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    • November 16, 2018

      Shyam Upadhyay

      Lack of annotated data is a constant obstacle in developing machine learning models, especially for natural language processing (NLP) tasks. In this talk, I explore this problem in the realm of Multilingual NLP, where the challenges become more acute as most of the annotation efforts in the NLP community have been predominantly aimed at English.

      In particular, I will discuss two techniques for overcoming the lack of annotation in multilingual settings. I focus on two information extraction tasks --- cross-lingual entity linking and name transliteration to English --- for which traditional approaches rely on generous amounts of supervision in the language of interest. In the first part of the talk, I show how we can perform cross-lingual entity linking by sharing supervision across languages through a shared multilingual feature space. This approach enables us to complement the supervision in a low-resource language with supervision from a high resource language. In the second part, I show how we use freely available knowledge and unlabeled data to substitute for lack of supervision for the transliteration task. Key to the approach is a constrained bootstrapping algorithm that mines new example pairs for improving the transliteration model. Results on both tasks show the effectiveness of these approaches, and pave the way for future tasks involving the 3-way interaction of text, knowledge, and reasoning, in a multilingual setting.

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    • November 12, 2018

      Kevin Jamieson

      In many science and industry applications, data-driven discovery is limited by the rate of data collection like the time it takes skilled labor to operate a pipette or the cost of expensive reagents or use of experimental apparatuses. When measurement budgets are necessarily small, adaptive data collection that uses previously collected data to inform future data collection in a closed loop can make the difference between inferring a phenomenon or not. While methods like multi-armed bandits have provided great insights into optimal means of collecting data in the last several years, these algorithms require a number of measurements that scales linearly with the total number of possible actions or measurements that can be made, even if discovering just one among possibly many true positives is desired. For example, if many of our 20,000 genes are critical for cell-growth and a measurement corresponds to knocking out just one gene and measuring a noisy phenotype signal, one may expect that we can find a single influential gene with far fewer than 20,000 total measurements. In this talk I will ground this intuition in a theoretical framework and describe several applications where I have applied this perspective and new algorithms including crowd-sourcing preferences, multiple testing with false discovery control, hyperparameter tuning, and crowdfunding.

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    • October 26, 2018

      Sam Thomson

      Is there a class of models that perform competitively with LSTMs, yet are interpretable, parallelizable, data-efficient, and whose mathematical properties are already well-studied? I will present a recent line of work where we show that weighted finite-state automata (WFSAs) can be made unreasonably effective sequence encoders by letting their transition weights be calculated by neural nets.

      First, we introduce a specific architecture, Soft Patterns (SoPa), which generalizes convolutional neural networks (CNNs), capturing fixed-length but gappy patterns. We show that SoPa is competitive with LSTMs at text classification, and even outperforms LSTMs in small data regimes.

      Next, we explore the limits of this general approach. We show that several existing recurrent neural networks (RNNs) are in fact WFSAs in disguise, including quasi-recurrent neural networks, simple recurrent units, input switched affine networks, and more. These networks are already in popular use, showing strong performance on a variety of tasks. We formally define and characterize this class of RNNs, which include CNNs but not arbitrary RNNs, dubbing them "rational recurrences."

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    • October 22, 2018

      Chelsea Finn

      Machine learning excels primarily in settings where an engineer can first reduce the problem to a particular function, and collect a substantial amount of labeled input-output pairs for that function. In drastic contrast, humans are capable of learning a range of versatile behaviors from streams of raw sensory data with minimal external instruction. How can we develop machines that learn more like the latter? In this talk, I will discuss recent work on learning versatile behaviors from raw sensory observations with minimal human supervision. In particular, I will show how we can use meta-learning to infer goals and intentions from humans with only a few positive examples, how robots can leverage large amounts of unlabeled experience to develop and plan with visual predictive models of the world, and how we can combine elements of meta-learning and unsupervised learning to develop agents that propose their own goals and learn to achieve them.

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    • October 17, 2018

      Rishabh Iyer

      Visual Data in the form of Images and Videos have been growing at an unprecedented rate in the last few years. While this massive data is a blessing to data science by helping improve predictive accuracy, it is also a curse since humans are unable to consume this large amount of data. Moreover, today, machine generated videos (via Drones, Dash-cams, Body-cams, Security cameras etc.) are being generated at a rate higher than what we as humans can process, and majority of this data is plagued with redundancy. In this talk, I will present a unified framework for Submodular Optimization which provides an end to end solution to these problems. We first show that submodular functions naturally model notions of diversity, coverage, representation and information. Moreover they also lend themselves to practical and provably near optimal algorithms for optimization, thereby providing practical data summarization strategies. Along the way, we will highlight several implementational aspects of submodular optimization, including memoization tricks useful in building real world summarization systems.

