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29th International Conference on Computational Linguistics, COLING 2022

作者： Ihori, Mana Sato, Hiroshi Tanaka, Tomohiro Masumura, Ryo NTT Computer and Data Science Laboratories NTT Corporation 1-1 Hikarinooka Yokosuka-Shi Kanagawa239-0847 Japan

This paper presents a novel multi-perspective document revision task. In conventional studies on document revision, tasks such as grammatical error correction, sentence reordering, and discourse relation classification have been performed individually;however, these tasks simultaneously should be revised to improve the readability and clarity of a whole document. Thus, our study defines multi-perspective document revision as a task that simultaneously revises multiple perspectives. To model the task, we design a novel Japanese multi-perspective document revision dataset that simultaneously handles seven perspectives to improve the readability and clarity of a document. Although a large amount of data that simultaneously handles multiple perspectives is needed to model multi-perspective document revision elaborately, it is difficult to prepare such a large amount of this data. Therefore, our study offers a multi-perspective document revision modeling method that can use a limited amount of matched data (i.e., data for the multi-perspective document revision task) and external partially-matched data (e.g., data for the grammatical error correction task). Experiments using our created dataset demonstrate the effectiveness of using multiple partially-matched datasets to model the multi-perspective document revision task. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Error correction

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Creating Smart Cities through Digital Twins

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NTT Technical Review 2024年第5期22卷 52-55页

作者： Shake, Ippei Yamamoto, Chihiro NTT Smart Data Science Center NTT Computer and Data Science Laboratories Japan

We describe our initiatives in creating smart cities using digital twin (DT) technology. In these Feature Articles on Urban DTC for Creating Optimized Smart Cities Attentive to the Individual, we first outline the concept of a totally optimized smart city. We then describe a variety of services that use digitalized data and artificial intelligence (AI), an integrated platform that achieves total optimization by interlinking those services, examples of services that link areas over a wide region, and the AI value platform that makes it easy to provide such cross-domain services. © 2024 Nippon Telegraph and Telephone Corp.. All rights reserved.

关键词： Smart city

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Refining Solar-power-generation Plans to Achieve Stable Power Supply by Predicting Total Solar Irradiance

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NTT Technical Review 2024年第5期22卷 102-109页

作者： Maki, Toshitaka Matsui, Kazuma Fujinami, Takashi Kurasawa, Hisashi Tomita, Junji NTT Smart Data Science Center NTT Computer and Data Science Laboratories Japan

To reduce the environmental burden, it is necessary to adjust the supply and demand of electricity so that the proportion of renewable energy is increased. For solar-power generation, which is widely used, total solar irradiance is the main variable used in power-generation planning because it is strongly correlated with the amount of electricity generated. However, measurement and prediction of total solar irradiance produce values with large errors, leading to large errors in power-generation plans, which in turn cause problems in terms of stable power supply and the cost of adjusting supply and demand. In light of these issues, we developed a technology that combines weather information and power-generation results to predict total solar irradiance with high accuracy in a manner that makes it possible to refine solar-power-generation plans. © 2024 Nippon Telegraph and Telephone Corp.. All rights reserved.

关键词： Solar power generation

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Recurrent neural networks for learning long-term temporal dependencies with reanalysis of time scale representation

