参考文献
[1]Agarwal, A. & A. Lavie. 2008. Meteor, m-bleu and m-ter: evaluation metrics for high-correlation with human rankings of machine translation output [A]. Proceedings of the Third Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 115-118.
[2]Anderson, P., Fernando, B., Johnson, M., et al. 2016. Spice: Semantic propositional image caption evaluation [A]. European Conference on Computer Vision [C]. Basel: Springer International Publishing. 382-398.
[3]Ariel, M. 2001. Accessibility theory: An overview [J]. Text Representation: Linguistic and Psycholinguistic Aspects, 8: 29-87.
[4]Artstein, R. & M. Poesio. 2008. Inter-coder agreement for computational linguistics [J]. Computational Linguistics, 34(4): 555-596.
[5]Avramidis, E., Burchardt, A, Hunsicker S, et al. 2014. The taraX? corpus of human-annotated machine translations [A]. Language Resources and Evaluation Conference [C]. The European Language Resources Association. 2679-2682.
[6]Baker, M. 1992. A Coursebook on Translation [M]. London: Routledge.
[7]Banarescu, L., Bonial, C., Cai, S., et al. 2012. Abstract meaning representation(AMR) 1.0 specification. In parsing on freebase from question-answer Pairs [A]. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing [C]. Association for Computational Linguistics. 1533-1544.
[8]Banarescu, L., Bonial, C., Cai, S., et al. 2013. Abstract meaning representation for sembanking [A]. Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse [C]. Association for Computational Linguistics. 178-186.
[9]Banarescu, L., Bonial, C., Cai, S., et al. 2014. Abstract meaning representation(AMR) 1.2 specification. In parsing on freebase from question-answer pairs [A]. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing [C]. Association for Computational Linguistic. 1533-1544.
[10]Banerjee, S. & A. Lavie. 2005. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments [A]. Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization [C]. Association for Computational Linguistics. 65-72.
[11]Barzilay, R. & M. Elhadad. 1999. Using lexical chains for text summarization [A]. In I. Mani & M. T. Maybury (Eds.) Advances in Automatic Text Summarization[C]. Cambridge: The MIT Press. 111-121.
[12]Basak, D., Pal S. & D. C. Patranabis. 2007. Support vector regression [J]. Neural Information Processing Letters and Reviews, 11(10): 203-224.
[13]Bergsma, S. & D. Lin. 2006. Bootstrapping path-based pronoun resolution [A]. Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics [C]. Association for Computational Linguistic. 33-40.
[14]Bernardi, R., Cakici, R., Elliott, D., et al. 2016. Automatic description generation from images: A Survey of models, datasets, and evaluation measures [J]. J. Artif. Intell. Res. (JAIR), 55: 409-442.
[15]Bloodgood, M. & C. Callison-Burch. 2010. Using Mechanical Turk to build machine translation evaluation sets [A]. Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk [C]. Association for Computational Linguistics. 208-211.
[16]Bojar, O., Buck, C., Federmann, C., et al. 2014. Findings of the 2014 workshop on statistical machine translation [A]. Proceedings of the Ninth Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 12-58.
[17]Bojar, O., Chatterjee R., Federmann C., et al. 2015. Findings of the 2015 workshop on statistical machine translation [A]. Proceedings of the Tenth Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 1-46.
[18]Bojar, O., Chatterjee, R., Federmann, C., et al. 2016. Findings of the 2016 conference on machine translation (wmt16) [A]. Proceedings of Workshop on Machine Translation [C]. Association for Computational Linguistics. 131-198.
[19]Bojar, O., Rosa, R. & A. Tamchyna. 2013. Chimera–three heads for English to Czech translation [A]. Proceedings of the Eighth Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 92-98.
[20]Bos, J. 2016. Expressive power of abstract meaning representations [J]. Computational Linguistics, 42(3): 527-535.
[21]Bowker, L. & P. Bennison. 2003. Student translation archive: design, development and application [A]. In F. Zanettin, S. Bernardini & D. Stewart (Eds.) Corpora in Translator Education [C]. Manchester: St Jerome. 103-117.
[22]Cai, S. & K. Knight. 2013. Smatch: an evaluation metric for semantic feature structures [A]. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics [C]. Association for Computational Linguistics. 748-752.
[23]Callison-Burch, C. 2009. Fast, cheap, and creative: evaluating translation quality using Amazon's Mechanical Turk [A]. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing [C]. Association for Computational Linguistics. 286-295.
