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18th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2017)

作者： Weiss, Ron J. Chorowski, Jan Jaitly, Navdeep Wu, Yonghui Chen, Zhifeng Google Brain Mountain View CA 94043 USA Nvidia Santa Clara CA USA

ISBN: (纸本)9781510848764

We present a recurrent encoder-decoder deep neural network architecture that directly translates speech in one language into text in another. The model does not explicitly transcribe the speech into text in the source language. nor does it require supervision from the ground truth source language transcription during training. We apply a slightly modified sequence-to-sequence with attention architecture that has previously been used for speech recognition and show that it can be repurposed for this more complex task, illustrating the power of attention-based models. A single model trained end-to-end obtains state-of-the-art performance on the Fisher Callhome Spanish-English speech translation task, outperforming a cascade of independently trained sequence-to-sequence speech recognition and machine translation models by 1.8 BLEU points on the Fisher test set. In addition, we find that making use of the training data in both languages by multi-task training sequence-to-sequence speech translation and recognition models with a shared encoder network can improve performance by a further 1.4 BLEU points.

关键词： speech translation sequence-to-sequence model

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AIML and sequence-to-sequence models to Build Artificial Intelligence Chatbots: Insights from a Comparative Analysis 2nd

AIML and Sequence-to-Sequence Models to Build Artificial Int...

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2nd International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM)

作者： Teckchandani, Nishant Santokhee, Aditya Bekaroo, Girish Middlesex Univ Mauritius Sch Sci & Technol Flic En Flac Mauritius

ISBN: (纸本)9783030182403;9783030182397

A chatbot is a software that is able to autonomously communicate with a human being through text and due to its usefulness, an increasing number of businesses are implementing such tools in order to provide timely communication to their clients. In the past, whilst literature has focused on implementing innovative chatbots and the evaluation of such tools, limited studies have been done to critically comparing such conversational systems. In order to address this gap, this study critically compares the Artificial Intelligence Mark-up Language (AIML), and sequence-to-sequence models for building chatbots. In this endeavor, two chatbots were developed to implement each model and were evaluated using a mixture of glass box and black box evaluation, based on 3 metrics, namely, user's satisfaction, the information retrieval rate, and the task completion rate of each chatbot. Results showed that the AIML chatbot ensured better user satisfaction, and task completion rate, while the sequence-to-sequence model had better information retrieval rate.

关键词： sequence-to-sequence model AIML Chatbot Conversational agents Artificial Intelligence Recurrent Neural Network

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Solving Turkish math word problems by sequence-to-sequence encoder-decoder models

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2023年第2期31卷 431-447页

作者： Gedik, Esin Güngör, Tunga Bogazici Univ Dept Comp Engn Istanbul Turkiye

Solving math word problems (MWP) is a challenging task due to the semantic gap between natural language texts , mathematical equations. The main purpose of the task is to take a written math problem as input and produce a proper equation as output for solving that problem. This paper describes a sequence-to-sequence (seq2seq) neural model for automatically solving Turkish MWPs based on their semantic meanings in the text. It comprises a bidirectional encoder to comprehend the semantics of the problem by encoding the input sequence and a decoder with attention to extract the equation by tracking the semantic meanings of the output symbols. We investigate the success of several embedding types, pretrained language models , neural models. Our research is novel in the sense that there exist no studies in Turkish on this natural language processing task that utilizes pretrained language models and neural models. There is also no Turkish dataset that can be used to train the neural models for the MWP task. As the first large-scale Turkish MWP dataset, we translated the well-known English MWP datasets into Turkish using a machine translation system. Although Turkish is an agglutinative and grammatically challenging language, the proposed models achieve around 72% accuracy on the dataset compiled from three English datasets.