arXiv

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arXiv 2021年

作者： Ohno, Kentaro Kumagai, Atsutoshi NTT Computer and Data Science Laboratories

Recurrent neural networks with a gating mechanism such as an LSTM or GRU are powerful tools to model sequential data. In the mechanism, a forget gate, which was introduced to control information flow in a hidden state in the RNN, has recently been re-interpreted as a representative of the time scale of the state, i.e., a measure how long the RNN retains information on inputs. On the basis of this interpretation, several parameter initialization methods to exploit prior knowledge on temporal dependencies in data have been proposed to improve learnability. However, the interpretation relies on various unrealistic assumptions, such as that there are no inputs after a certain time point. In this work, we reconsider this interpretation of the forget gate in a more realistic setting. We first generalize the existing theory on gated RNNs so that we can consider the case where inputs are successively given. We then argue that the interpretation of a forget gate as a temporal representation is valid when the gradient of loss with respect to the state decreases exponentially as time goes back. We empirically demonstrate that existing RNNs satisfy this gradient condition at the initial training phase on several tasks, which is in good agreement with previous initialization methods. On the basis of this finding, we propose an approach to construct new RNNs that can represent a longer time scale than conventional models, which will improve the learnability for long-term sequential data. We verify the effectiveness of our method by experiments with real-world datasets. © 2021, CC BY.

关键词： Time measurement

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Research on Transfer Learning to Give AI the Same Versatility and Skill as Humans

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NTT Technical Review 2021年第12期19卷 12-15页

作者： Kumagai, Atsutoshi NTT Computer and Data Science Laboratories NTT Social Informatics Laboratories Japan

Machine learning is needed to build artificial intelligence (AI), and this requires a large amount of training data. Sometimes, however, you cannot get enough high-quality training data. What’s more, to prevent an AI from becoming out of date, you need to re-train it regularly to keep pace with data that is constantly changing. In this interview, Distinguished Researcher Atsutoshi Kumagai talked to us about the technology of transfer learning, which can be used to improve AI performance even when ideal data cannot be obtained. © 2021 Nippon Telegraph and Telephone Corp.. All rights reserved.

关键词： Artificial intelligence

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Next-Speaker Prediction Based on Non-Verbal Information in Multi-Party Video Conversation

Next-Speaker Prediction Based on Non-Verbal Information in M...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Saki Mizuno Nobukatsu Hojo Satoshi Kobashikawa Ryo Masumura NTT Computer & Data Science Laboratories

We propose a method for next-speaker prediction, a task to predict who speaks in the next turn among multiple current listeners, in multi-party video conversation. Previous studies used non-verbal features, such as head movements and gaze behavior, for next-speaker prediction in face-to-face conversation. However, in video conversation, these non-verbal features are vague and ineffective because they look at the screen displaying other participants. Since non-verbal features include participant characteristics, it is necessary to use training data with rich combinations of participants to robustly predict the next speaker. Previous studies used training data with a limited number of combinations of participants because the data consist only of recorded data. Therefore, the proposed method uses 1) novel non-verbal features for next-speaker prediction in video conversation, specifically facial expressions, hand movements and speech segments, and 2) data augmentation of participant combinations in the training data. We conducted experiments to evaluate the proposed method, and the results using video-conversation data indicate its effectiveness.

关键词： Training data Oral communication Signal processing Acoustics Behavioral sciences Task analysis Speech processing

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Modeling Lead-Lag Structure in Facial Expression Synchrony for Social-Psychological Outcome Prediction from Negotiation Interaction

Modeling Lead-Lag Structure in Facial Expression Synchrony f...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Nobukatsu Hojo Saki Mizuno Satoshi Kobashikawa Ryo Masumura NTT Computer & Data Science Laboratories

This study proposes introducing facial-expression synchrony features to machine learning to estimate a customer’s psychological information from online business negotiation dialogue data. It is important for synchrony features to model the information on who led the synchrony and who followed it, the lead-lag structure, because the psychology of the leader and follower can differ. However, conventional synchrony models cannot incorporate such lead-lag structure information because they are based on the assumption that synchrony involves the co-occurrence of features in the same frame. To solve this problem, we propose using synchrony features extracted on the basis of windowed time-lagged cross-correlation, which cuts out a short segment from each of the input sequences and computes the cross-correlation between the segments. Since this method measures the similarity of signals across different frames, it is suitable for modeling the lead-lag structure. We conducted experiments based on an audio visual corpus of business negotiation dialogue assessed with various psychological measurements. The results indicate that considering lead-lag information can improve the accuracy in estimating psychological information.

