File size: 51,163 Bytes
6fa4bc9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 |
{
"paper_id": "2021",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T02:09:40.981684Z"
},
"title": "System Description on Automatic Simultaneous Translation Workshop",
"authors": [
{
"first": "Linjie",
"middle": [],
"last": "Chen",
"suffix": "",
"affiliation": {},
"email": "chenlinjie887@pingan.com.cn"
},
{
"first": "Jianzong",
"middle": [],
"last": "Wang",
"suffix": "",
"affiliation": {},
"email": "jzwang@188.com"
},
{
"first": "Zhangcheng",
"middle": [],
"last": "Huang",
"suffix": "",
"affiliation": {},
"email": "huangzhangcheng624@pingan.com.cn"
},
{
"first": "Xiongbin",
"middle": [],
"last": "Ding",
"suffix": "",
"affiliation": {},
"email": "dingxiongbin106@pingan.com.cn"
},
{
"first": "Jing",
"middle": [],
"last": "Xiao",
"suffix": "",
"affiliation": {},
"email": "xiaojing661@pingan.com.cn"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "This paper shows our submission on the second automatic simultaneous translation workshop at NAACL2021. We participate in all the two directions of Chinese-to-English translation, Chinese audio\u2192English text and Chinese text\u2192English text. We do data filtering and model training techniques to get the best BLEU score and reduce the average lagging. We propose a two-stage simultaneous translation pipeline system which is composed of Quartznet and BPE-based transformer. We propose a competitive simultaneous translation system and achieves a BLEU score of 24.39 in the audio input track.",
"pdf_parse": {
"paper_id": "2021",
"_pdf_hash": "",
"abstract": [
{
"text": "This paper shows our submission on the second automatic simultaneous translation workshop at NAACL2021. We participate in all the two directions of Chinese-to-English translation, Chinese audio\u2192English text and Chinese text\u2192English text. We do data filtering and model training techniques to get the best BLEU score and reduce the average lagging. We propose a two-stage simultaneous translation pipeline system which is composed of Quartznet and BPE-based transformer. We propose a competitive simultaneous translation system and achieves a BLEU score of 24.39 in the audio input track.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Our submitted system consists of an end to end speech recognition model and a neural machine translation model which follows the traditional pipeline framework in simultaneous translation task. The system input is Chinese audio file and the output is English translation text. A temporary Streaming transcription is obtained by speech recognition model and transmitted into machine translation model to get the target system output.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "For automatic speech recognition(ASR) model, we use the QuartzNet model (Kriman et al., 2019) of Nvidia Jarvis. At the moment, we expand the train data set by adding Aishell-1 and data that collected, then using plenty of rules to filter audio data and deal with parallel transcription. Compared to the Jasper model (Li et al., 2019) , it can reduce number of parameters quickly by using separable 1D convolutions including time channel.",
"cite_spans": [
{
"start": 72,
"end": 93,
"text": "(Kriman et al., 2019)",
"ref_id": "BIBREF5"
},
{
"start": 316,
"end": 333,
"text": "(Li et al., 2019)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Our neural machine translation model is Transformer (Vaswani et al., 2017) . We use some human rules and the pre-trained language model to filter the parallel corpus. The method of back translation (Sennrich et al., 2016) is also applied to generate synthetic Chinese sentences.",
"cite_spans": [
{
"start": 52,
"end": 74,
"text": "(Vaswani et al., 2017)",
"ref_id": "BIBREF13"
},
{
"start": 198,
"end": 221,
"text": "(Sennrich et al., 2016)",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "At the step of inference, we apply the wait-k words method (Ma et al., 2018) . Both the preprocessing and post-processing are applied to improve the terminology translation and deal with the word error produced by the ASR system.",
"cite_spans": [
{
"start": 59,
"end": 76,
