检索结果-内蒙古大学图书馆

Adversarial training and decoding strategies for end-to-end neural conversation models

COMPUTER SPEECH AND LANGUAGE 2019年 54卷 122-139页

作者： Hori, Takaaki Wang, Wen Koji, Yusuke Hori, Chiori Harsham, Bret Hershey, John R. Mitsubishi Elect Res Labs Cambridge MA 02139 USA Mitsubishi Electr Corp Informat Technol R&D Ctr Kamakura Kanagawa Japan

This paper presents adversarial training and decoding methods for neural conversation models that can generate natural responses given dialog contexts. In our prior work, we built several end-to-end conversation systems for the 6th Dialog System Technology Challenges (DSTC6) Twitter help-desk dialog task. These systems included novel extensions of sequence adversarial training, example-based response extraction, and Minimum Bayes-Risk based system combination. In DSTC6, our systems achieved the best performance in most objective measures such as BLEU and METEOR scores and decent performance in a subjective measure based on human rating. In this paper, we provide a complete set of our experiments for DSTC6 and further extend the training and decoding strategies more focusing on improving the subjective measure, where we combine responses of three adversarial models. Experimental results demonstrate that the extended methods improve the human rating score and outperform the best score in DSTC6. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Dialog system Conversation model sequence-to-sequence model Sentence generation

来源：评论

学校读者我要写书评

暂无评论

Speech Emotion Recognition Considering Nonverbal Vocalization in Affective Conversations

引用

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2021年 29卷 1675-1686页

作者： Hsu, Jia-Hao Su, Ming-Hsiang Wu, Chung-Hsien Chen, Yi-Hsuan Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 70101 Taiwan

In real-life communication, nonverbal vocalization such as laughter, cries or other emotion interjections, within an utterance play an important role for emotion expression. In previous studies, only few emotion recognition systems consider nonverbal vocalization, which naturally exists in our daily conversation. In this work, both verbal and nonverbal sounds within an utterance are considered for emotion recognition of real-life affective conversations. Firstly, a support vector machine (SVM)-based verbal and nonverbal sound detector is developed. A prosodic phrase auto-tagger is further employed to extract the verbal/nonverbal sound segments. For each segment, the emotion and sound feature embeddings are respectively extracted using the deep residual networks (ResNets). Finally, a sequence of the extracted feature embeddings for the entire dialog turn are fed to an attentive long short-term memory (LSTM)-based sequence-to-sequence model to output an emotional sequence as recognition result. The NNIME corpus (The NTHU-NTUA Chinese interactive multimodal emotion corpus), which consists of verbal and nonverbal sounds, was adopted for system training and testing. 4766 single speaker dialogue turns in the audio data of the NNIME corpus were selected for evaluation. The experimental results showed that nonverbal vocalization was helpful for speech emotion recognition. For comparison, the proposed method based on decision-level fusion achieved an accuracy of 61.92% for speech emotion recognition outperforming the traditional methods as well as the feature-level and model-level fusion approaches.

关键词： Speech recognition Emotion recognition Feature extraction Artificial neural networks Databases Speech processing Training Speech emotion recognition prosodic phrase nonverbal vocalization convolutional neural network long-short term memory sequence-to-sequence model

来源：评论

学校读者我要写书评

暂无评论

Automated Generation of Chinese Lyrics Based on Melody Emotions

引用

IEEE ACCESS 2021年 9卷 98060-98071页

作者： Huang, Yin-Fu You, Kai-Cheng Natl Yunlin Univ Sci & Technol Dept Comp Sci & Informat Engn Touliu 64002 Yunlin Taiwan

Natural Language Processing enables computers to understand human natural languages and assists humans to perform many tasks. Recently, language research was gradually moving towards the field of natural language generation, and lyrics generation is one of these research directions. In the past, many studies have explored how to generate lyrics using seeds or prompt words. But, they usually ignored the relationship between music and lyrics and even melody emotions when generating lyrics. In this paper, we propose the Chinese lyrics generation system based on notes and melody emotions. In the first phase, a support vector regression model is trained to detect melody emotions. In the second phase, a sequence-to-sequence model is trained to generate lyrics using notes, melody emotions, and lyrics. Finally, we conduct the experiments and evaluate the detected melody emotions and the generated lyrics. Results show that the generated lyrics have fluent sentences, and the sentences are with connected semantics.