[24]Callison-Burch, C., Fordyce, C., Koehn, P., Monz, C. & J. Schroeder. 2007.(Meta-) evaluation of machine translation [A]. Proceedings of the Second Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 136-158.
[25]Callison-Burch, C., Fordyce, C., Koehn, P., et al. 2008. Further meta-evaluation of machine translation [A]. Proceedings of the Third Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 70-106.
[26]Callison-Burch, C., Koehn, P., Monz, C., et al. 2010. Findings of the 2010 joint workshop on statistical machine translation and metrics for machine translation[A]. Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and Metrics MATR [C]. Association for Computational Linguistics. 17-53.
[27]Callison-Burch, C., Koehn, P., Monz, C., et al. 2011. Findings of the 2011 workshop on statistical machine translation [A]. Proceedings of the Sixth Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 22-64.
[28]Callison-Burch, C., Koehn, P., Monz, C., et al. 2012. Findings of the 2012 workshop on statistical machine translation [A]. Proceedings of the Seventh Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 10-51.
[29]Carlson, L., Marcu, D. & M. Ellen. 2001. Building a discourse-tagged corpus in the framework of rhetorical structure theory [A]. Proceedings of Second SIGdial Workshop on Discourse and Dialogue [C]. Association for Computational Linguistics. W01-1605.
[30]Castagnoli, S., Ciobanu, D., Kunz, K., et al. 2011. Designing a learner translator corpus for training purposes [A]. Proceedings of TALC 2006 [C]. 221-248.
[31]Cettolo, M., Niehues, J., Stüker, S., et al. 2015. The IWSLT 2015 evaluation campaign [A]. Proceedings of IWSLT [C]. Da Nang, Vietnam.
[32]Chang, Y. C., Chang, J. S., Chen, H. J. & H. C. Liou. 2008. An automatic collocation writing assistant for Taiwanese EFL learners: A case of corpus-based NLP technology [J]. Computer Assisted Language Learning, 21(3): 283-299.
[33]Chang, B., Danielsson, P. & W. Teubert. 2005. Chinese-English translation database: Extracting units of translation from parallel texts [A]. In G. Barnbrook, et al. (Eds.), Meaningful Texts [C]. London: Continuum. 131-140.
[34]Charniak, E. 2001. Immediate-head parsing for language models [A]. Proceedings of the 39th Annual Meeting on Association for Computational Linguistics [C]. Association for Computational Linguistics. 124-131.
[35]Chen, B. & R. Kuhn. 2011. Amber: A modified bleu, enhanced ranking metric[A]. Proceedings of the 6th Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 71-77.
[36]Choi, F. Y. Y. 2000. Advances in domain independent linear text segmentation[A]. Proceedings of the 1st North American chapter of the Association for Computational Linguistics Conference [C]. Association for Computational Linguistics. 26-33.
[37]Choi, F. Y. Y., Wiemer-Hastings, P. & J. Moore. 2001. Latent semantic analysis for text segmentation [A]. Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing [C]. Association for Computational Linguistics. W01-0514.
[38]Chomsky, N. 1981. Lectures on Government and Binding [M]. Dordrecht: Foris.
[39]Cohen, J. 1960. A coefficient of agreement for nominal scales [J]. Educational and Psychological Measurement, 20(1): 37-46.
[40]Cohen, J. 1968. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit [J]. Psychological Bulletin, 70(4): 213.
[41]Corder, S. P. 1974. Error analysis. In J. L. P. Allen & S. P. Corder (Eds.), Techniques in Applied Linguistics. Oxford: Oxford University Press.
[42]Dagan, I., Glickman, O. & B. Magnini. 2005. The PASCAL recognising textual entailment challenge [A]. Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment [C]. 1-15.
[43]Dahlmeier, D., Hwee, T. N. & M. W. Siew. 2013. Building a large annotated corpus of learner English: The NUS corpus of learner English [A]. Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2013) [C]. NAACL/HLT. 22-31.
[44]Deane, P. 2005. A nonparametric method for extraction of candidate phrasal terms[A]. Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics [C]. Association for Computational Linguistics. 605-613.
[45]Denkowski, M. & A. Lavie. 2010. Extending the METEOR machine translation evaluation metric to the phrase level [A]. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL [C]. Association for Computational Linguistics. 250-253.
[46]Denkowski, M. & A. Lavie. 2014. Meteor universal: language specific translation evaluation for any target language [A]. Proceedings of the EACL 2014 Workshop on Statistical Machine Translation [C]. European Chapter of the Association for Computational Linguistics. W14-3348.
