tagging issues at an average of 96% recall and 33% precision. In a related task for augmenting a set of existing trace links, the classifier returned precision at levels greater than 89% in all projects and recall of 50%.
2018-05-27
Proceedings of the 40th International Conference on Software Engineering (publié)
This paper presents a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size. MINE… (voir plus) is back-propable and we prove that it is strongly consistent. We illustrate a handful of applications in which MINE is succesfully applied to enhance the property of generative models in both unsupervised and supervised settings. We apply our framework to estimate the information bottleneck, and apply it in tasks related to supervised classification problems. Our results demonstrate substantial added flexibility and improvement in these settings.
We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence ne… (voir plus)ural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning - answering image-related questions which require a multi-step, high-level process - a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.
2018-04-29
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
We introduce an incremental processing scheme for convolutional neural network (CNN) inference, targeted at embedded applications with limit… (voir plus)ed memory budgets. Instead of processing layers one by one, individual input pixels are propagated through all parts of the network they can influence under the given structural constraints. This depth-first updating scheme comes with hard bounds on the memory footprint: the memory required is constant in the case of 1D input and proportional to the square root of the input dimension in the case of 2D input.
Usability and user experience (UX) issues are often not well emphasized and addressed in open source software (OSS) development. There is an… (voir plus) imperative need for supporting OSS communities to collaboratively identify, understand, and fix UX design issues in a distributed environment. In this paper, we provide an initial step towards this effort and report on an exploratory study that investigated how the OSS communities currently reported, discussed, negotiated, and eventually addressed usability and UX issues. We conducted in-depth qualitative analysis of selected issue tracking threads from three OSS projects hosted on GitHub. Our findings indicated that discussions about usability and UX issues in OSS communities were largely influenced by the personal opinions and experiences of the participants. Moreover, the characteristics of the community may have greatly affected the focus of such discussion.
2018-04-20
Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (publié)
A recurrent neural network is a powerful tool for modeling sequential data such as text and speech. While recurrent neural networks have ach… (voir plus)ieved record-breaking results in speech recognition, one remaining challenge is their slow processing speed. The main cause comes from the nature of recurrent neural networks that read only one frame at each time step. Therefore, reducing the number of reads is an effective approach to reducing processing time. In this paper, we propose a novel recurrent neural network architecture called Skip-RNN, which dynamically skips speech frames that are less important. The Skip-RNN consists of an acoustic model network and skip-policy network that are jointly trained to classify speech frames and determine how many frames to skip. We evaluate our proposed approach on the Wall Street Journal corpus and show that it can accelerate acoustic model computation by up to 2.4 times without any noticeable degradation in transcription accuracy.
2018-04-15
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)
Singing voice separation based on deep learning relies on the usage of time-frequency masking. In many cases the masking process is not a le… (voir plus)arnable function or is not encapsulated into the deep learning optimization. Consequently, most of the existing methods rely on a post processing step using the generalized Wiener filtering. This work proposes a method that learns and optimizes (during training) a source-dependent mask and does not need the aforementioned post processing step. We introduce a recurrent inference algorithm, a sparse transformation step to improve the mask generation process, and a learned denoising filter. Obtained results show an increase of 0.49 dB for the signal to distortion ratio and 0.30 dB for the signal to interference ratio, compared to previous state-of-the-art approaches for monaural singing voice separation.
2018-04-15
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)
Spoken language understanding system is traditionally designed as a pipeline of a number of components. First, the audio signal is processed… (voir plus) by an automatic speech recognizer for transcription or n-best hypotheses. With the recognition results, a natural language understanding system classifies the text to structured data as domain, intent and slots for down-streaming consumers, such as dialog system, hands-free applications. These components are usually developed and optimized independently. In this paper, we present our study on an end-to-end learning system for spoken language understanding. With this unified approach, we can infer the semantic meaning directly from audio features without the intermediate text representation. This study showed that the trained model can achieve reasonable good result and demonstrated that the model can capture the semantic attention directly from the audio features.
2018-04-15
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)
Recent advance of deep learning has been transforming the landscape in many domains. However, understanding the predictions of a deep networ… (voir plus)k remains a challenge, which is especially sensitive in health care domains as interpretability is key. Techniques that rely on saliency maps -highlighting the region of an image that influence the classifier’s decision the mostare often used for that purpose. However, gradients fluctuation make saliency maps noisy and thus difficult to interpret at a human level. Moreover, models tend to focus on one particular influential region of interest (ROI) in the image, even though other regions might be relevant for the decision. We propose a new framework that refines those saliency maps to generate segmentation masks over the ROI on the initial image. In a second contribution, we propose to apply those masks over the original inputs, then evaluate our classifier on the masked inputs to identify previously overlooked ROI. This iterative procedure allows us to emphasize new region of interests by extracting meaningful information from the saliency maps.