"text": "(Ma et al., 2018)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Since our submission is a two-stage system, the rest of this paper describes separately regards to the Automatic speech recognition(ASR and Machine translation(MT) sub-modules. We firstly describe the training and development datasets we used, then the data filtering methods we applied is introduced. Secondly, the system architecture is discussed and it is verified by the experiments. Lastly, we draw a conclusion of our system by analyzing the experiments.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "For audio data of ASR, we use qianyan audio datasets provided by NAACL workshop (Zhang et al., 2021) , Aishell-1 (Hui Bu, 2017) and lip sentences we collect by smartphone(16kHz, 16-bit).",
"cite_spans": [
{
"start": 80,
"end": 100,
"text": "(Zhang et al., 2021)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "2"
},
{
"text": "We invite 20 volunteers in data collection. Each volunteer performed two hours of Mandarin Chinese audio about 1000 sentences in the quiet room. To keep data diversity, different domains of audios were collected, including artificial intelligent, industrial production, business conversation and medical. Finally, we get a total of 19800 sentences (audio and transcription) in this way.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Audio Data",
"sec_num": "2.1"
},
{
"text": "For qianyan audio datasets, we split each audio into sentences according to the sentence-level transcription. After processing, the blank part of all entire audio files was removed, and duration time of audios was reduced from the original 68 hours to about 52 hours.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Audio Data",
"sec_num": "2.1"
},
{
"text": "For AIshell-1 datasets, we firstly deal with transcription files by using rules to get path and filename of every transcription. Then using wave library to read audio files to get the duration time of each audio.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Audio Data",
"sec_num": "2.1"
},
{
"text": "Noting that the audio data and the transcription may not exactly match. In order to improve the accuracy of the data, we use a pre-trained ASR model to transcript audio data to produce text result. Then using similarity matching algorithm to filter audio and original transcription data of lower similarity. Table1 shows the number of train data after filtering. The corpus we use to build our machine translation system is CWMT 19 corpus 1 . It includes both the bilingual and monolingual data. For the bilingual data, we apply data filtering techniques. The main process is described as follows. Firstly, we set the punctuation ratio and sentence length ratio of the sentence pairs to abandon the sentences higher than the ratio. Secondly, we calculate the cross entropy of each English sentence by a pre-trained language model and removed the sentence pair exceed the threshold. Thirdly, we construct a terminology table using the methods of name entity resolution and word alignment. The terminology such as companies, organizations and human names are replaced with specific words.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Audio Data",
"sec_num": "2.1"
},
{
"text": "For the monolingual data, we follow the method proposed by (Sennrich et al., 2016) . We firstly train an English to Chinese machine translation model. Then the monolingual English sentences are translated to generate synthetic Chinese translation. All the synthetic parallel data are filtered with the same strategies applied in bilingual data.",
"cite_spans": [
{
"start": 59,
"end": 82,
"text": "(Sennrich et al., 2016)",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Audio Data",
"sec_num": "2.1"
},
{
"text": "After the filtering process, we normalize the punctuation for both Chinese and English sentences. We apply Chinese word segmentation using LAC toolkit 2 (Jiao et al., 2018) for Chinese sentences. For the English sentences, we apply the Tokenizer and Truecaser toolkit provided by Moses scripts (Koehn et al., 2007) . Finally, we train a bytes pairs encoding model and applied it for both Chinese and English sentences.",
"cite_spans": [
{
"start": 153,
"end": 172,
"text": "(Jiao et al., 2018)",
"ref_id": "BIBREF1"
},
{
"start": 294,
"end": 314,
"text": "(Koehn et al., 2007)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Audio Data",
"sec_num": "2.1"
},
{
"text": "The model training process for both the speech recognition and machine translation model are implemented on a device with eight GPUs of Nvidia TESLA V100.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "System description",
"sec_num": "3"
},
{
"text": "The QuartzNet15x5 model is as our based model on ASR, we also use Memory-Self-Attention(MSA) (Luo et al., 2021 ) modules in the model structure of CTC and RNN-T.",