[47]Doddington, G. 2002. Automatic evaluation of machine translation quality using n-gram co-occurrence statistics [A]. Proceedings of the Second International Conference on Human Language Technology Research [C]. Brulingtong: Morgan Kaufmann Publishers Inc. 138-145.
[48]Dreyer, M. & D. Marcu. 2012. Hyter: meaning-equivalent semantics for translation evaluation [A]. Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies [C]. Association for Computational Linguistics. 162-171.
[49]Duh, K. 2008. Ranking vs. regression in machine translation evaluation [A]. Proceedings of the Third Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 191-194.
[50]Dunning, T. 1993. Accurate methods for the statistics of surprise and coincidence[J]. Computational Linguistics, 19(1): 61-74.
[51]Fishel, M., Bojar, O. & M. Popovi . 2012. Terra: a collection of translation error-annotated corpora [A]. Language Resources and Evaluation Conference [C]. The European Language Resources Association. 7-14.
[52]Florén, C. 2006. ENTRAD, an English Spanish parallel corpus created for the teaching of translation [A]. The 7th Teaching and Language Corpora Conference[C]. TALC. 431-442.
[53]Fort, K., Adda, G. & K. B. Cohen. 2011. Amazon mechanical turk: Gold mine or coal mine? [J]. Computational Linguistics, 37(2): 413-420.
[54]Gimenéz, J. & L. Màrquez. 2010. Asiya: an open toolkit for automatic machine translation (meta-) evaluation [J]. The Prague Bulletin of Mathematical Linguistics, 94: 77-86.
[55]Gundel, J. K., Hedberg, N. & R. Zacharski. 1993. Cognitive status and the form of referring expressions in discourse [J]. Language, 69: 274-307.
[56]Gupta, R., Orasan, C. & J. V. Genabith. 2015. Machine translation evaluation using recurrent neural networks [A]. Proceedings of the Tenth Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 380-384.
[57]Guzmán, F., Joty, S., Màrquez, L. & P. Nakov. 2015. Pairwise neural machine translation evaluation [A]. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing [C]. Association for Computational Linguistics. 805-814.
[58]Halliday, M. A. K. & C. M. Matthiessen. 2008. An Introduction to Functional Grammar [M]. Beijing: Foreign Language Teaching and Research Press.
[59]Han, A. L. F., Lu, Y., Wong, D. F., et al. 2013. Quality estimation for machine translation using the joint method of evaluation criteria and statistical modeling[A]. Proceedings of the ACL 2013 Eight Workshop on Statistical Machine Translation (ACL-WMT 2013) [C]. Association for Computational Linguistics. 365-372.
[60]He, H., Kevin, G. & L. Jimmy. 2015. Multi-perspective sentence similarity modeling with convolutional neural networks [A]. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing [C]. Association for Computational Linguistics. 1576-1586.
[61]He, Y., Du, J., Way, A. & J. van Genabith. 2010. The DCU dependency-based metric in WMT Metrics MATR 2010 [A]. Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and Metrics MATR [C]. 324-328.
[62]Hearst, M. A. 1997. TextTiling: Segmenting text into multi-paragraph subtopic passages [J]. Computational Linguistics, 23(1): 33-64.
[63]Hobbs, J. R. 1978. Resolving pronoun references [J]. Lingua, 44(4): 311-338.
[64]Hochreiter, S. & J. Schmidhuber. 1997. Long short-term memory [J]. Neural Computation, 9(8): 1735-1780.
[65]Itagaki, M., Aikawa, T. & X. D. He. 2007. Automatic validation of terminology translation consistency with statistical method [A]. Proceedings of MT Summit XI[C]. Association for Computational Linguistics. 269-274.
[66]Jones, K. S., Walker, S. & S. E. Robertson. 2000. A probabilistic model of information retrieval: development and comparative experiments: Part 2 [J]. Information Processing & Management, 36(6): 809-840.
[67]Kondo, F. 2007. Translation units in Japanese-English corpora: the case of frequent nouns [A]. Corpus Linguistics Conference (CL2007) [C]. CLARIN ERIC. 1-15.
[68]Kondo-Brown, K. 2002. A FACETS analysis of rater bias in measuring Japanese second language writing performance [J]. Language Testing, 19(1): 3-31.
[69]Koponen, M. & L. Salmi. 2015. On the correctness of machine translation: A machine translation post-editing task [J]. The Journal of Specialised Translation, 23: 118-136.
