A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining

Classifying and resolving coreferences of objects (e.g., product names) and attributes (e.g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance. However, the task is challenging as one often needs to consider domain-specific knowledge (e.g., iPad is a tablet and has aspect resolution) to identify coreferences in opinionated reviews. Also, compiling a handcrafted and curated domain-specific knowledge base for each domain is very time consuming and arduous. This paper proposes an approach to automatically mine and leverage domain-specific knowledge for classifying objects and attribute coreferences. The approach extracts domain-specific knowledge from unlabeled review data and trains a knowledgeaware neural coreference classification model to leverage (useful) domain knowledge together with general commonsense knowledge for the task. Experimental evaluation on realworld datasets involving five domains (product types) shows the effectiveness of the approach.Comment: Accepted to Proceedings of EMNLP 2020 (Findings

Dynabench: Rethinking Benchmarking in NLP

We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop dataset creation: annotators seek to create examples that a target model will misclassify, but that another person will not. In this paper, we argue that Dynabench addresses a critical need in our community: contemporary models quickly achieve outstanding performance on benchmark tasks but nonetheless fail on simple challenge examples and falter in real-world scenarios. With Dynabench, dataset creation, model development, and model assessment can directly inform each other, leading to more robust and informative benchmarks. We report on four initial NLP tasks, illustrating these concepts and highlighting the promise of the platform, and address potential objections to dynamic benchmarking as a new standard for the field

Representation Learning for Texts and Graphs: A Unified Perspective on Efficiency, Multimodality, and Adaptability

[...] This thesis is situated between natural language processing and graph representation learning and investigates selected connections. First, we introduce matrix embeddings as an efficient text representation sensitive to word order. [...] Experiments with ten linguistic probing tasks, 11 supervised, and five unsupervised downstream tasks reveal that vector and matrix embeddings have complementary strengths and that a jointly trained hybrid model outperforms both. Second, a popular pretrained language model, BERT, is distilled into matrix embeddings. [...] The results on the GLUE benchmark show that these models are competitive with other recent contextualized language models while being more efficient in time and space. Third, we compare three model types for text classification: bag-of-words, sequence-, and graph-based models. Experiments on five datasets show that, surprisingly, a wide multilayer perceptron on top of a bag-of-words representation is competitive with recent graph-based approaches, questioning the necessity of graphs synthesized from the text. [...] Fourth, we investigate the connection between text and graph data in document-based recommender systems for citations and subject labels. Experiments on six datasets show that the title as side information improves the performance of autoencoder models. [...] We find that the meaning of item co-occurrence is crucial for the choice of input modalities and an appropriate model. Fifth, we introduce a generic framework for lifelong learning on evolving graphs in which new nodes, edges, and classes appear over time. [...] The results show that by reusing previous parameters in incremental training, it is possible to employ smaller history sizes with only a slight decrease in accuracy compared to training with complete history. Moreover, weighting the binary cross-entropy loss function is crucial to mitigate the problem of class imbalance when detecting newly emerging classes. [...

Towards Real-World Data Streams for Deep Continual Learning

Continual Learning deals with Artificial Intelligent agents striving to learn from an ever-ending stream of data. Recently, Deep Continual Learning focused on the design of new strategies to endow Artificial Neural Networks with the ability to learn continuously without forgetting previous knowledge. In fact, the learning process of any Artificial Neural Network model is well-known to lack the sufficient stability to preserve existing knowledge when learning new information. This phenomenon, called catastrophic forgetting or simply forgetting, is considered one of the main obstacles for the design of effective Continual Learning agents. However, existing strategies designed to mitigate forgetting have been evaluated on a restricted set of Continual Learning scenarios. The most used one is, by far, the Class-Incremental scenario applied on object detection tasks. Even though it drove interest in Continual Learning, Class-Incremental scenarios strongly constraint the properties of the data stream, thus limiting its ability to model real-world environments. The core of this thesis concerns the introduction of three Continual Learning data streams, whose design is centered around specific real-world environments properties. First, we propose the Class- Incremental with Repetition scenario, which builds a data stream including both the introduction of new concepts and the repetition of previous ones. Repetition is naturally present in many environments and it constitutes an important source of information. Second, we formalize the Continual Pre-Training scenario, which leverages a data stream of unstructured knowledge to keep a pre-trained model updated over time. One important objective of this scenario is to study how to continuously build general, robust representations that does not strongly depend on the specific task to be solved. This is a fundamental property of real-world agents, which build cross-task knowledge and then adapts it to specific needs. Third, we study Continual Learning scenarios where data streams are composed by temporally-correlated data. Temporal correlation is ubiquitous and lies at the foundation of most environments we, as humans, experience during our life. We leverage Recurrent Neural Networks as our main model, due to their intrinsic ability to model temporal correlations. We discovered that, when applied to recurrent models, Continual Learning strategies behave in an unexpected manner. This highlights the limits of the current experimental validation, mostly focused on Computer Vision tasks. Ultimately, the introduction of new data streams contributed to deepen our understanding of how Artificial Neural Networks learn continuously. We discover that forgetting strongly depends on the properties of the data stream and we observed large changes from one data stream to another. Moreover, when forgetting is mild, we were able to effectively mitigate it with simple strategies, or even without any specific ones. Loosening the focus on forgetting allows us to turn our attention to other interesting problems, outlined in this thesis, like (i) separation between continual representation learning and quick adaptation to novel tasks, (ii) robustness to unbalanced data streams and (iii) ability to continuously learn temporal correlations. These objectives currently defy existing strategies and will likely represent the next challenge for Continual Learning research