"cite_spans": [
{
"start": 93,
"end": 110,
"text": "(Luo et al., 2021",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Automatic speech recognition",
"sec_num": "3.1"
},
{
"text": "After data pre-processing, we use the file of json structure to train quartznet 15x5 model. We list the model configuration and train parameters in Table2. When the model was trained, the size of each sample audio should be controlled to less than 16.7 s. To do this, it can improve the accuracy of model and accelerate the training speed. The ASR model was trained over three days and reached to the best WER. After the loss value converged, we use the last saved model to try to transform test datasets and get average score. We use WER-BEAT (Sheshadri et al., 2021) to evaluate our model. And we get closed to 1.0 WER. To increase the accuracy of model recognition, we use MSA modules in the model structure of CTC and RNN-T. The operation complexity of the model maintains a linear relationship with the length of the input speech, which greatly improves the efficiency of the model, and there will be no serious decline in efficiency as the input increases.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training Scheme",
"sec_num": "3.1.1"
},
{
"text": "Before we use the model, in order to improve the accuracy of recognition, we need to process the input voice file.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Model Usage",
"sec_num": "3.1.2"
},
{
"text": "In the end, we only submit one point in the competition. This point is to directly use the previously segmented audio transcription text as the input of the translation model, thereby obtaining a more accurate English text output.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Model Usage",
"sec_num": "3.1.2"
},
{
"text": "We use Transformer as our based model on machine translation, the attention mechanism is strength-able at capturing the Semantic relationship on a sentence. The development toolkit we used in machine translation is Marian (Junczys-Dowmunt et al., 2018) .",
"cite_spans": [
{
"start": 222,
"end": 252,
"text": "(Junczys-Dowmunt et al., 2018)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Machine translation",
"sec_num": "3.2"
},
{
"text": "After completing the data preprocessing on both the bilingual data and back-translated data, we train our baseline model by evaluating BLEU. The language tool for evaluation is uncased 4-gram BLEU (Papineni et al., 2002) . We list the model configuration in Table3 and training parameters in Table 4 . We train the model for over three days, the BLEU score increased rapidly at the beginning and the growth slowed after 30 hours. After the loss converged, we collect the last 20 checkpoints of the model in the time interval of one hour and applied checkpoint average to get the final model. We implement fine-tuning on the Transformer model using the development set of qianyan audio datasets (956 sentence pairs) to improve the translation quality on simultaneous translation task. Since fine-tuning is effective to build a domain-adaptive model.",
"cite_spans": [
{
"start": 197,
"end": 220,
"text": "(Papineni et al., 2002)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [
{
"start": 292,
"end": 300,
"text": "Table 4",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "Training Scheme",
"sec_num": "3.2.1"
},
{
"text": "This paper describes our submission to the second automatic simultaneous translation workshop at NAACL2021. We detail our process of data filtering and model training. The Consecutive Wait(CW) (Klein et al., 2017) of the best point reached to 18.4 while we get the BLEU value of 24.39 in the audio input track. In future work, we will continue to research on end-to-end speech translation model from Chinese speech input to English text output.",
"cite_spans": [
{
"start": 193,
"end": 213,
"text": "(Klein et al., 2017)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "4"
},
{
"text": "http://mteval.cipsc.org.cn:81/agreement/AutoSimTrans 2 https://github.com/baidu/lac",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Aishell-1: An open-source mandarin speech corpus and a speech recognition baseline",
"authors": [
{
"first": "Xingyu Na Bengu Wu Hao Zheng Hui",
"middle": [],
"last": "Bu",
"suffix": ""
},
{
"first": "Jiayu",
"middle": [],
"last": "Du",
"suffix": ""
}
],
"year": 2017,