[70]Kusner, M., Sun, Y., Kolkin, N., et al. 2015. From word embeddings to document distances [A]. International Conference on Machine Learning [C]. International Machine Learning Society. 957-966.
[71]Kuznetsova, P., Ordonez, V., Berg, A. C., et al. 2012. Collective generation of natural image descriptions [A]. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics [C]. Association for Computational Linguistics. 359-368.
[72]Landis, J. R. & G. G. Koch. 1977. An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers [J]. Biometrics, 33(2): 363-374.
[73]Leacock, C., Chodorow, M., Gamon, M., et al. 2010. Automated grammatical error detection for language learners [J]. Synthesis Lectures on Human Language Technologies, 3(1): 1-134.
[74]Leacock, C., Gamon, M. & C. Brockett. 2009. User input and interactions on Microsoft Research ESL assistant [A]. Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications [C]. Association for Computational Linguistics. 73-81.
[75]Lee, J. & S. Seneff. 2008. Correcting misuse of verb forms [A]. Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technology (ACL/HLT) [C]. Association for Computational Linguistics. 174-182.
[76]Liang, P., Taskar, B. & D. Klein. 2006. Alignment by agreement [A]. Proceedings of HLT-NAACL'06 [C]. North American Chapter of the Association for Computational Linguistics. 104-111.
[77]Lin, C. & E. Hovy. 2000. The automated acquisition of topic signatures for text summarization [A]. Proceedings of COLING'00 [C]. The International Committee on Computational Linguistics. 495-501.
[78]Lin, C. Y. 2004. ROUGE: a package for automatic evaluation of summaries [A]. Proceedings of the Workshop on Text Summarization Branches Out (WAS 2004)[C]. Association for Computational Linguistics. W04-1013.
[79]Liu, C., Dahlmeier, D. & H. T. Ng. 2010. TESLA: Translation evaluation of sentences with linear-programming-based analysis [A]. Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and Metrics MATR [C]. Association for Computational Linguistics. 354-359.
[80]Liu, D. & D. Gildea. 2005. Syntactic features for evaluation of machine translation [A]. Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization [C]. Association for Computational Linguistics. 25-32.
[81]MacCartney, B. & C. D. Manning. 2008. Modeling semantic containment and exclusion in natural language inference [A]. Proceedings of the 22nd International Conference on Computational Linguistics [C]. Association for Computational Linguistics. 521-528.
[82]Manning, C. 2015. Computational linguistics and deep learning [J]. Computational Linguistics, 41(4): 701-707.
[83]Manning, C. & H. Schütze. 1999. Foundations of Statistical Natural Language Processing [M]. Cambridge: MIT Press.
[84]Mikolov, T., Chen, K., Corrado, G., et al. 2013a. Efficient estimation of word representations in vector space [J]. arXiv preprint arXiv: 1301.3781.
[85]Mikolov, T., Sutskever, I., Chen, K., et al. 2013b. Distributed representations of words and phrases and their compositionality [A]. NIPS'13 Proceedings of the 26th International Conference on Neural Information Processing Systems [C]. Curran Associates Inc. 3111-3119.
[86]Moreau, E. & C. Vogel. 2012. Quality estimation: an experimental study using unsupervised similarity measures [A]. Proceedings of the Seventh Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics.
[87]Neubert, A. 1994. Competence in translation: a complex skill, how to study and to teach it [A]. In M. Snell-Hornby, F. Pochhacker & K. Kaindl (Eds.), Translation Studies: An Interdiscipline [C]. Amsterdam: John Benjamins.
[88]Nießen, S., Och, F. J., Leusch, G. & H. Ney. 2000. An evaluation tool for machine translation: fast evaluation for MT research [A]. Language Resources and Evaluation Conference [C]. The European Language Resources Association. L00-1210 .
[89]Och, F. J. & N. Hermann. 2003. A systematic comparison of various statistical alignment models [J]. Computational Linguistics, 9(1): 19-51.
[90]Omar, N., Asma N., Razali M. & S. Darus. 2008. Automated essay marking tool for ESL writing based on heuristics [A]. Proceedings of the International Conference of Education, Research and Innovation (ICERI) [C]. Researchgate. 1-10.
[91]Owczarzak, K, van Genabith, J. & A. Way. 2007. Labeled dependencies in machine translation evaluation [A]. Proceedings of the Second Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 104-111.