      We also show how we can efficiently learn submodular functions for different domains and tasks. We will demonstrate the utility of this in summarization tasks related to visual data: Image collection summarization and domain specific video summarization. What comprises a good visual summary depends on the domain at hand -- creating a video summary of a soccer game will involve very different modeling characteristics compared to a surveillance video. We try to take a principled approach towards domain specific video summarization, we argue how we can efficiently learn the right weights for the different model families. We shall point out several interesting observations and insights learnt from this characterization. Towards the end of this talk, we shall extend this work to training data subset selection, where we shall show how we can use our summarization framework for reducing training complexity, quick turn-around times for hyper-parameter tuning and Diversified Active Learning.

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    • October 10, 2018

      Lucy Wang

      Human interpretability is essential in biomedicine, because information flow between computational platforms and human stakeholders is crucial to the proper management and care of disease. Biomedical data is abundant, but do not lend themselves to easy summary and interpretation. Luckily, there are many structured biomedical knowledge resources that can be used to assist in the analysis of all these data. How best to integrate ontological data with contemporary machine learning techniques is one of my main research interest, the other of which is to apply these integrated techniques to enhancing our understanding of specific human diseases.

      My research can by summarized into two themes: 1) the development of tools for modeling biomedical knowledge, and 2) the application of biomedical knowledge and natural language processing techniques to understanding biomedical and clinical texts. In this talk, I will describe a few of my projects and propose ways to extend some of these research ideas in the future.

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    • October 1, 2018

      Ana Marasovic

      Abstract Anaphora Resolution (AAR) is a challenging task of finding a (typically) non-nominal antecedent of pronouns and noun phrases that refer to abstract objects like facts, events, actions or situations, in the (typically) preceding discourse. An example is given below.

      Our intuition is that we can learn what is the correct antecedent for a given abstract anaphor by learning attributes of the relation that holds between the sentence with the abstract anaphor and its antecedent. We propose a siamese-LSTM mention-ranking model to learn what characterizes mentioned relations [1].

      Although the current resources for AAR are really scarce, we can train our models on many instances of antecedent-anaphoric sentence pairs. Such pairs can be automatically extracted from parsed corpora by searching for constructions with embedded sentences, applying a simple transformation that replaces the embedded sentence with an abstract anaphor and using the cut-off embedded sentence as the antecedent [1].

      I will show results of the mention-ranking model trained for shell noun resolution [2] and results on an abstract anaphora subset of the ARRAU corpus [3]. Finally, I will discuss ideas on how the training data extraction method and the mention-ranking model could be further improved for the challenges ahead. In particular, I will talk about:

      (i) quality of harvested training data to answer whether nominal and pronominal anaphors be learned independently, (ii) selecting antecedents from a wider preceding window, (iii) addressing differences between anaphora types with multi-task learning, (iv) addressing differenced in harvested and natural data with adversarial training, (v) utilizing pretrained language models.

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    • September 27, 2018

      Nicolas Fiorini

      PubMed is a free search engine for the biomedical literature accessed by millions of users from around the world each day. With the rapid growth of biomedical literature, finding and retrieving the most relevant papers for a given query is increasingly challenging. I will introduce Best Match, the new relevance search algorithm for PubMed that leverages click logs and learning-to-rank. The Best Match algorithm is trained with past user searches with dozens of relevance ranking signals (factors), the most important being the past usage of an article, publication date, BM25 score, and the type of article. This new algorithm demonstrated state-of-the-art retrieval performance in benchmarking experiments as well as an improved user experience in real-world testing.

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    • September 19, 2018

      Kevin Gimpel

      A key challenge in natural language understanding is recognizing when two sentences have the same meaning. I'll discuss our work on this problem over the past few years, including the exploration of compositional functional architectures, learning criteria, and naturally-occurring sources of training data. The result is a single sentence embedding model that outperforms all systems from the 2012-2016 SemEval semantic textual similarity competitions without training on any of the annotated data from those tasks.

      As a by-product, we developed a large dataset of automatically-generated paraphrase pairs by using parallel text and neural machine translation. We've since used the dataset, which we call ParaNMT-50M, to impart a notion of meaning equivalence to controlled text generation tasks, including syntactically-controlled paraphrasing and textual style transfer.

