Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks.Unfortunately, there is no clear consensus on which models to use for which tasks.Muddying things further is the lack of comparisons that take differing training sets into account.In this paper, we present an application-focused empirical survey of neural OpenIE models, training sets, and benchmarks in an effort to help users choose the most suitable OpenIE systems for their applications.We find that the different assumptions made by different models and datasets have a statistically significant effect on performance, making it important to choose the most appropriate model for one's applications.We demonstrate the applicability of our recommendations on a downstream Complex QA application.

Open Information Extraction (OpenIE) is a traditional NLP task that extracts structured information from unstructured text to be used for other downstream applications.Traditionally, OpenIE focuses on extracting the surface forms of relations as they appear in the raw text, which we term extractive OpenIE.One of the main drawbacks of this approach is that implicit semantic relations (inferred relations) can not be extracted, compromising the performance of downstream applicationsIn this paper, we broaden the scope of OpenIE relations from merely the surface form of relations to include inferred relations, which we term abstractive OpenIE.This new task calls for the development of a new abstractive OpenIE training dataset and a baseline neural model that can extract those inferred relations.We also demonstrate the necessity for a new semantics-based metric for evaluating abstractive OpenIE extractions.Via a case study on Complex QA, we demonstrate the effectiveness of abstractive OpenIE. 1 1 Code and models are available at https://github.com/ kevinpei/AbstractiveOpenIE Sample SentenceTokyo, officially Tokyo Metropolis, is the capital city of Japan and one of its 47 prefectures.Extractive {Tokyo; is; the capital city of Japan} OpenIE Extractions {Tokyo; is; one of its 47 prefectures} {Tokyo; is; the capital city of Japan} Abstractive {Tokyo; is officially; Tokyo Metropolis} OpenIE Extractions {Tokyo; is; a prefecture} or {Tokyo; is; one of Japan's 47 prefectures}

Semantic role labeling (SRL) represents the meaning of a sentence in the form of predicate-argument structures. Such shallow semantic analysis is helpful in a wide range of downstream NLP tasks and real-world applications. As treebanks enabled the development of powerful syntactic parsers, the accurate predicate-argument analysis demands training data in the form of propbanks. Unfortunately, most languages simply do not have corresponding propbanks due to the high cost required to construct such resources. To overcome such challenges, Universal Proposition Bank 1.0 (UP1.0) was released in 2017, with high-quality propbank data generated via a two-stage method exploiting monolingual SRL and multilingual parallel data. In this paper, we introduce Universal Proposition Bank 2.0 (UP2.0), with significant enhancements over UP1.0: (1) propbanks with higher quality by using a state-of-the-art monolingual SRL and improved auto-generation of annotations; (2) expanded language coverage (from 7 to 9 languages); (3) span annotation for the decoupling of syntactic analysis; and (4) Gold data for a subset of the languages. We also share our experimental results that confirm the significant quality improvements of the generated propbanks. In addition, we present a comprehensive experimental evaluation on how different implementation choices impact the quality of the resulting data. We release these resources to the research community and hope to encourage more research on cross-lingual SRL.

Argument classification is at the core of Semantic Role Labeling.Given a sentence and the predicate, a semantic role label is assigned to each argument of the predicate.While semantic roles come with meaningful definitions, existing work has treated them as symbolic.Learning symbolic labels usually requires ample training data, which is frequently unavailable due to the cost of annotation.We instead propose to retrieve and leverage the definitions of these labels from the annotation guidelines.For example, the verb predicate "work" has arguments defined as "worker", "job", "employer", etc.Our model achieves state-of-theart performance on the CoNLL09 English SRL dataset injected with label definitions given the predicate senses.The performance improvement is even more pronounced in low-resource settings when training data is scarce. 1

We consider the problem of detecting whether a tensor signal having many missing entities lies within a given low dimensional Kronecker-Structured (KS) subspace. This is a matched subspace detection problem. Tensor matched subspace detection problem is more challenging because of the intertwined signal dimensions. We solve this problem by projecting the signal onto the KS subspace, which is a Kronecker product of different subspaces corresponding to each signal dimension. Under this framework, we define the KS subspaces and the orthogonal projection of the signal onto the KS subspace. We prove that reliable detection is possible as long as the cardinality of the missing signal is greater than the dimensions of the KS subspace by bounding the residual energy of the sampling signal with high probability.

In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS trip data. Secondly, we develop a carpooling simulation environment for RL training, with the output of ST-NN and using the NYC taxi trip dataset. In order to maximize transportation efficiency and minimize traffic congestion, we choose the effective distance covered by the driver on a carpool trip as the reward. Therefore, the more effective distance a driver achieves over a trip (i.e. to satisfy more trip demand) the higher the efficiency and the less will be the traffic congestion. We compared the performance of RL learned policy to a fixed policy (which always accepts carpool) as a baseline and obtained promising results that are interpretable and demonstrate the advantage of our RL approach. We also compare the performance of ST-NN to that of state-of-the-art travel time estimation methods and observe that ST-NN significantly improves the prediction performance and is more robust to outliers.

Kronecker-structured (K-S) models have been proposed for the efficient representation, processing, and classification of multidimensional signals such as images and video. In this paper, we study the classification performance of Kronecker-structured models in two asymptotic regimes. First, we study the diversity order, the slope of the error probability as the signal noise power goes to zero. We derive an exact expression for the diversity order as a function of the signal and subspace dimensions of a K-S model. Next, we study the classification capacity, the maximum rate at which the number of classes can grow as the signal dimension goes to infinity. We derive upper and lower bounds on the prelog factor of the classification capacity. Finally, we evaluate the empirical classification performance of K-S models, showing that they agree with the diversity order analysis.

Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting for label noise when training deep neural networks. We augment a standard deep network with a softmax layer that models the label noise statistics. Then, we train the deep network and noise model jointly via end-to-end stochastic gradient descent on the (perhaps mislabeled) dataset. The augmented model is underdetermined, so in order to encourage the learning of a non-trivial noise model, we apply dropout regularization to the weights of the noise model during training. Numerical experiments on noisy versions of the CIFAR-10 and MNIST datasets show that the proposed dropout technique outperforms state-of-the-art methods.