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Distributed semantics for automatically classifying discourse relations
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Speaker: Jacob Eisenstein, assistant professor at Georgia Institute of Technology, Atlanta, GA, U.S.A.
Abstract: Discourse structure provides a link between individual sentences, which can be analyzed syntactically, and document-level labels, which are typically computed from bag-of-words statistics. Successfully modeling discourse therefore has the potential to improve a range of natural language processing applications, from sentiment analysis to machine translation. One challenge to achieving this vision of discourse-driven NLP is that many discourse relations are fundamentally semantic in nature. While theoretical work has elucidated connections between formal semantics and discourse, at present this is of little practical use in automatic discourse parsing, due to the intractability of open-domain formal semantic analysis. Distributed compositional semantics offers an appealing alternative: the meaning of discourse arguments is captured in dense numerical vectors, which are constructed incrementally from smaller linguistic units; the compositional operations themselves can be learned so as to optimize performance on discourse parsing. But the key question is whether these vector-based representations are sufficiently expressive to capture the semantics behind discourse relations. This talk describes three projects in which distributed semantic representations yield significant improvements in discourse relation detection: rhetorical structure theory parsing, supervised PDTB relation classification, and a generative discourse relational language model.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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