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The usage and amount of information available on the internet increase over the past decade.
Cross-lingual language model pretraining
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End-to-end open-domain question answering with bertserini
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Scaling question answering to the web
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Language models are few-shot learners
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Question answering passage retrieval using dependency relations
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System and methods for inferring informational goals and preferred level of detail of results in response to questions posed to an automated information-retrieval or question-answering service
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A paradigm-based finite state morphological analyzer for marathi
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What do people ask their social networks, and why? a survey study of status message q&a behavior
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Developing oriya morphological analyzer using lt-toolbox
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Predicting web searcher satisfaction with existing community-based answers
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MCTest: A challenge dataset for the open-domain machine comprehension of text
Richardson, M., Burges, C. J., and Renshaw, E. (2013) · 2013
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VQA: visual question answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., and Parikh, D. (2015) · 2015
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Teaching machines to read and comprehend
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From word embeddings to document distances
Kusner, M. J., Sun, Y., Kolkin, N. I., and Weinberger, K. Q. (2015) · 2015
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Very deep convolutional networks for large-scale image recognition
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End-to-end memory networks
Sukhbaatar, S., Szlam, A., Weston, J., and Fergus, R. (2015) · 2015
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Sequence to sequence – video to text
Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., and Saenko, K. (2015) · 2015
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A long short-term memory model for answer sentence selection in question answering
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Describing videos by exploiting temporal structure
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Video paragraph captioning using hierarchical recurrent neural networks
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Embracing data abundance: Booktest dataset for reading comprehension
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End to end long short term memory networks for non-factoid question answering
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Incorporating copying mechanism in sequence-to-sequence learning
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The goldilocks principle: Reading children’s books with explicit memory representations
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TGIF: A new dataset and benchmark on animated GIF description
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Social question answering: Textual, user, and network features for best answer prediction
Molino, P., Aiello, L. M., and Lops, P. (2016) · 2016
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MS MARCO: A human generated machine reading comprehension dataset
Nguyen, T., Rosenberg, M., Song, X., Gao, J., Tiwary, S., Majumder, R., and Deng, L. (2016) · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P. (2016) · 2016
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Visual question answering: A survey of methods and datasets
Wu, Q., Teney, D., Wang, P., Shen, C., Dick, A. R., and van den Hengel, A. (2016) · 2016
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Dynamic coattention networks for question answering
Xiong, C., Zhong, V., and Socher, R. (2016) · 2016
Dr. tux: A question answering system for ubuntu users
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Anaphora resolution with the arrau corpus
Poesio, M., Grishina, Y., Kolhatkar, V., Moosavi, N. S., Roesiger, I., Roussel, A., Simonjetz, F., Uma, A., Uryupina, O., Yu, J., et al. (2018) · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2018) · 2018
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Know what you don’t know: Unanswerable questions for SQuAD
Rajpurkar, P., Jia, R., and Liang, P. (2018) · 2018
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Context-aware neural machine translation learns anaphora resolution
Voita, E., Serdyukov, P., Sennrich, R., and Titov, I. (2018) · 2018
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Cross-lingual word embeddings for low-resource language modeling
Adams, O., Makarucha, A., Neubig, G., Bird, S., and Cohn, T. (2017) · 2017
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Aroma: A recursive deep learning model for opinion mining in arabic as a low resource language
Al-Sallab, A., Baly, R., Hajj, H., Shaban, K. B., El-Hajj, W., and Badaro, G. (2017) · 2017
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Evaluating natural language understanding services for conversational question answering systems
Braun, D., Mendez, A. H., Matthes, F., and Langen, M. (2017) · 2017
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Reading Wikipedia to answer open-domain questions
Chen, D., Fisch, A., Weston, J., and Bordes, A. (2017) · 2017
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Quasar: Datasets for question answering by search and reading
Dhingra, B., Mazaitis, K., and Cohen, W. W. (2017) · 2017
