Trace2trace—A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories

Neural machine translation is a prominent field in the computational linguistics domain. By leveraging the recent developments of deep learning, it gave birth to powerful algorithms for translating text from one language to another. This study aims to assess the feasibility of transferring the neural machine translation approach into a completely different context, namely human mobility and trajectory analysis. Building a conceptual parallelism between sentences (sequences of words) and motion traces (sequences of locations), we aspire to translate individual trajectories generated by a certain category of users into the corresponding mobility traces potentially generated by a different category of users. The experiment is inserted in the background of tourist mobility analysis, with the goal of translating the motion behavior of tourists belonging to a specific nationality into the motion behavior of tourists belonging to a different nationality. The model adopted is based on the seq2seq approach and consists of an encoder–decoder architecture based on long short-term memory (LSTM) neural networks and neural embeddings. The encoder turns an input location sequence into a corresponding hidden vector; the decoder reverses the process, turning the vector into an output location sequence. The proposed framework, tested on a real-world large-scale dataset, explores an effective attempt of motion transformation between different entities, arising as a potentially powerful source of mobility information disclosure, especially in the context of crowd management and smart city services.

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LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists

Sustainability ◽

10.3390/su12010349 ◽

2020 ◽

Vol 12 (1) ◽

pp. 349 ◽

Cited By ~ 2

Author(s):

Alessandro Crivellari ◽

Euro Beinat

Keyword(s):

Deep Learning ◽

Large Scale ◽

Short Term Memory ◽

Human Mobility ◽

Short Term ◽

Motion Patterns ◽

Motion Data ◽

Crowd Management ◽

Wide Range ◽

Traditional Approaches

The increasing availability of trajectory recordings has led to the mining of a massive amount of historical track data, allowing for a better understanding of travel behaviors by revealing meaningful motion patterns. In the context of human mobility analysis, the problem of motion prediction assumes a central role and is beneficial for a wide range of applications, including for touristic purposes, such as personalized services or targeted recommendations, and sustainability studies related to crowd management and resource redistribution. This paper tackles a particular case of the trajectory prediction problem, focusing on large-scale mobility traces of short-term foreign tourists. These sparse trajectories, short and non-repetitive, lack spatial and temporal regularity, making prediction analysis based on individual historical motion data unreliable. To face this issue, we hereby propose a deep learning-based approach, taking into account the collective mobility of tourists over the territory. The underlying semantics of motion patterns are captured by means of a long short-term memory (LSTM) neural network model trained on pre-processed location sequences, aiming to predict the next visited place in the trajectory. We tested the methodology on a real-world big dataset, demonstrating its higher feasibility with respect to traditional approaches.

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A Joint Back-Translation and Transfer Learning Method for Low-Resource Neural Machine Translation

Mathematical Problems in Engineering ◽

10.1155/2020/6140153 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Gong-Xu Luo ◽

Ya-Ting Yang ◽

Rui Dong ◽

Yan-Hong Chen ◽

Wen-Bo Zhang

Keyword(s):

Machine Translation ◽

Transfer Learning ◽

Large Scale ◽

Data Augmentation ◽

Training Methods ◽

Learning Method ◽

Neural Machine Translation ◽

Low Resource ◽

Parallel Data ◽

Back Translation

Neural machine translation (NMT) for low-resource languages has drawn great attention in recent years. In this paper, we propose a joint back-translation and transfer learning method for low-resource languages. It is widely recognized that data augmentation methods and transfer learning methods are both straight forward and effective ways for low-resource problems. However, existing methods, which utilize one of these methods alone, limit the capacity of NMT models for low-resource problems. In order to make full use of the advantages of existing methods and further improve the translation performance of low-resource languages, we propose a new method to perfectly integrate the back-translation method with mainstream transfer learning architectures, which can not only initialize the NMT model by transferring parameters of the pretrained models, but also generate synthetic parallel data by translating large-scale monolingual data of the target side to boost the fluency of translations. We conduct experiments to explore the effectiveness of the joint method by incorporating back-translation into the parent-child and the hierarchical transfer learning architecture. In addition, different preprocessing and training methods are explored to get better performance. Experimental results on Uygur-Chinese and Turkish-English translation demonstrate the superiority of the proposed method over the baselines that use single methods.

