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A major challenge for scaling machine learning is training models to perform tasks that are very difficult or time-consuming for humans to evaluate.
Learning from dialogue after deployment: Feed yourself, chatbot!
Hancock, B., Bordes, A., Mazare, P.-E., and Weston, J. (2019) · 1901
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Bertscore: Evaluating text generation with bert
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., and Artzi, Y. (2019a) · 1904
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Hierarchical transformers for multi-document summarization
Liu, Y. and Lapata, M. (2019a) · 1905
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Zhang, X., Wei, F., and Zhou, M. (2019c) · 1905
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Explain yourself! leveraging language models for commonsense reasoning
Rajani, N. F., McCann, B., Xiong, C., and Socher, R. (2019) · 1906
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Bigpatent: A large-scale dataset for abstractive and coherent summarization
Sharma, E., Li, C., and Wang, L. (2019) · 1906
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Way off-policy batch deep reinforcement learning of implicit human preferences in dialog
Jaques, N., Ghandeharioun, A., Shen, J. H., Ferguson, C., Lapedriza, A., Jones, N., Gu, S., and Picard, R. (2019) · 1907
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Text summarization with pretrained encoders
Liu, Y. and Lapata, M. (2019b) · 1908
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Better rewards yield better summaries: Learning to summarise without references
Böhm, F., Gao, Y., Meyer, C. M., Shapira, O., Dagan, I., and Gurevych, I. (2019) · 1909
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Finding generalizable evidence by learning to convince q&a models
Perez, E., Karamcheti, S., Fergus, R., Weston, J., Kiela, D., and Cho, K. (2019) · 1909
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Answers unite! unsupervised metrics for reinforced summarization models
Scialom, T., Lamprier, S., Piwowarski, B., and Staiano, J. (2019) · 1909
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On extractive and abstractive neural document summarization with transformer language models
Subramanian, S., Li, R., Pilault, J., and Pal, C. (2019) · 1909
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Extractive summarization of long documents by combining global and local context
Xiao, W. and Carenini, G. (2019) · 1909
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Fine-tuning language models from human preferences
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., and Irving, G. (2019) · 1909
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J. (2019) · 1910
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Transfer of learning by composing solutions of elemental sequential tasks
Singh, S. P. (1992) · 1992
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Feudal reinforcement learning. nips’93 (pp. 271–278)
Dayan, P. and Hinton, G. (1993) · 1993
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A framework for behavioural cloning
Bain, M. and Sammut, C. (1995) · 1995
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Unsupervised question decomposition for question answering
Perez, E., Lewis, P., Yih, W.-t., Cho, K., and Kiela, D. (2020) · 2002
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Zhou, W. and Xu, K. (2020) · 2002
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Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics
Lin, C.-Y. and Och, F. J. (2004) · 2004
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Textrank: Bringing order into text
Mihalcea, R. and Tarau, P. (2004) · 2004
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Mead-a platform for multidocument multilingual text summarization
Radev, D., Allison, T., Blair-Goldensohn, S., Blitzer, J., Çelebi, A., Dimitrov, S., Drabek, E. F., Hakim, A., Lam, W., Liu, D., Otterbacher, J., Qi, H., Saggion, H., Teufel, S., Topper, M., Winkel, A., and Zhang, Z. (2004) · 2004
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Asking and answering questions to evaluate the factual consistency of summaries
Wang, A., Cho, K., and Lewis, M. (2020) · 2004
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · 2005
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Supert: Towards new frontiers in unsupervised evaluation metrics for multi-document summarization
Gao, Y., Zhao, W., and Eger, S. (2020) · 2005
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Unifiedqa: Crossing format boundaries with a single qa system
Thinking fast and slow with deep learning and tree search
Anthony, T., Tian, Z., and Barber, D. (2017) · 2017
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Deep reinforcement learning from human preferences
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D. (2017) · 2017
