{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:46:59Z","timestamp":1743032819973,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030522452"},{"type":"electronic","value":"9783030522469"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-52246-9_44","type":"book-chapter","created":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T11:03:49Z","timestamp":1593774229000},"page":"599-609","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Rethinking Our Assumptions About Language Model Evaluation"],"prefix":"10.1007","author":[{"given":"Nancy","family":"Fulda","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,4]]},"reference":[{"key":"44_CR1","unstructured":"Embedding projector. \nhttps:\/\/projector.tensorflow.org\/"},{"key":"44_CR2","unstructured":"Text similarity: Estimate the degree of similarity between publisher = Dandelion API. \nhttps:\/\/dandelion.eu\/semantic-text\/text-similarity-demo\/"},{"key":"44_CR3","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135\u2013146 (2017)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"44_CR4","doi-asserted-by":"crossref","unstructured":"Brokos, G.-I., Malakasiotis, P., Androutsopoulos, I.: Using centroids of word embeddings and word mover\u2019s distance for biomedical document retrieval in question answering. CoRR, abs\/1608.03905 (2016)","DOI":"10.18653\/v1\/W16-2915"},{"key":"44_CR5","doi-asserted-by":"crossref","unstructured":"Cer, D., Yang, Y., Kong, S., Hua, N., Limtiaco, N., St. John, R., Constant, N., Guajardo-Cespedes, M., Yuan, S., Tar, C., Sung, Y.-H., Strope, B., Kurzweil, R.: Universal sentence encoder. CoRR, abs\/1803.11175 (2018)","DOI":"10.18653\/v1\/D18-2029"},{"key":"44_CR6","unstructured":"Colyer, A.: The amazing power of word vectors (2016). \nhttps:\/\/blog.acolyer.org\/2016\/04\/21\/the-amazing-power-of-word-vectors\/"},{"key":"44_CR7","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint \narXiv:1810.04805\n\n (2018)"},{"key":"44_CR8","unstructured":"Fulda, N., Murdoch, B., Ricks, D., Wingate, D.: Informing action primitives through free-form text. In: NIPS Workshop on Visually Grounded Interaction and Language (2017)"},{"key":"44_CR9","doi-asserted-by":"crossref","unstructured":"Fulda, N., Ricks, D., Murdoch, B., Wingate, D.: What can you do with a rock? Affordance extraction via word embeddings. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 1039\u20131045 (2017)","DOI":"10.24963\/ijcai.2017\/144"},{"key":"44_CR10","unstructured":"Fulda, N., Ricks, D., Murdoch, B., Wingate, D.: Threat, explore, barter, puzzle: a semantically-informed algorithm for extracting interaction modes. In: AAAI Workshop on Knowledge Extraction from Games (2018)"},{"key":"44_CR11","unstructured":"Fulda, N., Tibbetts, N., Brown, Z., Wingate, D.: Harvesting common-sense navigational knowledge for robotics from uncurated text corpora. In: Proceedings of the First Conference on Robot Learning (CoRL) (2017)"},{"key":"44_CR12","unstructured":"The Turku\u00a0NLP Group. Word embeddings demo. \nhttp:\/\/bionlp-www.utu.fi\/wv_demo\/"},{"key":"44_CR13","doi-asserted-by":"crossref","unstructured":"Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. CoRR, abs\/1605.09096 (2016)","DOI":"10.18653\/v1\/P16-1141"},{"key":"44_CR14","unstructured":"Horan, C.: Using sentence embeddings to automate customer support, part one, December 2018. \nhttps:\/\/blog.floydhub.com\/automate-customer-support-part-two\/"},{"key":"44_CR15","doi-asserted-by":"crossref","unstructured":"Iyyer, M., Manjunatha, V., Boyd-Graber, J., Daum\u00e9 III, H.: Deep unordered composition rivals syntactic methods for text classification. In: Association for Computational Linguistics (2015)","DOI":"10.3115\/v1\/P15-1162"},{"issue":"1","key":"44_CR16","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1186\/s12859-019-2678-8","volume":"20","author":"I Karadeniz","year":"2019","unstructured":"Karadeniz, I., \u00d6zg\u00fcr, A.: Linking