Interactional Slingshots: Providing Support Structure to User Interactions in Hybrid Intelligence Systems

The proliferation of artificial intelligence (AI) systems has enabled us to engage more deeply and powerfully with our digital and physical environments, from chatbots to autonomous vehicles to robotic assistive technology. Unfortunately, these state-of-the-art systems often fail in contexts that require human understanding, are never-before-seen, or complex. In such cases, though the AI-only approaches cannot solve the full task, their ability to solve a piece of the task can be combined with human effort to become more robust to handling complexity and uncertainty. A hybrid intelligence system—one that combines human and machine skill sets—can make intelligent systems more operable in real-world settings. In this dissertation, we propose the idea of using interactional slingshots as a means of providing support structure to user interactions in hybrid intelligence systems. Much like how gravitational slingshots provide boosts to spacecraft en route to their final destinations, so do interactional slingshots provide boosts to user interactions en route to solving tasks. Several challenges arise: What does this support structure look like? How much freedom does the user have in their interactions? How is user expertise paired with that of the machine’s? To do this as a tractable socio-technical problem, we explore this idea in the context of data annotation problems, especially in those domains where AI methods fail to solve the overall task. Getting annotated (labeled) data is crucial for successful AI methods, and becomes especially more difficult in domains where AI fails, since problems in such domains require human understanding to fully solve, but also present challenges related to annotator expertise, annotation freedom, and context curation from the data. To explore data annotation problems in this space, we develop techniques and workflows whose interactional slingshot support structure harnesses the user’s interaction with data. First, we explore providing support in the form of nudging non-expert users’ interactions as they annotate text data for the task of creating conversational memory. Second, we add support structure in the form of assisting non-expert users during the annotation process itself for the task of grounding natural language references to objects in 3D point clouds. Finally, we supply support in the form of guiding expert and non-expert users both before and during their annotations for the task of conversational disentanglement across multiple domains. We demonstrate that building hybrid intelligence systems with each of these interactional slingshot support mechanisms—nudging, assisting, and guiding a user’s interaction with data—improves annotation outcomes, such as annotation speed, accuracy, effort level, even when annotators’ expertise and skill levels vary. Thesis Statement: By providing support structure that nudges, assists, and guides user interactions, it is possible to create hybrid intelligence systems that enable more efficient (faster and/or more accurate) data annotation.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163138/1/sairohit_1.pd

Canada: Multiculturalism, Religion, and Accommodation

In this thesis I use Critical Discourse Analysis to examine discursive constructions of identity (individual, religious, and national) within the framework of Canadian multiculturalism as they are constructed in two Canadian newspapers (the Toronto Star and the Gazette) between 2003 and 2013. I am particularly interested in how understandings of multiculturalism delimit the boundaries of belonging for religious practitioners in Canada. In chapter one I establish the academic context of this thesis and give a brief outline of the history of Canadian multiculturalism. In chapter two I focus on definitions and assessments of Canadian multiculturalism and the integration of immigrants who belong to religious minority groups. In chapter three I examine identity and belonging through the lens of ‘Canadian values’, including tolerance and secularism. In chapter four I examine the construction of the religious other as presented in discourse strands about religious accommodation with a particular focus on Muslim veiling

The State of AI Ethics Report (June 2020)

These past few months have been especially challenging, and the deployment of technology in ways hitherto untested at an unrivalled pace has left the internet and technology watchers aghast. Artificial intelligence has become the byword for technological progress and is being used in everything from helping us combat the COVID-19 pandemic to nudging our attention in different directions as we all spend increasingly larger amounts of time online. It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with each other. With this inaugural edition of the State of AI Ethics we hope to bring forward the most important developments that caught our attention at the Montreal AI Ethics Institute this past quarter. Our goal is to help you navigate this ever-evolving field swiftly and allow you and your organization to make informed decisions. This pulse-check for the state of discourse, research, and development is geared towards researchers and practitioners alike who are making decisions on behalf of their organizations in considering the societal impacts of AI-enabled solutions. We cover a wide set of areas in this report spanning Agency and Responsibility, Security and Risk, Disinformation, Jobs and Labor, the Future of AI Ethics, and more. Our staff has worked tirelessly over the past quarter surfacing signal from the noise so that you are equipped with the right tools and knowledge to confidently tread this complex yet consequential domain