"venue": "Oriental COCOSDA 2017",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xingyu Na Bengu Wu Hao Zheng Hui Bu, Jiayu Du. 2017. Aishell-1: An open-source mandarin speech corpus and a speech recognition baseline. In Orien- tal COCOSDA 2017, page Submitted.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Chinese lexical analysis with deep bi-gru-crf network",
"authors": [
{
"first": "Zhenyu",
"middle": [],
"last": "Jiao",
"suffix": ""
},
{
"first": "Shuqi",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "Ke",
"middle": [],
"last": "Sun",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1807.01882"
]
},
"num": null,
"urls": [],
"raw_text": "Zhenyu Jiao, Shuqi Sun, and Ke Sun. 2018. Chinese lexical analysis with deep bi-gru-crf network. arXiv preprint arXiv:1807.01882.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Marian: Fast neural machine translation in C++",
"authors": [
{
"first": "Marcin",
"middle": [],
"last": "Junczys-Dowmunt",
"suffix": ""
},
{
"first": "Roman",
"middle": [],
"last": "Grundkiewicz",
"suffix": ""
},
{
"first": "Tomasz",
"middle": [],
"last": "Dwojak",
"suffix": ""
},
{
"first": "Hieu",
"middle": [],
"last": "Hoang",
"suffix": ""
},
{
"first": "Kenneth",
"middle": [],
"last": "Heafield",
"suffix": ""
},
{
"first": "Tom",
"middle": [],
"last": "Neckermann",
"suffix": ""
},
{
"first": "Frank",
"middle": [],
"last": "Seide",
"suffix": ""
},
{
"first": "Ulrich",
"middle": [],
"last": "Germann",
"suffix": ""
},
{
"first": "Alham",
"middle": [],
"last": "Fikri Aji",
"suffix": ""
},
{
"first": "Nikolay",
"middle": [],
"last": "Bogoychev",
"suffix": ""
},
{
"first": "F",
"middle": [
"T"
],
"last": "Andr\u00e9",
"suffix": ""
}
],
"year": 2018,
"venue": "Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "116--121",
"other_ids": {
"DOI": [
"10.18653/v1/P18-4020"
]
},
"num": null,
"urls": [],
"raw_text": "Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Al- ham Fikri Aji, Nikolay Bogoychev, Andr\u00e9 F. T. Mar- tins, and Alexandra Birch. 2018. Marian: Fast neu- ral machine translation in C++. In Proceedings of ACL 2018, Melbourne, Australia, July 15-20, 2018, System Demonstrations, pages 116-121. Associa- tion for Computational Linguistics.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Opennmt: Open-source toolkit for neural machine translation",
"authors": [
{
"first": "Guillaume",
"middle": [],
"last": "Klein",
"suffix": ""
},
{
"first": "Yoon",
"middle": [],
"last": "Kim",
"suffix": ""
},
{
"first": "Yuntian",
"middle": [],
"last": "Deng",
"suffix": ""
},
{
"first": "Jean",
"middle": [],
"last": "Senellart",
"suffix": ""
},
{
"first": "Alexander",
"middle": [
"M"
],
"last": "Rush",
"suffix": ""
}
],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senel- lart, and Alexander M. Rush. 2017. Opennmt: Open-source toolkit for neural machine translation.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Moses: Open source toolkit for statistical machine translation",
"authors": [
{
"first": "Philipp",
"middle": [],
"last": "Koehn",
"suffix": ""
},
{
"first": "Hieu",
"middle": [],
"last": "Hoang",
"suffix": ""
},
{
"first": "Alexandra",
"middle": [],
"last": "Birch",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Callison-Burch",
"suffix": ""
},
{
"first": "Marcello",
"middle": [],
"last": "Federico",
"suffix": ""
},
{
"first": "Nicola",
"middle": [],
"last": "Bertoldi",
"suffix": ""
},
{
"first": "Brooke",
"middle": [],
"last": "Cowan",
"suffix": ""
},
{
"first": "Wade",
"middle": [],
"last": "Shen",
"suffix": ""
},
{
"first": "Christine",
"middle": [],
"last": "Moran",
"suffix": ""
},
{
"first": "Richard",
"middle": [],
"last": "Zens",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Dyer",
"suffix": ""
},
{
"first": "Ondrej",
"middle": [],
"last": "Bojar",
"suffix": ""
},
{
"first": "Alexandra",
"middle": [],
"last": "Constantin",
"suffix": ""
},
{
"first": "Evan",
"middle": [],
"last": "Herbst",
"suffix": ""
}
],
"year": 2007,
"venue": "ACL 2007, Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Constantin, and Evan Herbst. 2007. Moses: Open source toolkit for statistical machine translation. In ACL 2007, Proceedings of the 45th Annual Meet- ing of the Association for Computational Linguistics, June 23-30, 2007, Prague, Czech Republic. The As- sociation for Computational Linguistics.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Quartznet: Deep automatic speech recognition with 1d time-channel separable convolutions",
"authors": [
{
"first": "Stanislav",
"middle": [],
"last": "Samuel Kriman",