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Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation

Transactions of the Association for Computational Linguistics ◽

10.1162/tacl_a_00105 ◽

2016 ◽

Vol 4 ◽

pp. 371-383 ◽

Cited By ~ 40

Author(s):

Jie Zhou ◽

Ying Cao ◽

Xuguang Wang ◽

Peng Li ◽

Wei Xu

Keyword(s):

Machine Translation ◽

Short Term Memory ◽

Short Term ◽

Neural Machine Translation ◽

Attention Model ◽

Linear Connections ◽

New Type ◽

Long Short Term Memory ◽

Unknown Words ◽

First Time

Neural machine translation (NMT) aims at solving machine translation (MT) problems using neural networks and has exhibited promising results in recent years. However, most of the existing NMT models are shallow and there is still a performance gap between a single NMT model and the best conventional MT system. In this work, we introduce a new type of linear connections, named fast-forward connections, based on deep Long Short-Term Memory (LSTM) networks, and an interleaved bi-directional architecture for stacking the LSTM layers. Fast-forward connections play an essential role in propagating the gradients and building a deep topology of depth 16. On the WMT’14 English-to-French task, we achieve BLEU=37.7 with a single attention model, which outperforms the corresponding single shallow model by 6.2 BLEU points. This is the first time that a single NMT model achieves state-of-the-art performance and outperforms the best conventional model by 0.7 BLEU points. We can still achieve BLEU=36.3 even without using an attention mechanism. After special handling of unknown words and model ensembling, we obtain the best score reported to date on this task with BLEU=40.4. Our models are also validated on the more difficult WMT’14 English-to-German task.

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Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings

Applied Sciences ◽

10.3390/app9142861 ◽

2019 ◽

Vol 9 (14) ◽

pp. 2861 ◽

Cited By ~ 2

Author(s):

Alessandro Crivellari ◽

Euro Beinat

Keyword(s):

Neural Network ◽

Motion Tracking ◽

Large Scale ◽

Short Term Memory ◽

Human Mobility ◽

User Profiling ◽

Personalized Recommendation ◽

Mobility Patterns ◽

Trajectory Data ◽

Short Term

The interest in human mobility analysis has increased with the rapid growth of positioning technology and motion tracking, leading to a variety of studies based on trajectory recordings. Mapping the routes that people commonly perform was revealed to be very useful for location-based service applications, where individual mobility behaviors can potentially disclose meaningful information about each customer and be fruitfully used for personalized recommendation systems. This paper tackles a novel trajectory labeling problem related to the context of user profiling in “smart” tourism, inferring the nationality of individual users on the basis of their motion trajectories. In particular, we use large-scale motion traces of short-term foreign visitors as a way of detecting the nationality of individuals. This task is not trivial, relying on the hypothesis that foreign tourists of different nationalities may not only visit different locations, but also move in a different way between the same locations. The problem is defined as a multinomial classification with a few tens of classes (nationalities) and sparse location-based trajectory data. We hereby propose a machine learning-based methodology, consisting of a long short-term memory (LSTM) neural network trained on vector representations of locations, in order to capture the underlying semantics of user mobility patterns. Experiments conducted on a real-world big dataset demonstrate that our method achieves considerably higher performances than baseline and traditional approaches.

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Audio–Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model

Applied Sciences ◽

10.3390/app10207263 ◽

2020 ◽

Vol 10 (20) ◽

pp. 7263

Author(s):

Yong-Hyeok Lee ◽

Dong-Won Jang ◽

Jae-Bin Kim ◽

Rae-Hong Park ◽

Hyung-Min Park

Keyword(s):

Speech Recognition ◽

Machine Translation ◽

Short Term Memory ◽

Visual Speech ◽

Visual Modality ◽

Neural Machine Translation ◽

Visual Speech Recognition ◽

Context Vector ◽

Improved Performance ◽

Transformer Model

Since attention mechanism was introduced in neural machine translation, attention has been combined with the long short-term memory (LSTM) or replaced the LSTM in a transformer model to overcome the sequence-to-sequence (seq2seq) problems with the LSTM. In contrast to the neural machine translation, audio–visual speech recognition (AVSR) may provide improved performance by learning the correlation between audio and visual modalities. As a result that the audio has richer information than the video related to lips, AVSR is hard to train attentions with balanced modalities. In order to increase the role of visual modality to a level of audio modality by fully exploiting input information in learning attentions, we propose a dual cross-modality (DCM) attention scheme that utilizes both an audio context vector using video query and a video context vector using audio query. Furthermore, we introduce a connectionist-temporal-classification (CTC) loss in combination with our attention-based model to force monotonic alignments required in AVSR. Recognition experiments on LRS2-BBC and LRS3-TED datasets showed that the proposed model with the DCM attention scheme and the hybrid CTC/attention architecture achieved at least a relative improvement of 7.3% on average in the word error rate (WER) compared to competing methods based on the transformer model.