关键词： Feature extraction Training Rhythm Natural language processing Timbre Semantics Harmonic analysis Lyric generation melody emotion detection natural language processing sequence-to-sequence model

来源：评论

学校读者我要写书评

暂无评论

Towards improving coherence and diversity of slogan generation

引用

NATURAL LANGUAGE ENGINEERING 2023年第2期29卷 254-286页

作者： Jin, Yiping Bhatia, Akshay Wanvarie, Dittaya Le, Phu T. V. Chulalongkorn Univ Dept Math & Comp Sci Fac Sci Bangkok 10300 Thailand Knorex 14-16 Crown Robinson Singapore 068907 Singapore

Previous work in slogan generation focused on utilising slogan skeletons mined from existing slogans. While some generated slogans can be catchy, they are often not coherent with the company's focus or style across their marketing communications because the skeletons are mined from other companies' slogans. We propose a sequence-to-sequence (seq2seq) Transformer model to generate slogans from a brief company description. A naive seq2seq model fine-tuned for slogan generation is prone to introducing false information. We use company name delexicalisation and entity masking to alleviate this problem and improve the generated slogans' quality and truthfulness. Furthermore, we apply conditional training based on the first words' part-of-speech tag to generate syntactically diverse slogans. Our best model achieved a ROUGE-1/-2/-L F-1 score of 35.58/18.47/33.32. Besides, automatic and human evaluations indicate that our method generates significantly more factual, diverse and catchy slogans than strong long short-term memory and Transformer seq2seq baselines.

关键词： Natural language generation sequence-to-sequence model Slogan generation

来源：评论

学校读者我要写书评

暂无评论

A Non-Autoregressive Neural Machine Translation model With Iterative Length Update of Target Sentence

引用

IEEE ACCESS 2022年 10卷 43341-43350页

作者： Lim, Yeon-Soo Park, Eun-Ju Song, Hyun-Je Park, Seong-Bae Kyung Hee Univ Dept Comp Sci & Engn Yonin Si 17104 Gyeonggi Do South Korea Jeonbuk Natl Univ Dept Informat & Engn Jeonju Si 54896 Jeollabuk Do South Korea

The non-autoregressive decoders in neural machine translation are paid increasing attention due to their faster decoding than autoregressive decoders. However, their apparent problem is a low performance which is mainly originated from wrong prediction about the target sentence length. To attack this problem, this paper proposes a novel machine translation model with a new non-autoregressive decoder named Iterative and Length-Adjustive Non-Autoregressive Decoder (ILAND). This decoder adopts a masked language model to avoid generation of low-confident tokens and changes the length of a target sentence iteratively to an optimal length. To complete these goals, ILAND consists of three complementary sub-modules of a token masker, a length adjuster, and a token generator. The token masker and the token generator take charge of the masked language model, and the length adjuster optimizes the target sentence length. The sequence-to-sequence training of the translation model is also introduced. In this training, the length adjuster and the token generator are jointly trained since they share a similar structure. The effectiveness of the translation model is proven by showing empirically that the model outperforms other models with various non-autoregressive decoders. The thorough analysis suggests that the performance gain of the translation model comes from target sentence length adaptation and the joint learning. In addition, ILAND is also shown to be faster than other iterative non-autoregressive decoders while it is still robust against the multi-modality problem.