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    • August 29, 2018

      Robin Jia

      Reading comprehension systems that answer questions over a context passage can often achieve high test accuracy, but they are frustratingly brittle: they often rely heavily on superficial cues, and therefore struggle on out-of-domain inputs. In this talk, I will describe our work on understanding and challenging these systems. First, I will show how to craft adversarial reading comprehension examples by adding irrelevant distracting text to the context passage. Next, I will present the newest version of the SQuAD dataset, SQuAD 2.0, which tests whether models can distinguish answerable questions from similar but unanswerable ones. Finally, I will propose a new way of evaluating reading comprehension systems by measuring their zero-shot performance on other NLP tasks, such as relation extraction or semantic parsing, that have been converted to textual question answering problems.

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    • August 28, 2018

      Dan Weld

      Since AI software uses techniques like deep lookahead search and stochastic optimization of huge neural networks, it often results in complex behavior that is difficult for people to understand. Yet organizations are deploying AI algorithms in many mission-critical settings. To trust their behavior, we must make AI intelligible, either by using inherently interpretable models or by developing new methods for explaining and adjusting otherwise overwhelmingly complex decisions using local approximation, vocabulary alignment, and interactive explanation. This talk argues that intelligibility is essential, surveys recent work on building such systems, and highlights key directions for research.

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    • August 24, 2018

      Sebastian Ruder

      Deep neural networks excel at learning from labeled data. In contrast, learning from unlabeled data, especially under domain shift, which is common in many real-world applications, remains a challenge. In this talk, I will touch on three aspects of learning under domain shift: First I will discuss an approach to select relevant data for domain adaptation in order to minimize negative transfer. Secondly, I will show how classic bootstrapping algorithms can be applied to neural networks and that they make for strong baselines in this challenging setting. Finally, I will describe new methods to use language models for semi-supervised learning.

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    • August 21, 2018

      Chen Liang

      Learning to generate programs from natural language can support a wide range of applications including question answering, virtual assistant, AutoML, etc. It is natural to apply reinforcement learning to directly optimize the task reward, and generalization to new unseen inputs is crucial. However, three challenges need to be addressed: (1) how to model the structures in the programs; (2) how to efficiently learn from sparse rewards; (3) how to explore a large search space. In this talk, I will present (1) Neural Symbolic Machines (NSM), a hybrid framework that integrates a neural “programmer” with a symbolic "computer" to generate programs for multi-step reasoning; (2) Memory Augmented Policy optimization (MAPO), a novel policy optimization formulation that incorporates a memory buffer of promising trajectories to reduce the variance of policy gradient estimates, especially given sparse rewards. NSM with MAPO is the first end-to-end model trained with RL that achieves new state-of-the-art on weakly supervised semantic parsing, evaluated on 3 well-established benchmarks: WebQuestionsSP, WikiTableQuestions, and WikiSQL.

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    • August 6, 2018

      Pradeep Dasigi

      Natural Language Understanding systems typically involve encoding and reasoning components that are trained end-to-end to produce task-specific outputs given human utterances as inputs. I will talk about the role of external knowledge in making both these components better, and describe NLU systems that benefit from incorporating background and contextual knowledge. First, I will describe an approach for augmenting recurrent neural network models for encoding sentences, with background knowledge from knowledge bases like WordNet. I show that the resulting ontology-grounded context-sensitive representations of words lead to improvements in predicting prepositional phrase attachments and textual entailment.

      Second, I will focus on reasoning, and talk about complex question answering (QA) over structured contexts like tables and images. These QA tasks can be seen as semantic parsing problems, with supervision provided only in the form of answers, and not logical forms. I will discuss the challenges involved in the setup, and discuss three ways of exploiting contextual knowledge to deal with them: 1) use a grammar to constrain the output space of the decoder in a seq2seq model, 2) incorporate a minimal lexicon to bias the seq2seq model towards logical forms that are relevant to the utterances, and finally 3) exploit the compositionality of the logical form language to define a novel iterative training procedure for semantic parsers.