"suffix": ""
},
{
"first": "Boris",
"middle": [],
"last": "Beliaev",
"suffix": ""
},
{
"first": "Jocelyn",
"middle": [],
"last": "Ginsburg",
"suffix": ""
},
{
"first": "Oleksii",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "Vitaly",
"middle": [],
"last": "Kuchaiev",
"suffix": ""
},
{
"first": "Ryan",
"middle": [],
"last": "Lavrukhin",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Leary",
"suffix": ""
},
{
"first": "Yang",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Zhang",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Samuel Kriman, Stanislav Beliaev, Boris Ginsburg, Jo- celyn Huang, Oleksii Kuchaiev, Vitaly Lavrukhin, Ryan Leary, Jason Li, and Yang Zhang. 2019. Quartznet: Deep automatic speech recognition with 1d time-channel separable convolutions.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Jasper: An end-to-end convolutional neural acoustic model",
"authors": [
{
"first": "Ravi Teja",
"middle": [],
"last": "Nguyen",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Gadde",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Nguyen, and Ravi Teja Gadde. 2019. Jasper: An end-to-end convolutional neural acoustic model.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Unidirectional memory-self-attention transducer for online speech recognition",
"authors": [
{
"first": "Jian",
"middle": [],
"last": "Luo",
"suffix": ""
},
{
"first": "Jianzong",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Ning",
"middle": [],
"last": "Cheng",
"suffix": ""
},
{
"first": "Jing",
"middle": [],
"last": "Xiao",
"suffix": ""
}
],
"year": 2021,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jian Luo, Jianzong Wang, Ning Cheng, and Jing Xiao. 2021. Unidirectional memory-self-attention trans- ducer for online speech recognition.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "STACL: simultaneous translation with integrated anticipation and controllable latency",
"authors": [
{
"first": "Mingbo",
"middle": [],
"last": "Ma",
"suffix": ""
},
{
"first": "Liang",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "Hao",
"middle": [],
"last": "Xiong",
"suffix": ""
},
{
"first": "Kaibo",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Chuanqiang",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Zhongjun",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Hairong",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Xing",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Haifeng",
"middle": [],
"last": "Wang",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mingbo Ma, Liang Huang, Hao Xiong, Kaibo Liu, Chuanqiang Zhang, Zhongjun He, Hairong Liu, Xing Li, and Haifeng Wang. 2018. STACL: simul- taneous translation with integrated anticipation and controllable latency. CoRR, abs/1810.08398.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Bleu: a method for automatic evaluation of machine translation",
"authors": [
{
"first": "Kishore",
"middle": [],
"last": "Papineni",
"suffix": ""
},
{
"first": "Salim",
"middle": [],
"last": "Roukos",
"suffix": ""
},
{
"first": "Todd",
"middle": [],
"last": "Ward",
"suffix": ""
},
{
"first": "Wei-Jing",
"middle": [],
"last": "Zhu",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "311--318",
"other_ids": {
"DOI": [
"10.3115/1073083.1073135"
]
},
"num": null,
"urls": [],
"raw_text": "Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. 2002. Bleu: a method for automatic eval- uation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Compu- tational Linguistics, July 6-12, 2002, Philadelphia, PA, USA, pages 311-318. ACL.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Improving neural machine translation models with monolingual data",
"authors": [
{
"first": "Rico",
"middle": [],
"last": "Sennrich",
"suffix": ""
},
{
"first": "Barry",
"middle": [],
"last": "Haddow",
"suffix": ""
},
{
"first": "Alexandra",
"middle": [],
"last": "Birch",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016",
"volume": "1",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.18653/v1/p16-1009"
]
},
"num": null,
"urls": [],
"raw_text": "Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Improving neural machine translation mod- els with monolingual data. In Proceedings of the 54th Annual Meeting of the Association for Compu- tational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers. The Asso- ciation for Computer Linguistics.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Wer-bert: Automatic wer estimation with bert in a balanced ordinal classification paradigm",