关键词： Decoding Iterative decoding Generators Adaptation models Machine translation Predictive models Transformers Machine translation non-autoregressive decoder sequence-to-sequence model target length adaptation transformer

来源：评论

学校读者我要写书评

暂无评论

Neural Conversation Generation with Auxiliary Emotional Supervised models

引用

ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2020年第2期19卷 1–17页

作者： Zhou, Guangyou Fang, Yizhen Peng, Yehong Lu, Jiaheng Cent China Normal Univ Sch Comp Wuhan 430079 Peoples R China Univ Helsinki Dept Comp Sci FI-00014 Helsinki Finland

An important aspect of developing dialogue agents involves endowing a conversation system with emotion perception and interaction. Most existing emotion dialogue models lack the adaptability and extensibility of different scenes because of their limitation to require a specified emotion category or their reliance on a fixed emotional dictionary. To overcome these limitations, we propose a neural conversation generation with auxiliary emotional supervised model (nCG-FSM) comprising a sequence-to-sequence (Seq2Seq) generation model and an emotional classifier used as an auxiliary model. The emotional classifier was trained to predict the emotion distributions of the dialogues, which were then used as emotion supervised signals to guide the generation model to generate diverse emotional responses. The proposed nCG-ESM is flexible enough to generate responses with emotional diversity, including specified or unspecified emotions, which can be adapted and extended to different scenarios. We conducted extensive experiments on the popular dataset of Weibo post-response pairs. Experimental results showed that the proposed model was capable of producing more diverse, appropriate, and emotionally rich responses, yielding substantial gains in diversity scores and human evaluations.

关键词： Neural conversation sequence-to-sequence model natural language processing

来源：评论

学校读者我要写书评

暂无评论

Improving unified named entity recognition by incorporating mention relevance

引用

NEURAL COMPUTING & APPLICATIONS 2023年第30期35卷 22223-22234页

作者： Ji, Lijun Yan, Danfeng Cheng, Zhuoran Song, Yan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Shanghai Int Studies Univ Sch Business & Management Shanghai 200092 Peoples R China

Named entity recognition (NER) is a fundamental task for natural language processing, which aims to detect mentions of real-world entities from text and classifying them into predefined types. Recently, research on overlapped and discontinuous named entity recognition has received increasing attention. However, we note that few studies have considered both overlapped and discontinuous entities. In this paper, we proposed a novel sequence-to-sequence model that is capable of recognizing both overlapped and discontinuous entities based on machine reading comprehension. The model utilizes machine reading comprehension formulation to encode significant inferior information about the entity category. Then input sequence passes through a question-answering model to predict the mention relevance of the given source sentences to the query. Finally, we incorporate the mention relevance into the BART-based generation model. We conducted experiments on three type of NER datasets to show the generality of our model. The experimental results demonstrate that our model beats almost all the current top-performing baselines achieves a vast amount of performance boost over current SOTA models on overlapped and discontinuous NER datasets.

关键词： Named entity recognition sequence-to-sequence model Machine reading comprehension Mention relevance attention

来源：评论

学校读者我要写书评

暂无评论

A response generator with response-aware encoder for generating specific and relevant responses

引用

SOFT COMPUTING 2023年第7期27卷 3721-3732页

作者： Kim, So-Eon Song, Hyun-Je Park, Seong-Bae Kyung Hee Univ Dept Comp Sci & Engn 1732 Deogyeong Yongin 17104 Gyeonggi South Korea Jeonbuk Natl Univ Dept Informat Technol 567 Baekje Jeonju 54896 Jeollabuk South Korea

The dialogue data usually consist of the pairs of a query and its response, but no previous response generators have exploited the responses explicitly in their training while a response provides significant information about the meaning of a query. Therefore, this paper proposes a sequence-to-sequence response generator with a response-aware encoder. The proposed generator exploits golden responses by reflecting them into query representation. For this purpose, the response-aware encoder adds a relevancy scorer layer to the transformer encoder that calculates the relevancy of query tokens to a response. However, golden responses are available only during training of the response generator and unavailable at inference time. As a solution to this problem, the joint learning of a teacher and a student relevancy scorer is adopted. That is, at the training time, both the teacher and the student relevancy scorers are optimized but the decoder generates a response using only the relevancy of the teacher scorer. However, at the inference time, the decoder uses that of the student scorer. Since the student scorer is trained to minimize the difference from the teacher scorer, it can be used to compute the relevancy of a prospective response. The proposed model is the first attempt to use a golden response directly for generating a query representation, whereas previous studies used the responses for its implicit and indirect reflection. As a result, it achieved higher dialogue evaluation score than the current state-of-the-art model for Reddit, Persona-Chat, and DailyDialog data sets.