关键词： Math word problems sequence-to-sequence model attention mechanism natural language processing

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Turkish Data-to-Text Generation Using sequence-to-sequence Neural Networks

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2023年第2期22卷 1-27页

作者： Demir, Seniz MEF Univ Dept Comp Engn Maslak Ayazaga Caddesi TR-34396 Istanbul Turkiye

End-to-end data-driven approaches lead to rapid development of language generation and dialogue systems. Despite the need for large amounts of well-organized data, these approaches jointly learn multiple components of the traditional generation pipeline without requiring costly human intervention. End-to-end approaches also enable the use of loosely aligned parallel datasets in system development by relaxing the degree of semantic correspondences between training data representations and text spans. However, their potential in Turkish language generation has not yet been fully exploited. In this work, we apply sequenceto-sequence (Seq2Seq) neural models to Turkish data-to-text generation where the input data given in the form of a meaning representation is verbalized. We explore encoder-decoder architectures with attention mechanism in unidirectional, bidirectional, and stacked recurrent neural network (RNN) models. Our models generate one-sentence biographies and dining venue descriptions using a crowdsourced dataset where all field value pairs that appear in meaning representations are fully captured in reference sentences. To support this work, we also explore the performances of our models on a more challenging dataset, where the content of a meaning representation is too large to fit into a single sentence, and hence content selection and surface realization need to be learned jointly. This dataset is retrieved by coupling introductory sentences of person-related Turkish Wikipedia articles with their contained infobox tables. Our empirical experiments on both datasets demonstrate that Seq2Seq models are capable of generating coherent and fluent biographies and venue descriptions from field value pairs. We argue that the wealth of knowledge residing in our datasets and the insights obtained fromthis study hold the potential to give rise to the development of new end-to-end generation approaches for Turkish and other morphologically rich languages.

关键词： Data-to-text generation sequence-to-sequence model Turkish Wikipedia

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JoinER-BART: Joint Entity and Relation Extraction With Constrained Decoding, Representation Reuse and Fusion

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2023年 31卷 3603-3616页

作者： Chang, Hongyang Xu, Hongfei van Genabith, Josef Xiong, Deyi Zan, Hongying Zhengzhou Univ Zhengzhou 450001 Henan Peoples R China DFKI D-66123 Saarbrucken Germany Saarland Univ Informat Campus D-66123 Saarbrucken Germany Tianjin Univ Tianjin 300350 Peoples R China

Joint Entity and Relation Extraction (JERE) is an important research direction in Information Extraction (IE). Given the surprising performance with fine-tuning of pre-trained BERT in a wide range of NLP tasks, nowadays most studies for JERE are based on the BERT model. Rather than predicting a simple tag for each word, these approaches are usually forced to design complex tagging schemes, as they may have to extract entity-relation pairs which may overlap with others from the same sequence of word representations in a sentence. Recently, sequence-to-sequence (seq2seq) pre-trained BART models show better performance than BERT models in many NLP tasks. Importantly, a seq2seq BART model can simply generate sequences of (many) entity-relation triplets with its decoder, rather than just tag input words. In this article, we present a new generative JERE framework based on pre-trained BART. Different from the basic seq2seq BART architecture: 1) our framework employs a constrained classifier which only predicts either a token of the input sentence or a relation in each decoding step, and 2) we reuse representations from the pre-trained BART encoder in the classifier instead of a newly trained weight matrix, as this better utilizes the knowledge of the pre-trained model and context-aware representations for classification, and empirically leads to better performance. In our experiments on the widely studied NYT and WebNLG datasets, we show that our approach outperforms previous studies and establishes a new state-of-the-art (92.91 and 91.37 F1 respectively in exact match evaluation).

关键词： Joint Entity and Relation Extraction pre-trained BART sequence-to-sequence model

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A multi-step ahead global solar radiation prediction method using an attention-based transformer model with an interpretable mechanism

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第40期48卷 15317-15330页

作者： Zhou, Yong Li, Yizhuo Wang, Dengjia Liu, Yanfeng Xian Univ Architecture & Technol Sch Management 13 Yanta Rd Xian 710055 Peoples R China Xi an Univ Architecture & Technol State Key Lab Green Bldg Western China 13 Yanta Rd Xian 710055 Peoples R China Xian Univ Architecture & Technol Sch Bldg Serv Sci & Engn 13 Yanta Rd Xian 710055 Peoples R China

