Developing conversational systems that can converse in many languages is an interesting challenge for natural language processing. In this paper, we introduce multilingual addressee and response selection. In this task, a conversational system predicts an appropriate addressee and response for an input message in multiple languages. A key to developing such multilingual responding systems is how to utilize high-resource language data to compensate for low-resource language data. We present several knowledge transfer methods for conversational systems. To evaluate our methods, we create a new multilingual conversation dataset. Experiments on the dataset demonstrate the effectiveness of our methods.

Comprehension of spoken natural language is an essential skill for robots to communicate with humans effectively. However, handling unconstrained spoken instructions is challenging due to (1) complex structures and the wide variety of expressions used in spoken language, and (2) inherent ambiguity of human instructions. In this paper, we propose the first comprehensive system for controlling robots with unconstrained spoken language, which is able to effectively resolve ambiguity in spoken instructions. Specifically, we integrate deep learning-based object detection together with natural language processing technologies to handle unconstrained spoken instructions, and propose a method for robots to resolve instruction ambiguity through dialogue. Through our experiments on both a simulated environment as well as a physical industrial robot arm, we demonstrate the ability of our system to understand natural instructions from human operators effectively, and show how higher success rates of the object picking task can be achieved through an interactive clarification process.

To create conversational systems working in actual situations, it is crucial to assume that they interact with multiple agents.In this work, we tackle addressee and response selection for multi-party conversation, in which systems are expected to select whom they address as well as what they say.The key challenge of this task is to jointly model who is talking about what in a previous context.For the joint modeling, we propose two modeling frameworks: 1) static modeling and 2) dynamic modeling.To show benchmark results of our frameworks, we created a multi-party conversation corpus.Our experiments on the dataset show that the recurrent neural network based models of our frameworks robustly predict addressees and responses in conversations with a large number of agents.

We propose a neural network approach to benefit from the non-linearity of corpus-wide statistics for part-of-speech (POS) tagging. We investigated several types of corpus-wide information for the words, such as word embeddings and POS tag dis-tributions. Since these statistics are en-coded as dense continuous features, it is not trivial to combine these features com-paring with sparse discrete features. Our tagger is designed as a combination of a linear model for discrete features and a feed-forward neural network that cap-tures the non-linear interactions among the continuous features. By using several re-cent advances in the activation functions for neural networks, the proposed method marks new state-of-the-art accuracies for English POS tagging tasks. 1

We present a clustered personal classifier method (CPC method) that jointly estimates a classifier and clusters of workers in order to address the learning from crowds problem.Crowdsourcing allows us to create a large but low-quality data set at very low cost.The learning from crowds problem is to learn a classifier from such a low-quality data set.From some observations, we notice that workers form clusters according to their abilities.Although such a fact was pointed out several times, no method has applied it to the learning from crowds problem.We propose a CPC method that utilizes the clusters of the workers to improve the performance of the obtained classifier, where both the classifier and the clusters of the workers are estimated.The proposed method has two advantages.One is that it realizes robust estimation of the classifier because it utilizes prior knowledge about the workers that they tend to form clusters.The other is that we can obtain the clusters of the workers, which help us analyze the properties of the workers.Experimental results on synthetic and real data sets indicate that the proposed method can estimate the classifier robustly.In addition, clustering workers is shown to work well. Especially in the real data set, an outlier worker was found by applying the proposed method.

Recently crowdsourcing services are often used to collect a large amount of labeled data for machine learning, since they provide us an easy way to get labels at very low cost and in a short period. The use of crowdsourcing has introduced a new challenge in machine learning, that is, coping with the variable quality of crowd-generated data. Although there have been many recent attempts to address the quality problem of multiple workers, only a few of the existing methods consider the problem of learning classifiers directly from such noisy data. All these methods modeled the true labels as latent variables, which resulted in non-convex optimization problems. In this paper, we propose a convex optimization formulation for learning from crowds without estimating the true labels by introducing personal models of the individual crowd workers. We also devise an efficient iterative method for solving the convex optimization problems by exploiting conditional independence structures in multiple classifiers. We evaluate the proposed method against three competing methods on synthetic data sets and a real crowdsourced data set and demonstrate that the proposed method outperforms the other three methods.

We propose a fast batch learning method for linear-chain Conditional Random Fields (CRFs) based on Newton-CG methods. Newton-CG methods are a variant of Newton method for high-dimensional problems. They only require the Hessian-vector products instead of the full Hessian matrices. To speed up Newton-CG methods for the CRF learning, we derive a novel dynamic programming procedure for the Hessian-vector products of the CRF objective function. The proposed procedure can reuse the byproducts of the time-consuming gradient computation for the Hessian-vector products to drastically reduce the total computation time of the Newton-CG methods. In experiments with tasks in natural language processing, the proposed method outperforms a conventional quasi-Newton method. Remarkably, the proposed method is competitive with online learning algorithms that are fast but unstable.

We propose a new statistical approach to the problem of inlier-based outlier detection, i.e.,finding outliers in the test set based on the training set consisting only of inliers. Our key idea is to use the ratio of training and test data densities as an outlier score; we estimate the ratio directly in a semi-parametric fashion without going through density estimation. Thus our approach is expected to have better performance in high-dimensional problems. Furthermore, the applied algorithm for density ratio estimation is equipped with a natural cross-validation procedure, allowing us to objectively optimize the value of tuning parameters such as the regularization parameter and the kernel width. The algorithm offers a closed-form solution as well as a closed-form formula for the leave-one-out error. Thanks to this, the proposed outlier detection method is computationally very efficient and is scalable to massive datasets. Simulations with benchmark and real-world datasets illustrate the usefulness of the proposed approach.

We propose a new loss function for discriminative learning of Markov random fields, which is an intermediate loss function between the sequential loss and the pointwise loss. We show this loss function has “Markov property”, that is, the importance of correct labeling for a particular position depends on the numbers of the correct labels around there. This property works to keep local consistencies among the assigned labels, and is useful for optimizing systems identifying structural segments, such as information extraction systems.

Covariate shift is a situation in supervised learning where training and test inputs follow different distributions even though the functional relation remains unchanged. A common approach to compensating for the bias caused by covariate shift is to reweight the training samples according to importance, which is the ratio of test and training densities. We propose a novel method that allows us to directly estimate the importance from samples without going through the hard task of density estimation. An advantage of the proposed method is that the computation time is nearly independent of the number of test input samples, which is highly beneficial in recent applications with large numbers of unlabeled samples. We demonstrate through experiments that the proposed method is computationally more efficient than existing approaches with comparable accuracy.

We introduce a new perceptron-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses pointwise label prediction, large features, including arbitrary number of hidden variables, can be incorporated with polynomial time complexity. This is in contrast to existing labelers that can handle only features of a small number of hidden variables, such as Maximum Entropy Markov Models and Conditional Random Fields. We also introduce several kernel functions for labeling sequences, trees, and graphs and efficient algorithms for them.