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Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation

Recurrent Neural Networks for Learning Long-term Temporal De...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Kentaro Ohno Atsutoshi Kumagai NTT Computer and Data Science Laboratories

ISBN: (纸本)9781665438599

Recurrent neural networks with a gating mechanism such as an LSTM or GRU are powerful tools to model sequential data. In the mechanism, a forget gate, which was introduced to control information flow in a hidden state in the RNN, has recently been re-interpreted as a representative of the time scale of the state, i.e., a measure how long the RNN retains information on inputs. On the basis of this interpretation, several parameter initialization methods to exploit prior knowledge on temporal dependencies in data have been proposed to improve learn-ability. However, the interpretation relies on various unrealistic assumptions, such as that there are no inputs after a certain time point. In this work, we reconsider this interpretation of the forget gate in a more realistic setting. We first generalize the existing theory on gated RNNs so that we can consider the case where inputs are successively given. We then argue that the interpretation of a forget gate as a temporal representation is valid when the gradient of loss with respect to the state decreases exponentially as time goes back. We empirically demonstrate that existing RNNs satisfy this gradient condition at the initial training phase on several tasks, which is in good agreement with previous initialization methods. On the basis of this finding, we propose an approach to construct new RNNs that can represent a longer time scale than conventional models, which will improve the learnability for long-term sequential data. We verify the effectiveness of our method by experiments with real-world datasets.

关键词： Training Analytical models Recurrent neural networks Conferences Logic gates data models Time measurement

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Few-shot learning for feature selection with hilbert-schmidt independence criterion 22

Few-shot learning for feature selection with hilbert-schmidt...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Atsutoshi Kumagai Tomoharu Iwata Yasutoshi Ida Yasuhiro Fujiwara NTT Computer and Data Science Laboratories NTT Communication Science Laboratories

ISBN: (纸本)9781713871088

We propose a few-shot learning method for feature selection that can select relevant features given a small number of labeled instances. Existing methods require many labeled instances for accurate feature selection. However, sufficient instances are often unavailable. We use labeled instances in multiple related tasks to alleviate the lack of labeled instances in a target task. To measure the dependency between each feature and label, we use the Hilbert-Schmidt Independence Criterion, which is a kernel-based independence measure. By modeling the kernel functions with neural networks that take a few labeled instances in a task as input, we can encode the task- specific information to the kernels such that the kernels are appropriate for the task. Feature selection with such kernels is performed by using iterative optimization methods, in which each update step is obtained as a closed-form. This formulation enables us to directly and efficiently minimize the expected test error on features selected by a small number of labeled instances. We experimentally demonstrate that the proposed method outperforms existing feature selection methods.

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META-LEARNING FOR OUT-OF-DISTRIBUTION DETECTION VIA DENSITY ESTIMATION IN LATENT SPACE

arXiv

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arXiv 2022年

作者： Iwata, Tomoharu Kumagai, Atsutoshi NTT Communication Science Laboratories NTT Computer and Data Science Laboratories

Many neural network-based out-of-distribution (OoD) detection methods have been proposed. However, they require many training data for each target task. We propose a simple yet effective meta-learning method to detect OoD with small in-distribution data in a target task. With the proposed method, the OoD detection is performed by density estimation in a latent space. A neural network shared among all tasks is used to flexibly map instances in the original space to the latent space. The neural network is meta-learned such that the expected OoD detection performance is improved by using various tasks that are different from the target tasks. This meta-learning procedure enables us to obtain appropriate representations in the latent space for OoD detection. For density estimation, we use a Gaussian mixture model (GMM) with full covariance for each class. We can adapt the GMM parameters to in-distribution data in each task in a closed form by maximizing the likelihood. Since the closed form solution is differentiable, we can meta-learn the neural network efficiently with a stochastic gradient descent method by incorporating the solution into the meta-learning objective function. In experiments using six datasets, we demonstrate that the proposed method achieves better performance than existing meta-learning and OoD detection methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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