The conventional multi-step ahead solar radiation prediction method ignores the time -dependence of a future solar radiation time series. Therefore, according to sequence-to -sequence (seq2seq) model theory, this paper proposes the seq2seq long-and short-term memory model (seq2seq-LSTM), the seq2seq-LSTM model with an attention mechanism (seq2seq-at-LSTM), and a transformer model, which consists only of the attention mech-anism. The hourly global solar radiation data between 2016 and 2018 from Shaanxi, China, is used to train and validate the models. The results show that the introduction of the attention mechanism can effectively improve the prediction accuracy of the seq2seq-LSTM model. However, the model is still not very good at capturing the long-distance depen-dence of the solar radiation time series due to the inherent properties of LSTM. In com-parison, the transformer model, which is based entirely on the attention mechanism, performs much better at capturing the long-distance dependence of the solar radiation time series. Furthermore, as the number of time-steps increases, the performance of the solar radiation prediction decreases relatively smoothly and slowly. The obtained average coefficient of determination, root mean square error (RMSE), relative RMSE, and mean bias error are 0.9788, 72.91 W/m2, 25.25%, and 38.35 W/m2, respectively. In addition, the average skill score of the transformer model is around 44.9%, which is 20.54% higher than that of the seq2seq-at-LSTM model and about 40.84% higher than that of the seq2seq-LSTM model. Besides, the use of the attention mechanism can explain the improved prediction compared to other models. This model developed in this study could also be used for

关键词： Multi-step ahead Global solar radiation model predictions sequence-to-sequence model Transformer model

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Discontinuous grammar as a foreign language

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NEUROCOMPUTING 2023年 524卷 43-58页

作者： Fernandez-Gonzalez, Daniel Gomez-Rodriguez, Carlos Univ A Coruna Dept Ciencias Comp & Tecnol Informac FASTPARSE Lab CITICLyS Grp Campus de Elvina s-n La Coruna 15071 Spain

In order to achieve deep natural language understanding, syntactic constituent parsing is a vital step, highly demanded by many artificial intelligence systems to process both text and speech. One of the most recent proposals is the use of standard sequence-to-sequence models to perform constituent parsing as a machine translation task, instead of applying task-specific parsers. While they show a competitive performance, these text-to-parse transducers are still lagging behind classic techniques in terms of accuracy, coverage and speed. To close the gap, we here extend the framework of sequence-to-sequence models for constituent parsing, not only by providing a more powerful neural architecture for improving their performance, but also by enlarging their coverage to handle the most complex syntactic phenomena: discontinuous structures. To that end, we design several novel linearizations that can fully produce discontinuities and, for the first time, we test a sequence-to-sequence model on the main discontinuous benchmarks, obtaining competitive results on par with task-specific discontinuous constituent parsers and achieving state-of-the-art scores on the (discontinuous) English Penn Treebank. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Natural language processing Computational linguistics Parsing Discontinuous constituent parsing Neural network Deep learning sequence-to-sequence model

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Improving Open Information Extraction with Distant Supervision Learning

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NEURAL PROCESSING LETTERS 2021年第5期53卷 3287-3306页

作者： Han, Jiabao Wang, Hongzhi Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China

Open information extraction (Open IE), as one of the essential applications in the area of Natural Language Processing (NLP), has gained great attention in recent years. As a critical technology for building Knowledge Bases (KBs), it converts unstructured natural language sentences into structured representations, usually expressed in the form of triples. Most conventional open information extraction approaches leverage a series of manual pre-defined extraction patterns or learn patterns from labeled training examples, which requires a large number of human resources. Additionally, many Natural Language Processing tools are involved, which leads to error accumulation and propagation. With the rapid development of neural networks, neural-based models can minimize the error propagation problem, but it also faces the problem of data-hungry in supervised learning. Especially, they leverage existing Open IE tools to generate training data, and it causes data quality issues. In this paper, we employ a distant supervision learning approach to improve the Open IE task. We conduct extensive experiments by employing two popular sequence-to-sequence models (RNN and Transformer) and a large benchmark data set to demonstrate the performance of our approach.

关键词： Distant supervision learning Open information extraction Neural network sequence-to-sequence model

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Travel Mode Identification for Non-Uniform Passive Mobile Phone Data

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第9期25卷 11103-11116页

作者： Zeng, Jiaqi Huang, Yulang Zhang, Guozheng Cai, Zhengyi Wang, Dianhai Zhejiang Univ Inst Intelligent Transportat Syst Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China Zhejiang Univ Zhongyuan Inst Zhengzhou 450000 Peoples R China Hangzhou City Univ Intelligent Transportat Syst Res Ctr Hangzhou 310058 Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China