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    • June 26, 2018

      Chaitanya Malaviya

      Morphological analysis involves predicting the syntactic traits of a word (e.g. {POS: Noun, Case: Acc, Gender: Fem}). Previous work in morphological tagging improves performance for low-resource languages (LRLs) through cross-lingual training with a high-resource language (HRL) from the same family, but is limited by the strict---often false---assumption that tag sets exactly overlap between the HRL and LRL. In this paper we propose a method for cross-lingual morphological tagging that aims to improve information sharing between languages by relaxing this assumption. The proposed model uses factorial conditional random fields with neural network potentials, making it possible to (1) utilize the expressive power of neural network representations to smooth over superficial differences in the surface forms, (2) model pairwise and transitive relationships between tags, and (3) accurately generate tag sets that are unseen or rare in the training data. Experiments on four languages from the Universal Dependencies Treebank demonstrate superior tagging accuracies over existing cross-lingual approaches.

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    • June 13, 2018

      Hao Fang

      Engaging users in long, open-domain conversations with a chatbot remains a challenging research problem. Unlike task-oriented dialog systems which aim to accomplish small tasks quickly, users expect a broader variety of experiences from conversational chatbots (e.g., companionship, discussing recent news, or entertainment). The recent Alexa Prize has provided a new platform for researchers to build and test such open-domain dialog systems, i.e., socialbots, by allowing systems to interact with millions of real users through Alexa-enabled devices. The first part of this talk presents Sounding Board (winner of 2017 Alexa Prize) and discusses how Sounding Board uses massive and dynamically changing online contents to engage users in a coherent social conversation. While the Alexa platform provides an opportunity for getting real user feedback on a very large scale, some challenges remain. The second half of the talk focuses on addressing the challenge of scoring long socialbot conversations which cover several different topics. Using a large collection of Alexa Prize conversations, we study agent, content, and user factors that correlate with user ratings. We demonstrate approaches to estimate ratings at multiple levels of a long socialbot conversation.

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    • June 7, 2018

      Vered Shwartz

      Recognizing lexical inferences is one of the building blocks of natural language understanding. Lexical inference corresponds to a semantic relation that holds between two lexical items (words and multi-word expressions), when the meaning of one can be inferred from the other. In reading comprehension, for example, answering the question "which phones have long-lasting batteries?" given the text "Galaxy has a long-lasting battery", requires knowing that Galaxy is a model of a phone. In text summarization, lexical inference can help identifying redundancy, when two candidate sentences for the summary differ only in terms that hold a lexical inference relation (e.g. "the battery is long-lasting" and "the battery is enduring"). In this talk, I will present our work on automatic acquisition of lexical semantic relations from free text, focusing on two methods: the first is an integrated path-based and distributional method for recognizing lexical semantic relations (e.g. cat is a type of animal, tail is a part of cat). The second method focuses on the special case of interpreting the implicit semantic relation that holds between the constituent words of a noun compound (e.g. olive oil is made of olives, while baby oil is for babies).

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    • May 18, 2018

      Hany Hassan

      Machine translation has made rapid advances in recent years. Millions of people are using it today in online translation systems and mobile applications in order to communicate across language barriers. The question naturally arises whether such systems can approach or achieve parity with human translations. In this talk, we first describe our recent advances in Nerul Machine translation that led to SOTA results on news translation. We then address the problem of how to define and accurately measure human parity in translation. We will discuss our system achieving human performance and discuss limitations as well as future directions of current NMT systems.

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    • May 8, 2018

      Saining Xie

      With the support of big-data and big-compute, deep learning has reshaped the landscape of research and applications in artificial intelligence. Whilst traditional hand-guided feature engineering in many cases is simplified, the deep network architectures become increasingly more complex. A central question is if we can distill the minimal set of structural priors that can provide us the maximal flexibility and lead us to richer sets of structural primitives that potentially lay the foundations towards the ultimate goal of building general intelligent systems. In this talk I will introduce my Ph.D. work along the aforementioned direction. I will show how we can tackle different real world problems, with carefully designed architectures, guided by simple yet effective structural priors. In particular, I will focus on two structural priors that have proven to be useful in many different scenarios: the multi-scale prior and the sparse-connectivity prior. will also show examples of learning structural priors from data, instead of hard-wiring them.

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    • April 20, 2018

      Kyle Richardson

      In this talk, I will give an overview of research being done at the University of Stuttgart on semantic parser induction and natural language understanding. The main topic, semantic parser induction, relates to the problem of learning to map input text to full meaning representations from parallel datasets. Such resulting “semantic parsers” are often a core component in various downstream natural language understanding applications, including automated question-answering and generation systems. We look at learning within several novel domains and datasets being developed in Stuttgart (e.g., software documentation for text-to-code translation) and under various types of data supervision (e.g., learning from entailment, "polyglot" modeling, or learning from multiple datasets).

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