"authors": [
{
"first": "Krishna",
"middle": [],
"last": "Akshay",
"suffix": ""
},
{
"first": "Anvesh",
"middle": [
"Rao"
],
"last": "Sheshadri",
"suffix": ""
},
{
"first": "Sukhdeep",
"middle": [],
"last": "Vijjini",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Kharbanda",
"suffix": ""
}
],
"year": 2021,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Akshay Krishna Sheshadri, Anvesh Rao Vijjini, and Sukhdeep Kharbanda. 2021. Wer-bert: Automatic wer estimation with bert in a balanced ordinal classi- fication paradigm.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Attention is all you need",
"authors": [
{
"first": "Ashish",
"middle": [],
"last": "Vaswani",
"suffix": ""
},
{
"first": "Noam",
"middle": [],
"last": "Shazeer",
"suffix": ""
},
{
"first": "Niki",
"middle": [],
"last": "Parmar",
"suffix": ""
},
{
"first": "Jakob",
"middle": [],
"last": "Uszkoreit",
"suffix": ""
},
{
"first": "Llion",
"middle": [],
"last": "Jones",
"suffix": ""
},
{
"first": "Aidan",
"middle": [
"N"
],
"last": "Gomez",
"suffix": ""
},
{
"first": "Lukasz",
"middle": [],
"last": "Kaiser",
"suffix": ""
},
{
"first": "Illia",
"middle": [],
"last": "Polosukhin",
"suffix": ""
}
],
"year": 2017,
"venue": "Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems",
"volume": "",
"issue": "",
"pages": "5998--6008",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Pro- cessing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4- 9, 2017, Long Beach, CA, USA, pages 5998-6008.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Bstc: A large-scale chinese-english speech translation dataset",
"authors": [
{
"first": "Ruiqing",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Xiyang",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Chuanqiang",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Zhongjun",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Hua",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Zhi",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Haifeng",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Ying",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Qinfei",
"middle": [],
"last": "Li",
"suffix": ""
}
],
"year": 2021,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:2104.03575"
]
},
"num": null,
"urls": [],
"raw_text": "Ruiqing Zhang, Xiyang Wang, Chuanqiang Zhang, Zhongjun He, Hua Wu, Zhi Li, Haifeng Wang, Ying Chen, and Qinfei Li. 2021. Bstc: A large-scale chinese-english speech translation dataset. arXiv preprint arXiv:2104.03575.",
"links": null
}
},
"ref_entries": {
"TABREF0": {
"text": "ZH-EN audio train datasets",
"content": "<table><tr><td>Data Source</td><td colspan=\"2\">Duration Total Samples</td></tr><tr><td colspan=\"2\">Qianyan(NAACL) 70hours</td><td>36,140</td></tr><tr><td>Aishell-1</td><td colspan=\"2\">178hours 120,098</td></tr><tr><td>Collection</td><td>40hours</td><td>19,800</td></tr><tr><td>2.2 Text Data</td><td/><td/></tr></table>",
"type_str": "table",
"num": null,
"html": null
},
"TABREF1": {
"text": "Model Configuration",
"content": "<table><tr><td>Configuration</td><td>Value</td></tr><tr><td>Sample rate</td><td>16,000</td></tr><tr><td>Repeat</td><td>5</td></tr><tr><td>n fft</td><td>512</td></tr><tr><td>activation</td><td>relu</td></tr><tr><td>Chinese Vocabulary size</td><td>5,270</td></tr><tr><td>Optimizer</td><td>Adam</td></tr><tr><td>residual</td><td>true</td></tr><tr><td>filters</td><td>256/512</td></tr><tr><td>batch size</td><td>64</td></tr></table>",
"type_str": "table",
"num": null,
"html": null
},
"TABREF2": {
"text": "Model Configuration",
"content": "<table><tr><td>Configuration</td><td>Value</td></tr><tr><td>Encoder/Decoder depth</td><td>6</td></tr><tr><td>Attention heads</td><td>16</td></tr><tr><td>Word Embedding</td><td>1024</td></tr><tr><td>FFN size</td><td>4096</td></tr><tr><td>Chinese Vocabulary size</td><td>50,000</td></tr><tr><td>English Vocabulary size</td><td>50,000</td></tr><tr><td>Optimizer</td><td>Adam</td></tr></table>",
"type_str": "table",
"num": null,
"html": null
},
"TABREF3": {
"text": "Training Parameters",
"content": "<table><tr><td>Parameter</td><td>Value</td></tr><tr><td>Label smoothing</td><td>0.1</td></tr><tr><td>Learning rate</td><td>16</td></tr><tr><td>Warmup rates</td><td>15,000</td></tr><tr><td>Maximum sentence length</td><td>120</td></tr><tr><td>Clip normalization</td><td>5</td></tr><tr><td>3.2.2 Fine-tuning</td><td/></tr></table>",
"type_str": "table",
"num": null,
"html": null
}
}
}
} |