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A supervised approach to extractive summarisation of scientific papers
Collins, E., Augenstein, I., and Riedel, S. (2017) · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017) · 2017
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Towards coherent and cohesive long-form text generation
Cho, W. S., Zhang, P., Zhang, Y., Li, X., Galley, M., Brockett, C., Wang, M., and Gao, J. (2018) · 2018
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Khashabi, D., Min, S., Khot, T., Sabharwal, A., Tafjord, O., Clark, P., and Hajishirzi, H. (2020) · 2005
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Exploring content selection in summarization of novel chapters
Ladhak, F., Li, B., Al-Onaizan, Y., and McKeown, K. (2020) · 2005
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An approach to summarizing short stories
Kazantseva, A. (2006) · 2006
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Seal: Segment-wise extractive-abstractive long-form text summarization
Zhao, Y., Saleh, M., and Liu, P. J. (2020) · 2006
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Explorations in automatic book summarization
Mihalcea, R. and Ceylan, H. (2007) · 2007
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Frustratingly hard evidence retrieval for qa over books
Mou, X., Yu, M., Yao, B., Yang, C., Guo, X., Potdar, S., and Su, H. (2020) · 2007
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Hidden incentives for auto-induced distributional shift
Krueger, D., Maharaj, T., and Leike, J. (2020) · 2009
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Learning to summarize from human feedback
Stiennon, N., Ouyang, L., Wu, J., Ziegler, D. M., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P. (2020) · 2009
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Supervising strong learners by amplifying weak experts
Christiano, P., Shlegeris, B., and Amodei, D. (2018) · 2018
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A discourse-aware attention model for abstractive summarization of long documents
Cohan, A., Dernoncourt, F., Kim, D. S., Bui, T., Kim, S., Chang, W., and Goharian, N. (2018) · 2018
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Hierarchical neural story generation
Fan, A., Lewis, M., and Dauphin, Y. (2018) · 2018
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Reward learning from human preferences and demonstrations in atari
Ibarz, B., Leike, J., Pohlen, T., Irving, G., Legg, S., and Amodei, D. (2018) · 2018
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Irving, G., Christiano, P., and Amodei, D. (2018) · 2018
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The narrativeqa reading comprehension challenge
Kočiskỳ, T., Schwarz, J., Blunsom, P., Dyer, C., Hermann, K. M., Melis, G., and Grefenstette, E. (2018) · 2018
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Can neural machine translation be improved with user feedback?
Kreutzer, J., Khadivi, S., Matusov, E., and Riezler, S. (2018) · 2018
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Improving a neural semantic parser by counterfactual learning from human bandit feedback
Lawrence, C. and Riezler, S. (2018) · 2018
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Scalable agent alignment via reward modeling: a research direction
Leike, J., Krueger, D., Everitt, T., Martic, M., Maini, V., and Legg, S. (2018) · 2018
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Generating wikipedia by summarizing long sequences
Liu, P. J., Saleh, M., Pot, E., Goodrich, B., Sepassi, R., Kaiser, L., and Shazeer, N. (2018) · 2018
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2019) · 2019
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Overview and insights from the shared tasks at scholarly document processing 2020: Cl-scisumm, laysumm and longsumm
Chandrasekaran, M. K., Feigenblat, G., Hovy, E., Ravichander, A., Shmueli-Scheuer, M., and de Waard, A. (2020) · 2020
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Guir@ longsumm 2020: Learning to generate long summaries from scientific documents
Gharebagh, S. S., Cohan, A., and Goharian, N. (2020) · 2020
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Specification gaming: the flip side of ai ingenuity
Krakovna, V., Uesato, J., Mikulik, V., Rahtz, M., Everitt, T., Kumar, R., Kenton, Z., Leike, J., and Legg, S. (2020) · 2020
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Evaluating arguments one step at a time
Ought (2020) · 2020
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Booksum: A collection of datasets for long-form narrative summarization
Kryściński, W., Rajani, N., Agarwal, D., Xiong, C., and Radev, D. (2021) · 2021
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Readtwice: Reading very large documents with memories
Zemlyanskiy, Y., Ainslie, J., de Jong, M., Pham, P., Eckstein, I., and Sha, F. (2021) · 2021
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