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Identifying Semantics in Clinical Reports Using Neural Machine Translation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33019552 ◽

2019 ◽

Vol 33 ◽

pp. 9552-9557

Author(s):

Srikanth Mujjiga ◽

Vamsi Krishna ◽

Kalyan Chakravarthi ◽

Vijayananda J

Keyword(s):

Machine Translation ◽

Large Scale ◽

Language Translation ◽

Semantic Search ◽

Neural Machine Translation ◽

Healthcare Facilities ◽

Semantic Level ◽

Concept Space ◽

Unique Approach ◽

Retrieval Problem

Clinical documents are vital resources for radiologists when they have to consult or refer while studying similar cases. In large healthcare facilities where millions of reports are generated, searching for relevant documents is quite challenging. With abundant interchangeable words in clinical domain, understanding the semantics of the words in the clinical documents is vital to improve the search results. This paper details an end to end semantic search application to address the large scale information retrieval problem of clinical reports. The paper specifically focuses on the challenge of identifying semantics in the clinical reports to facilitate search at semantic level. The semantic search works by mapping the documents into the concept space and the search is performed in the concept space. A unique approach of framing the concept mapping problem as a language translation problem is proposed in this paper. The concept mapper is modelled using the Neural machine translation model (NMT) based on encoder-decoder with attention architecture. The regular expression based concept mapper takes approximately 3 seconds to extract UMLS concepts from a single document, where as the trained NMT does the same in approximately 30 milliseconds. NMT based model further enables incorporation of negation detection to identify whether a concept is negated or not, facilitating search for negated queries.

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Synchronous Bidirectional Neural Machine Translation

Transactions of the Association for Computational Linguistics ◽

10.1162/tacl_a_00256 ◽

2019 ◽

Vol 7 ◽

pp. 91-105 ◽

Cited By ~ 8

Author(s):

Long Zhou ◽

Jiajun Zhang ◽

Chengqing Zong

Keyword(s):

Machine Translation ◽

Large Scale ◽

State Of The Art ◽

Target Language ◽

Single Model ◽

Neural Machine Translation ◽

German Translation ◽

Transformer Model ◽

Target Side ◽

Future Information

Existing approaches to neural machine translation (NMT) generate the target language sequence token-by-token from left to right. However, this kind of unidirectional decoding framework cannot make full use of the target-side future contexts which can be produced in a right-to-left decoding direction, and thus suffers from the issue of unbalanced outputs. In this paper, we introduce a synchronous bidirectional–neural machine translation (SB-NMT) that predicts its outputs using left-to-right and right-to-left decoding simultaneously and interactively, in order to leverage both of the history and future information at the same time. Specifically, we first propose a new algorithm that enables synchronous bidirectional decoding in a single model. Then, we present an interactive decoding model in which left-to-right (right-to-left) generation does not only depend on its previously generated outputs, but also relies on future contexts predicted by right-to-left (left-to-right) decoding. We extensively evaluate the proposed SB-NMT model on large-scale NIST Chinese-English, WMT14 English-German, and WMT18 Russian-English translation tasks. Experimental results demonstrate that our model achieves significant improvements over the strong Transformer model by 3.92, 1.49, and 1.04 BLEU points, respectively, and obtains the state-of-the-art per- formance on Chinese-English and English- German translation tasks. 1

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Human Mobility Patterns at the Smallest Scales

Communications in Computational Physics ◽

10.4208/cicp.120614.190115a ◽

2015 ◽

Vol 18 (2) ◽

pp. 417-428 ◽

Cited By ~ 3

Author(s):

Pedro G. Lind ◽

Adriano Moreira

Keyword(s):