[92]Padó, S., Galley, M., Jurafsky, D., et al. 2009. Robust machine translation evaluation with entailment features [A]. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP [C]. Association for Computational Linguistics. 297-305.
[93]Palmer, M., Gildea, D. & P. Kingsbury. 2005. The Proposition Bank: A corpus annotated with semantic roles [J]. Computational Linguistics Journal, 31(1): 71-106.
[94]Papineni, K., Roukos, S., Ward, T., et al. 2002. BLEU: a method for automatic evaluation of machine translation [A]. Proceedings of the 40th Annual Meeting on ACL [C]. Association for Computational Linguistics. 311-318.
[95]Pascanu, R., Gulcehre, C., Cho, K. & Y. Bengio. 2013. How to construct deep recurrent neural networks [J]. arXiv preprint arXiv: 1312.6026.
[96]Popovi, M. & H. Ney. 2009. Syntax-oriented evaluation measures for machine translation output [A]. Proceedings of the Fourth Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 29-32.
[97]Popovi, M. & M. Arcan. 2016. PE2rr Corpus: manual error annotation of automatically pre-annotated MT post-edits [A]. Proceedings of the Tenth International Conference on Language Resources and Evaluation [C]. The European Language Resources Association. 1-10.
[98]Prince, E. F. 1992. The ZPG letter: Subjects, definiteness, and information status[A]. In C. M. William & S. A. Thompson (Eds), Discourse Description: Diverse Linguistic Analyses of a Fund-raising Text [C]. Amsterdam: John Benjamins. 295-325.
[99]Pym, A. D. 1992. Translation error analysis and the interface with language teaching [A]. In C. Dollerup & A. Lindegaard (Eds.), Teaching Translation and Interpreting: Training, Talent and Experience [C]. Amsterdam: John Benjamins. 279-288.
[100]Richards, I. A. 1953. Towards a theory of translating: In studies in Chinese thought [A]. In A. F. Wright (Ed.), American Anthropological Association, Vol. 55, memoir 75. Chicago: Chicago University Press. 247-262.
[101]Salton, G. & C. Buckley. 1998. Term weighting approaches in automatic text retrieval [J]. Information Processing and Management, 24(5): 513-523.
[102]Savenkov, D., Pavel, B. & L. Mikhail. 2011. Search snippet evaluation at yandex: Lessons learned and future directions [A]. International Conference of the Cross-Language Evaluation Forum for European Languages [C]. Springer: 14-25.
[103]Shei, C. C. & H. Pain. 2000. An ESL writer's collocational aid [J]. Computer Assisted Language Learning, 13(2): 167-182.
[104]Sleator, D. & D. Temperley. 1993. Parsing English with a Link Grammar [A]. Proceedings of Third International Workshop on Parsing Technologies [C].
[105]Snover, M., Dorr, B., Schwartz, R., et al. 2006. A study of translation edit rate with targeted human annotation [A]. Proceedings of Association for Machine Translation in the Americas [C]. The Association for Machine Translation. 223-231.
[106]Snover, M., Madnani, N., Dorr, B., et al. 2009a. TER-Plus: Paraphrase, semantic, and alignment enhancements to translation edit rRate [J]. Machine Translation, 23(2): 117-127.
[107]Snover, M., Madnani, N., Dorr, B. J., et al. 2009b. Fluency, adequacy, or HTER?: Exploring different human judgments with a tunable MT metric [A]. Proceedings of the Fourth Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 259-268.
[108]Sosnina, E. P. 2006. Development and application of Russian translation learner corpus [A]. Presented at Corpus Linguistics-2006 [C]. 10-14.
[109]Specia, L., Paetzold, G. & C. Scarton. 2015. Multi-level translation quality prediction with QuEst++ [A]. Proceedings of ACL-IJCNLP 2015 System Demonstrations [C]. Association for Computational Linguistics. 115-120.
[110]Specia, L., Raj, D. & M. Turchi. 2010. Machine translation evaluation versus quality estimation [J]. Machine Translation, 24(1): 39-50.
[111]Specia, L., Shah, K., Souza J. G., et al. 2013. QuEst - A translation quality estimation framework [A]. Proceedings of the 51st Annual Meeting of the ACL[C]. Association for Computational Linguistics. 79-84.
[112]Stymne, S. 2011. Blast: A tool for error analysis of machine translation output [A]. Proceedings of ACL-HLT'11 [C]. Association for Computational Linguistics. 56-61.
