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1
Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model ...
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2
Detection of the Prodromal Phase of Bipolar Disorder from Psychological and Phonological Aspects in Social Media ...
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3
AP17-OLR Challenge: Data, Plan, and Baseline ...
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4
Additional file 1: of New insights on serodiagnosis of trichinellosis during window period: early diagnostic antigens from Trichinella spiralis intestinal worms ...
Wang, Zhong-Quan; Shi, Ya-Li; Rou-Dan Liu. - : Figshare, 2017
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Additional file 1: of New insights on serodiagnosis of trichinellosis during window period: early diagnostic antigens from Trichinella spiralis intestinal worms ...
Wang, Zhong-Quan; Shi, Ya-Li; Rou-Dan Liu. - : Figshare, 2017
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6
Video Captioning with Guidance of Multimodal Latent Topics ...
Chen, Shizhe; Chen, Jia; Jin, Qin. - : arXiv, 2017
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7
Understanding the Changing Roles of Scientific Publications via Citation Embeddings ...
He, Jiangen; Chen, Chaomei. - : arXiv, 2017
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8
Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning ...
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9
Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation ...
Zhou, Ganbin; Luo, Ping; Cao, Rongyu. - : arXiv, 2017
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10
A Scalable and Adaptive Method for Finding Semantically Equivalent Cue Words of Uncertainty ...
Chen, Chaomei; Song, Ming; Heo, Go Eun. - : arXiv, 2017
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11
Discover and Learn New Objects from Documentaries ...
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12
A Semantic QA-Based Approach for Text Summarization Evaluation ...
Chen, Ping; Wu, Fei; Wang, Tong. - : arXiv, 2017
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13
Adversarial Multi-Criteria Learning for Chinese Word Segmentation ...
Chen, Xinchi; Shi, Zhan; Qiu, Xipeng. - : arXiv, 2017
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14
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions ...
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15
A Sequential Matching Framework for Multi-turn Response Selection in Retrieval-based Chatbots ...
Abstract: We study the problem of response selection for multi-turn conversation in retrieval-based chatbots. The task requires matching a response candidate with a conversation context, whose challenges include how to recognize important parts of the context, and how to model the relationships among utterances in the context. Existing matching methods may lose important information in contexts as we can interpret them with a unified framework in which contexts are transformed to fixed-length vectors without any interaction with responses before matching. The analysis motivates us to propose a new matching framework that can sufficiently carry the important information in contexts to matching and model the relationships among utterances at the same time. The new framework, which we call a sequential matching framework (SMF), lets each utterance in a context interacts with a response candidate at the first step and transforms the pair to a matching vector. The matching vectors are then accumulated following the order ... : Submitted to Computational Linguistics ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1710.11344
https://dx.doi.org/10.48550/arxiv.1710.11344
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16
Phonetic Temporal Neural Model for Language Identification ...
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17
Phone-aware Neural Language Identification ...
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18
End-to-End Attention based Text-Dependent Speaker Verification ...
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19
Weakly Supervised Dense Video Captioning ...
Shen, Zhiqiang; Li, Jianguo; Su, Zhou. - : arXiv, 2017
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20
No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models ...
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