关键词： Response generator sequence-to-sequence model Response-aware Transformer architecture Natural language processing

来源：评论

学校读者我要写书评

暂无评论

URS: An Unsupervised Radargram Segmentation Network Based on Self-Supervised ViT With Contrastive Feature Learning Framework

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 15512-15524页

作者： Ghosh, Raktim Bovolo, Francesca Fdn Bruno Kessler Ctr Digital Soc I-38123 Trento Italy Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy

Radar sounders are air and space-borne nadir-looking sensors operating in high-frequency (HF) or very high-frequency (VHF) bands and collect subsurface backscattered returns by transmitting electromagnetic pulses. The backscatter echoes are coherently integrated to generate radargrams for investigating and identifying geophysical characteristics of subsurface targets. While recent efforts have been made to develop supervised or semisupervised deep learning models for segmenting radargrams, obtaining accurate labeled information is often a challenging task. Therefore, it is of paramount importance to develop automatic unsupervised semantic segmentation methods to characterize the subsurface targets without labeled information. Unsupervised segmentation methods learn to discover meaningful semantic contents and decompose them into distinct semantic segments with known ontology. Here, we propose an unsupervised radargram segmentation network that uses a convolution-based expansive network as a proxy decoder and a progressive stepwise reconstruction strategy of the input signal from the latent space to measure the spatial similarity with the input radar sounder signal. After designing a unique training strategy by bootstrapping the randomness inside the minibatch and combining the spatial similarity loss along with the contrastive correlation loss, the proposed architecture outperformed the state of the art in fully unsupervised settings. Experiments were conducted on the multichannel coherent radar depth sounder to test the robustness of the proposed method. We carried out a comparative analysis with the state-of-the-art unsupervised and supervised segmentation methods. MIoU is improved by 23.47%.

关键词： Ice Training Sensors Radar Task analysis Noise Semantics Multichannel coherent radar depth sounder (MCoRDS) radar sounder semantic segmentation sequence-to-sequence model TransFuse TransUNet transformers

来源：评论

学校读者我要写书评

暂无评论

Emotional dialog generation via multiple classifiers based on a generative adversarial network

引用

Virtual Reality & Intelligent Hardware 2021年第1期3卷 18-32页

作者： Wei CHEN Xinmiao CHEN Xiao SUN School of Computer and Information Hefei University of TechnologyHefei 230601China

Background Human-machine dialog generation is an essential topic of research in the field of natural language *** high-quality,diverse,fluent,and emotional conversation is a challenging *** on continuing advancements in artificial intelligence and deep learning,new methods have come to the forefront in recent *** particular,the end-to-end neural network model provides an extensible conversation generation framework that has the potential to enable machines to understand semantics and automatically generate ***,neural network models come with their own set of questions and *** basic conversational model framework tends to produce universal,meaningless,and relatively"safe"*** Based on generative adversarial networks(GANs),a new emotional dialog generation framework called EMC-GAN is proposed in this study to address the task of emotional dialog *** proposed model comprises a generative and three discriminative *** generator is based on the basic sequence-to-sequence(Seq2Seq)dialog generation model,and the aggregate discriminative model for the overall framework consists of a basic discriminative model,an emotion discriminative model,and a fluency discriminative *** basic discriminative model distinguishes generated fake sentences from real sentences in the training *** emotion discriminative model evaluates whether the emotion conveyed via the generated dialog agrees with a pre-specified emotion,and directs the generative model to generate dialogs that correspond to the category of the pre-specified ***,the fluency discriminative model assigns a score to the fluency of the generated dialog and guides the generator to produce more fluent *** Based on the experimental results,this study confirms the superiority of the proposed model over similar existing models with respect to emotional accuracy,fluency,and *** The proposed EMC-

关键词： Emotional dialog generation sequence-to-sequence model Emotion classification Generative adversarial networks Multiple classifiers

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：