[113]Teubert, W. 2004. Units of meaning, parallel corpora, and their implications for language teaching [A]. In U. Connor & T. A. Upton (Eds.), Applied Corpus Linguistics: A Multidimensional Perspective [C]. Amsterdam: Rododi. 171-189.
[114]Turian, P. J., Shen, L. & I. D. Melamed. 2003. Evaluation of machine translation and its evaluation [A]. Proceedings of MT Summit IX [C]. Association for Computational Linguistics. 386-393.
[115]Turpin, A., Tsegay, Y., Hawking, D., et al. 2007. Fast generation of result snippets in web search [A]. Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval [C]. Association for Computing Machinery. 127-134.
[116]Uzar, R. & J. Waliński. 2001. Analysing the fluency of translators [J]. International Journal of Corpus Linguistics, 6: 155-166.
[117]Valotkaite, J. & M. Asadullah. 2012. Error detection for post-editing rule-based machine translation [A]. Proceedings of the AMTA [C]. WPTP2.
[118]Vedantam, R., Lawrence Z. C. & D. Parikh. 2015. Cider: Consensus-based image description evaluation [A]. IEEE Conference on Computer Vision and Pattern Recognition [C]. 4566-4574.
[119]Vilar, D., Xu, J., d'Haro, L. F. & H. Ney. 2006. Error analysis of statistical machine translation output [A]. Language Resources and Evaluation Conference[C]. The European Language Resources Association. 697-702.
[120]Vinyals, O., Toshev, A., Bengio, S., et al. 2015. Show and tell: A neural image caption generator [A]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition [C]. IEEE. 3156-3164.
[121]Wang, W. Q. 2007. Corpus-driven study of translation units in an EnglishChinese parallel corpus [A]. Corpus Linguistics Conference (CL2007) [C]. University of Birmingham.
[122]Wisniewski, G., Singh, A. K. & F. Yvon. 2013. Quality estimation for machine translation: Some lessons learned [J]. Machine Translation, 27(3-4): 213-238.
[123]Xiong, D., Zhang, M. & H. Li. 2010. Error detection for statistical machine translation using linguistic features [A]. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics [C]. Association for Computational Linguistics. 604-611.
[124]Xue, N., Bojar, O., Hajic, J., et al. 2014. Not an interlingua, but close: comparison of English AMRs to Chinese and Czech [A]. Language Resources and Evaluation Conference [C]. The European Language Resources Association. 1765-1772.
[125]Ye, Y., Zhou, M. & C. Y. Lin. 2007. Sentence level machine translation evaluation as a ranking problem: one step aside from BLEU [A]. Proceedings of the Second Workshop on Statistical Machine Translation [C]. Association for Computational Linguistics. 240-247.
[126]Yu, S. W. 1991. Automatic evaluation of output quality for machine translation systems [A]. In Falkedal, K. (Ed.), Proceedings of the Evaluators' Forum [C]. Switzerland: Les Rasses.
[127]Zaidan, O. F. & C. Callison-Burch. 2011. Crowdsourcing translation: professional quality from non-professionals [A]. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies[C]. Association for Computational Linguistics. 1220-1229.
[128]Zeman, D., Fishel, M., Berka, J. & O. Bojar. 2011. Addicter: what is wrong with my translations? [J]. The Prague Bulletin of Mathematical Linguistics, 96: 79-88.
[129]Zhang, Y. & B. Wallace. 2015. A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification [J]. arXiv preprint arXiv: 1510.03820.
[130]Zhou, M., Wang, B., Liu, S., et al. 2008. Diagnostic evaluation of machine translation systems using automatically constructed linguistic checkpoints [A]. Proceedings of the 22nd International Conference on Computational Linguistics[C]. Association for Computational Linguistics. 1121-1128.
[131]江进林.2010.中国学习者汉译英机助评分模型的构建[D].北京外国语大学博士论文.
[132]秦颖.2015.翻译质量自动评价研究综述[J].计算机应用研究(2):326-329.
[133]曲维光、周俊生、吴晓东等.2017.自然语言句子抽象语义表示AMR研究综述[J].数据采集与处理(1):26-36.
[134]司显柱.2004.论功能语言学视角的翻译质量评估模式研究[J].外语教学(4):45-50.
[135]王金铨.2008.中国学习者汉译英机助评分模型的构建[D].北京外国语大学博士论文.
[136]宗成庆,张霄军译.2012.统计机器翻译.北京:电子工业出版社.