The collection of individual GPS data, as a substitute for traditional travel surveys, is hindered by low response rates and high costs. Meanwhile, passive mobile phone data, such as Location-Based Service (LBS) data, has high user penetration but remains unexplored for travel behavior analysis. The irregular frequency of data collection results in distorted features and non-uniformly distributed information of trajectories in the Travel Mode Identification (TMI) task. The diversity of trajectories also poses challenges for TMI models. To fill the gap, we propose a TMI framework named Trajectory-as-a-sequence for Non-uniform data (TaaSN). Specifically, we incorporate GIS features to address the sparsity of motion features. Then, we design a model structure that accounts for time gaps, capturing non-uniform information of trajectory due to irregular frequency. To further enhance model's generalizability to diverse trajectory data, we propose a trajectory point dropout training strategy. The experimental results demonstrate that the TaaSN framework can greatly exploit the potential of LBS data in travel behavior mining. The proposed model achieves high accuracy on both non-uniform and uniform trajectories. On pseudo-LBS data, the accuracy reaches 84.9% when applied to the trajectories of existing travelers, and achieves 83.5% when applied to the trajectories of new travelers. On uniform data, it reaches 86.8% and 85.4%, respectively. Furthermore, we carry out comprehensive experiments and conclude valuable insights for future TMI framework design.

关键词： Trajectory Global Positioning System Data models Mobile handsets Task analysis Training Motion segmentation Travel mode identification location-based service data GPS data deep learning sequence-to-sequence model

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Overview of the sixth dialog system technology challenge: DSTC6

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COMPUTER SPEECH AND LANGUAGE 2019年 55卷 1-25页

作者： Horia, Chiori Perez, Julien Higashinakac, Ryuichiro Horia, Takaaki Boureau, Y-Lan Inabae, Michimasa Tsunomorif, Yuiko Takahashig, Tetsuro Yoshinoh, Koichiro Kim, Seokhwan Mitsubishi Elect Res Lab Cambridge MA USA Naver Labs Europe Grenoble France NTT Corp NTT Media Intelligence Labs 1-1 Hikarinooka Yokosuka Kanagawa 2390847 Japan Facebook AI Res New York NY USA Hiroshima City Univ Grad Sch Informat Sci Asaminami Ku 3-4-1 Ozukahigashi Hiroshima 7313194 Japan NTT DOCOMO INC Serv Dev Grp Minato Ku 4-5 Akasaka 2 Chome Tokyo 1079992 Japan Fujitsu Labs LTD AI Deployment Project Nakahara Ku 4-1-1 Kamikodanaka Kawasaki Kanagawa 2118588 Japan Nara Inst Sci & Technol Nara Japan Adobe Res San Jose CA USA

This paper describes the experimental setups and the evaluation results of the sixth Dialog System Technology Challenges (DSTC6) aiming to develop end-to-end dialogue systems. Neural network models have become a recent focus of investigation in dialogue technologies. Previous models required training data to be manually annotated with word meanings and dialogue states, but end-to-end neural network dialogue systems learn to directly output natural-language system responses without needing training data to be manually annotated. Thus, this approach allows us to scale up the size of training data and cover more dialog domains. In addition, dialogue systems require a meta-function to avoid deploying inappropriate responses generated by themselves. To challenge such issues, the DSTC6 consists of three tracks, (1). End-to-End Goal Oriented dialogue Learning to select system responses, (2). End-to-End Conversation modeling to generate system responses using Natural Language Generation (NLG) and (3). Dialogue Breakdown Detection. Since each domain has different issues to be addressed to develop dialogue systems, we targeted restaurant retrieval dialogues to fill slot-value in Track 1, customer services on Twitter by combining goal-oriented dialogues and ChitChat in Track 2 and human-machine dialogue data for ChitChat in Track 3. DSTC6 had 141 people declaring their interests and 23 teams submitted their final results. 18 scientific papers were presented in the wrap-up workshop. We find the blending end-to-end trainable models associated to meaningful prior knowledge performs the best for the restaurant retrieval for Track 1. Indeed, Hybrid Code Network and Memory Network have been the best models for this task. In Track 2, 78.5% of the system responses automatically generated by the best system were rated better than acceptable by humans and this achieves 89% of the number of the human responses rated in the same class. In Track3, the dialogue breakdown detection technolog

关键词： DSTC End-to-end dialogue system Conversation model sequence-to-sequence model Natural Language Generation Dialogue breakdown

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