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Explicit Contextual Semantics for Text Comprehension [article]

Zhuosheng Zhang, Yuwei Wu, Zuchao Li, Hai Zhao
2019 arXiv   pre-print
Extensive experiments on benchmark machine reading comprehension and inference datasets verify that the proposed semantic learning helps our system reach new state-of-the-art over strong baselines which  ...  In terms of deep learning models, our embeddings are enhanced by explicit contextual semantic role labels for more fine-grained semantics.  ...  for text comprehension from probably quite different domains in both textual entailment and machine reading comprehension.  ... 
arXiv:1809.02794v3 fatcat:ztsqgbwaujefdj5daqwjq6tdiy

Annotating Entailment Relations for Shortanswer Questions

Simon Ostermann, Andrea Horbach, Manfred Pinkal
2015 Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications  
We annotate entailment relations between learner and target answers in the Corpus of Reading Comprehension Exercises for German (CREG) with a finegrained label inventory and compare them in various ways  ...  This paper presents an annotation project that explores the relationship between textual entailment and short answer scoring (SAS).  ...  We also thank our annotators Fernando Ardente, Sophie Henning and Maximilian Wolf for their help with this study and alexis Palmer for valuable feedback for our annotation guidelines.  ... 
doi:10.18653/v1/w15-4408 dblp:conf/acl-tea/OstermannHP15 fatcat:bybiyw2aafdujdjk6zochs46h4

CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment

Zhengping Jiang, Qi Sun
2018 Proceedings of The 12th International Workshop on Semantic Evaluation  
In this document we present an end-to-end machine reading comprehension system that solves multiple choice questions with a textual entailment perspective.  ...  In the model two kinds of prediction structure are ensembled, and the final accuracy of our system is 10 percent higher than the naiive baseline.  ...  Recently a lot of datasets are available for evaluating machine reading comprehension systems, for example, there are SQuAD (Rajpurkar et al., 2016) and the MCTest (Richardson et al., 2013) .  ... 
doi:10.18653/v1/s18-1176 dblp:conf/semeval/JiangS18 fatcat:p4h76e7osvdvfg5riz4lwa6uw4

Satisfying information needs with multi-document summaries

Sanda Harabagiu, Andrew Hickl, Finley Lacatusu
2007 Information Processing & Management  
Generating summaries that meet the information needs of a user relies on (1) several forms of question decomposition; (2) different summarization approaches; and (3) textual inference for combining the  ...  This novel framework for summarization has the advantage of producing highly responsive summaries, as indicated by the evaluation results.  ...  Textual entailment evaluation The system for recognizing textual entailment was evaluated as part of the Second PASCAL Recognizing Textual Entailment (RTE) Challenge ( Bar-Haim et al., 2006) .  ... 
doi:10.1016/j.ipm.2007.01.004 fatcat:uujzhptylfci3i4kdjknvn7bpa

Natural Language Processing Applications: A New Taxonomy using Textual Entailment

Manar Elshazly, Mohammed Haggag, Soha Ahmed Ehssan
2021 International Journal of Advanced Computer Science and Applications  
For this purpose, when combining a textual entailment with deep learning, they can hugely showed an improvement in performance accuracy and aid in new applications such as depression detection.  ...  Deep learning strategies are used in the work of text entailment instead of traditional Machine learning or raw coding to achieve new enhanced results.  ...  For defining the entailment between a couple of question and answer they propose this tool.  ... 
doi:10.14569/ijacsa.2021.0120580 fatcat:yp7wtq6n7bamjcsnpl2hathrwy

A Survey on Recognizing Textual Entailment as an NLP Evaluation [article]

Adam Poliak
2020 arXiv   pre-print
Recognizing Textual Entailment (RTE) was proposed as a unified evaluation framework to compare semantic understanding of different NLP systems.  ...  In this survey paper, we provide an overview of different approaches for evaluating and understanding the reasoning capabilities of NLP systems.  ...  Patrick Xia and Elias Stengel-Eskin for feedback on this draft, and Yonatan Belinkov and Sasha Rush for the encouragement to write a survey on RTE.  ... 
arXiv:2010.03061v1 fatcat:jfmgkh4ginalzauawlqdbkb6pq

Read + Verify: Machine Reading Comprehension with Unanswerable Questions [article]

Minghao Hu, Furu Wei, Yuxing Peng, Zhen Huang, Nan Yang, Dongsheng Li
2018 arXiv   pre-print
Machine reading comprehension with unanswerable questions aims to abstain from answering when no answer can be inferred.  ...  To address this problem, we propose a novel read-then-verify system, which not only utilizes a neural reader to extract candidate answers and produce no-answer probabilities, but also leverages an answer  ...  Acknowledgments We would like to thank Pranav Rajpurkar and Robin Jia for their helps with SQuAD 2.0 submissions.  ... 
arXiv:1808.05759v5 fatcat:shyxpkpxqvfobmhmxras2deswy

Survey on Answer Validation for Indonesian Question Answering System (IQAS)

Abdiansah Abdiansah, Azhari Azhari, Anny K. Sari
2018 International Journal of Intelligent Systems and Applications  
Research on Question Answering System (QAS) has been done mainly in English.  ...  One of the important issues in IQAS is Answer Validation (AV), which is a system that can determine the correctness of QAS.  ...  Lastly, [11] presented a methodology for tackling the problem of answer validation in question answering for reading comprehension tests.  ... 
doi:10.5815/ijisa.2018.04.08 fatcat:r3r6tygg5nawzkgkwj7jh42ele