Large Scale ◽

Spatial Scales ◽

Human Mobility ◽

Numerical Approach ◽

Human Motion ◽

Mobile Phone Data ◽

Data Sets ◽

Mobility Patterns ◽

Access Points ◽

Dollar Bill

AbstractWe present a study on human mobility at small spatial scales. Differently from large scale mobility, recently studied through dollar-bill tracking and mobile phone data sets within one big country or continent, we report Brownian features of human mobility at smaller scales. In particular, the scaling exponents found at the smallest scales is typically close to one-half, differently from the larger values for the exponent characterizing mobility at larger scales. We carefully analyze 12-month data of the Eduroam database within the Portuguese university of Minho. A full procedure is introduced with the aim of properly characterizing the human mobility within the network of access points composing the wireless system of the university. In particular, measures of flux are introduced for estimating a distance between access points. This distance is typically non-Euclidean, since the spatial constraints at such small scales distort the continuum space on which human mobility occurs. Since two different exponents are found depending on the scale human motion takes place, we raise the question at which scale the transition from Brownian to non-Brownian motion takes place. In this context, we discuss how the numerical approach can be extended to larger scales, using the full Eduroam in Europe and in Asia, for uncovering the transition between both dynamical regimes.

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Neural machine translation of low-resource languages using SMT phrase pair injection

Natural Language Engineering ◽

10.1017/s1351324920000303 ◽

2020 ◽

pp. 1-22

Author(s):

Sukanta Sen ◽

Mohammed Hasanuzzaman ◽

Asif Ekbal ◽

Pushpak Bhattacharyya ◽

Andy Way

Keyword(s):

Machine Translation ◽

Large Scale ◽

Production Systems ◽

Statistical Machine Translation ◽

Training Data ◽

Original Training ◽

Neural Machine Translation ◽

Parallel Corpus ◽

Low Resource ◽

Better Than

Abstract Neural machine translation (NMT) has recently shown promising results on publicly available benchmark datasets and is being rapidly adopted in various production systems. However, it requires high-quality large-scale parallel corpus, and it is not always possible to have sufficiently large corpus as it requires time, money, and professionals. Hence, many existing large-scale parallel corpus are limited to the specific languages and domains. In this paper, we propose an effective approach to improve an NMT system in low-resource scenario without using any additional data. Our approach aims at augmenting the original training data by means of parallel phrases extracted from the original training data itself using a statistical machine translation (SMT) system. Our proposed approach is based on the gated recurrent unit (GRU) and transformer networks. We choose the Hindi–English, Hindi–Bengali datasets for Health, Tourism, and Judicial (only for Hindi–English) domains. We train our NMT models for 10 translation directions, each using only 5–23k parallel sentences. Experiments show the improvements in the range of 1.38–15.36 BiLingual Evaluation Understudy points over the baseline systems. Experiments show that transformer models perform better than GRU models in low-resource scenarios. In addition to that, we also find that our proposed method outperforms SMT—which is known to work better than the neural models in low-resource scenarios—for some translation directions. In order to further show the effectiveness of our proposed model, we also employ our approach to another interesting NMT task, for example, old-to-modern English translation, using a tiny parallel corpus of only 2.7K sentences. For this task, we use publicly available old-modern English text which is approximately 1000 years old. Evaluation for this task shows significant improvement over the baseline NMT.

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A comparative study of neural machine translation models for Turkish language

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211453 ◽

2021 ◽

pp. 1-11

Author(s):

Özgür Özdemir ◽

Emre Salih Akın ◽

Rıza Velioğlu ◽

Tuğba Dalyan

Keyword(s):

Machine Translation ◽

Computational Linguistics ◽

Attention Mechanism ◽

Neural Machine Translation ◽

English Translations ◽

Benchmark Datasets ◽

Important Challenge ◽

Morphologically Rich Languages ◽

Transformer Model ◽

Ted Talks

Machine translation (MT) is an important challenge in the fields of Computational Linguistics. In this study, we conducted neural machine translation (NMT) experiments on two different architectures. First, Sequence to Sequence (Seq2Seq) architecture along with a variation that utilizes attention mechanism is performed on translation task. Second, an architecture that is fully based on the self-attention mechanism, namely Transformer, is employed to perform a comprehensive comparison. Besides, the contribution of employing Byte Pair Encoding (BPE) and Gumbel Softmax distributions are examined for both architectures. The experiments are conducted on two different datasets: TED Talks that is one of the popular benchmark datasets for NMT especially among morphologically rich languages like Turkish and WMT18 News dataset that is provided by The Third Conference on Machine Translation (WMT) for shared tasks on various aspects of machine translation. The evaluation of Turkish-to-English translations’ results demonstrate that the Transformer model with combination of BPE and Gumbel Softmax achieved 22.4 BLEU score on TED Talks and 38.7 BLUE score on WMT18 News dataset. The empirical results support that using Gumbel Softmax distribution improves the quality of translations for both architectures.

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