entities through an ontology using word embeddings and syntactic re-ranking. BMC Bioinformatics 20(1), 156 (2019)","journal-title":"BMC Bioinformatics"},{"key":"44_CR17","unstructured":"Karpathy, A., Joulin, A., Li, F.: Deep fragment embeddings for bidirectional image sentence mapping. \narXiv:1406.5679\n\n (2014)"},{"key":"44_CR18","unstructured":"Kiros, R.: Sent2vec encoder and training code from the paper \u201cskip-thought vectors\u201d (2017). \nhttps:\/\/github.com\/ryankiros\/skip-thoughts"},{"key":"44_CR19","unstructured":"Kiros, R., Zhu, Y., Salakhutdinov, R., Zemel, R.S., Torralba, A., Urtasun, R., Fidler, S.: Skip-thought vectors, pp. 3294\u20133302 (2015)"},{"key":"44_CR20","unstructured":"Li, Y., Su, H., Shen, X., Li, W., Cao, Z., Niu, S.: DailyDialog: a manually labelled multi-turn dialogue dataset. arXiv preprint \narXiv:1710.03957\n\n (2017)"},{"key":"44_CR21","unstructured":"Liu, A.: Word to vec JS demo (2016). \nhttp:\/\/turbomaze.github.io\/word2vecjson\/"},{"key":"44_CR22","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR, abs\/1301.3781 (2013)"},{"key":"44_CR23","unstructured":"Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. Association for Computational Linguistics, May 2013"},{"key":"44_CR24","unstructured":"Moody, C.: A word is worth a thousand vectors (2015). \nhttps:\/\/multithreaded.stitchfix.com\/blog\/2015\/03\/11\/word-is-worth-a-thousand-vectors\/"},{"key":"44_CR25","unstructured":"Nicholson, C.: A beginner\u2019s guide to word2vec and neural word embeddings. \nhttps:\/\/skymind.ai\/wiki\/word2vec"},{"key":"44_CR26","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, Doha, Qatar, 25\u201329 October 2014, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"44_CR27","doi-asserted-by":"crossref","unstructured":"Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., Zettlemoyer, L.: Deep contextualized word representations. arXiv preprint \narXiv:1802.05365\n\n (2018)","DOI":"10.18653\/v1\/N18-1202"},{"key":"44_CR28","unstructured":"Shima, H.: Ws4j demo. \nhttp:\/\/ws4jdemo.appspot.com\/"},{"key":"44_CR29","unstructured":"spaCy. Word vectors and semantic similarity (2016\u20132019). \nhttps:\/\/spacy.io\/usage\/vectors-similarity"},{"key":"44_CR30","unstructured":"username: DaveTheAl. Best practical algorithm for sentence similarity (2017). \nhttps:\/\/datascience.stackexchange.com\/questions\/25053\/best-practical-algorithm-for-sentence-similarity"},{"key":"44_CR31","unstructured":"username: whs2k. How is the similarity method in spacy computed (2017). \nhttps:\/\/stats.stackexchange.com\/questions\/304217\/how-is-the-similarity-method-in-spacy-computed"},{"key":"44_CR32","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 5998\u20136008. Curran Associates, Inc. (2017)"},{"key":"44_CR33","unstructured":"Wilson, T., Kozareva, Z., Nakov, P., Ritter, A., Rosenthal, S., Stoyanov, V.: Sentiment analysis in twitter (2013). \nhttp:\/\/www.cs.york.ac.uk\/semeval-2013\/task2\/"},{"key":"44_CR34","unstructured":"Wingate, D., Myers, W., Fulda, N., Etchart, T.: Embedding grammars. arXiv preprint \narXiv:1808.04891\n\n (2018)"},{"key":"44_CR35","unstructured":"Wingate, D., Myers, W., Fulda, N., Etchart, T.: Embedding grammars (2018)"}],"container-title":["Advances in Intelligent Systems and Computing","Intelligent Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-52246-9_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T23:11:36Z","timestamp":1593817896000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-52246-9_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030522452","9783030522469"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-52246-9_44","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"4 July 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Science and Information Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/saiconference.com\/Computing","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}