Mining Numbers in Text: A Survey [chapter]

Minoru Yoshida, Kenji Kita
2021 Information Systems - Intelligent Information Processing Systems [Working Title]  
In this survey, we provide a quick overview of the history and recent advances of the research of mining such relations between numerals and words found in text data.  ...  However, relatively little attention has been paid in numerals found in texts and many systems treated the numbers found in the document in ad-hoc ways, such as regarded them as mere strings in the same  ...  Reading comprehension is a more complicated task, where the system is required to answer various types of questions.  ... 
doi:10.5772/intechopen.98540 fatcat:mhzwc2ykcvcglfsh3ltxzvzq5q

Jack the Reader – A Machine Reading Framework

Dirk Weissenborn, Pasquale Minervini, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bošnjak, Jeff Mitchell, Thomas Demeester, Tim Dettmers, Pontus Stenetorp, Sebastian Riedel
2018 Proceedings of ACL 2018, System Demonstrations  
Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions.  ...  To that end, we present Jack the Reader (JACK), a framework for Machine Reading that allows for quick model prototyping by component reuse, evaluation of new models on existing datasets as well as integrating  ...  Introduction Automated reading and understanding of textual and symbolic input, to a degree that enables question answering, is at the core of Machine Reading (MR).  ... 
doi:10.18653/v1/p18-4005 dblp:conf/acl/WeissenbornMAWR18 fatcat:or5rhfs3grfsvkulm3kkatddq4

Jack the Reader - A Machine Reading Framework [article]

Dirk Weissenborn, Pasquale Minervini, Tim Dettmers, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bošnjak, Jeff Mitchell, Thomas Demeester, Pontus Stenetorp, Sebastian Riedel
2018 arXiv   pre-print
Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions.  ...  To that end, we present Jack the Reader (Jack), a framework for Machine Reading that allows for quick model prototyping by component reuse, evaluation of new models on existing datasets as well as integrating  ...  Introduction Automated reading and understanding of textual and symbolic input, to a degree that enables question answering, is at the core of Machine Reading (MR).  ... 
arXiv:1806.08727v1 fatcat:p4r52au3zzewvaop66sy4lbyxu

Read + Verify: Machine Reading Comprehension with Unanswerable Questions

Minghao Hu, Furu Wei, Yuxing Peng, Zhen Huang, Nan Yang, Dongsheng Li
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Machine reading comprehension with unanswerable questions aims to abstain from answering when no answer can be inferred.  ...  To address this problem, we propose a novel read-then-verify system, which not only utilizes a neural reader to extract candidate answers and produce no-answer probabilities, but also leverages an answer  ...  Acknowledgments We would like to thank Pranav Rajpurkar and Robin Jia for their helps with SQuAD 2.0 submissions.  ... 
doi:10.1609/aaai.v33i01.33016529 fatcat:cqw3bgrxofbnnaoykuihcd2vem

Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems

Zhen Huang, Shiyi Xu, Minghao Hu, Xinyi Wang, Jinyan Qiu, Yongquan Fu, Yuncai Zhao, Yuxing Peng, Changjian Wang
2020 IEEE Access  
INDEX TERMS Open-domain textual question answering, deep learning, machine reading comprehension, information retrieval.  ...  Open-domain textual question answering (QA), which aims to answer questions from large data sources like Wikipedia or the web, has gained wide attention in recent years.  ...  EVALUATION For extractive textual QA tasks, in order to evaluate the predicted answer, we usually adopt two evaluation metrics [13] , which measure exact match and partially overlapped scores respectively  ... 
doi:10.1109/access.2020.2988903 fatcat:po4euxfronf3pob52qc2wcgrre

Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models [article]

Alena Fenogenova, Maria Tikhonova, Vladislav Mikhailov, Tatiana Shavrina, Anton Emelyanov, Denis Shevelev, Alexandr Kukushkin, Valentin Malykh, Ekaterina Artemova
2022 arXiv   pre-print
tests for understanding the meaning of a word in context (RUSSE) along with reading comprehension and common sense reasoning (DaNetQA, RuCoS, MuSeRC).  ...  Finally, we provide the integration of Russian SuperGLUE with a framework for industrial evaluation of the open-source models, MOROCCO (MOdel ResOurCe COmparison), in which the models are evaluated according  ...  The task is to come up with a binary answer (yes or no) for the given question. MuSeRC is a machine reading comprehension (MRC) task.  ... 
arXiv:2202.07791v1 fatcat:3yzjribv7zgonly55na2orldku

Comparing Test Sets with Item Response Theory [article]

Clara Vania, Phu Mon Htut, William Huang, Dhara Mungra, Richard Yuanzhe Pang, Jason Phang, Haokun Liu, Kyunghyun Cho, Samuel R. Bowman
2021 arXiv   pre-print
We find that Quoref, HellaSwag, and MC-TACO are best suited for distinguishing among state-of-the-art models, while SNLI, MNLI, and CommitmentBank seem to be saturated for current strong models.  ...  Recent years have seen numerous NLP datasets introduced to evaluate the performance of fine-tuned models on natural language understanding tasks.  ...  MRQA 2019 shared task: Evaluating generalization in reading comprehension. In Proceedings of the 2nd Work- shop on Machine Reading for Question Answering, pages 1-13, Hong Kong, China.  ... 
arXiv:2106.00840v1 fatcat:holqdsprhbb5fhdzvp3dzltd7m
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