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Learning to ask: Neural question generation for reading comprehension
Du, X., Shao, J., and Cardie, C. (2017) · 2017
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Searchqa: A new q&a dataset augmented with context from a search engine
Dunn, M., Sagun, L., Higgins, M., Güney, V. U., Cirik, V., and Cho, K. (2017) · 2017
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A better way to attend: Attention with trees for video question answering
Xue, H., Chu, W., Zhao, Z., and Cai, D. (2018) · 2018
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WikiQA: A challenge dataset for open-domain question answering
Yang, Y., Yih, W.-t., and Meek, C. (2015) · 2018
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Modelling domain relationships for transfer learning on retrieval-based question answering systems in e-commerce
Yu, J., Qiu, M., Jiang, J., Huang, J., Song, S., Chu, W., and Chen, H. (2018) · 2018
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Variational reasoning for question answering with knowledge graph
Zhang, Y., Dai, H., Kozareva, Z., Smola, A., and Song, L. (2018) · 2018
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Paragraph-level neural question generation with maxout pointer and gated self-attention networks
Zhao, Y., Ni, X., Ding, Y., and Ke, Q. (2018) · 2018
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Careful selection of knowledge to solve open book question answering
Banerjee, P., Pal, K. K., Mitra, A., and Baral, C. (2019) · 2019
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Sentence mover’s similarity: Automatic evaluation for multi-sentence texts
Clark, E., Celikyilmaz, A., and Smith, N. A. (2019) · 2019
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A logic-based question answering system for cultural heritage
Cuteri, B., Reale, K., and Ricca, F. (2019) · 2019
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Knowledge graph embedding based question answering
Huang, X., Zhang, J., Li, D., and Li, P. (2019) · 2019
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Natural questions: A benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., Toutanova, K., Jones, L., Kelcey, M., Chang, M.-W., Dai, A. M., Uszkoreit, J., Le, Q., and Petrov, S. (2019) · 2019
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Real-time decoding of question-and-answer speech dialogue using human cortical activity
Moses, D. A., Leonard, M. K., Makin, J. G., and Chang, E. F. (2019) · 2019
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Bert with history answer embedding for conversational question answering
Qu, C., Yang, L., Qiu, M., Croft, W. B., Zhang, Y., and Iyyer, M. (2019) · 2019
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Coqa: A conversational question answering challenge
Reddy, S., Chen, D., and Manning, C. D. (2019) · 2019
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Framework for question-answering in Sanskrit through automated construction of knowledge graphs
Terdalkar, H. and Bhattacharya, A. (2019) · 2019
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Message passing for complex question answering over knowledge graphs
Vakulenko, S., Fernandez Garcia, J. D., Polleres, A., de Rijke, M., and Cochez, M. (2019) · 2019
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Deep modular co-attention networks for visual question answering
Yu, Z., Yu, J., Cui, Y., Tao, D., and Tian, Q. (2019) · 2019
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Tydi qa: A benchmark for information-seeking question answering in ty pologically di verse languages
Clark, J. H., Choi, E., Collins, M., Garrette, D., Kwiatkowski, T., Nikolaev, V., and Palomaki, J. (2020) · 2020
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Satellite image classification with data augmentation and convolutional neural network
Dave, P. R. and Pandya, H. A. (2020) · 2020
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Feqa: A question answering evaluation framework for faithfulness assessment in abstractive summarization
Durmus, E., He, H., and Diab, M. (2020) · 2020
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Hybrid query expansion using lexical resources and word embeddings for sentence retrieval in question answering
Esposito, M., Damiano, E., Minutolo, A., De Pietro, G., and Fujita, H. (2020) · 2020
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Cross-lingual multi-keyword rank search with semantic extension over encrypted data
Guan, Z., Liu, X., Wu, L., Wu, J., Xu, R., Zhang, J., and Li, Y. (2020) · 2020
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Xtreme: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation
Hu, J., Ruder, S., Siddhant, A., Neubig, G., Firat, O., and Johnson, M. (2020) · 2020
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Leveraging passage retrieval with generative models for open domain question answering
Izacard, G. and Grave, E. (2020) · 2020
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Dense passage retrieval for open-domain question answering
Karpukhin, V., Oğuz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., and tau Yih, W. (2020) · 2020
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Graph-based reasoning over heterogeneous external knowledge for commonsense question answering
Lv, S., Guo, D., Xu, J., Tang, D., Duan, N., Gong, M., Shou, L., Jiang, D., Cao, G., and Hu, S. (2020) · 2020
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Open-retrieval conversational question answering
Qu, C., Yang, L., Chen, C., Qiu, M., Croft, W. B., and Iyyer, M. (2020) · 2020
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Unsupervised commonsense question answering with self-talk
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Bert representations for video question answering
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