Learning to Ask: Neural Question Generation for Reading Comprehension
Xinya Du,Junru Shao,Claire Cardie +2 more
- Vol. 1, pp 1342-1352
TLDR
This paper proposed an attention-based sequence learning model for question generation from text passages in reading comprehension, which is trainable end-to-end via sequence-tosequence learning and significantly outperforms the state-of-the-art rule-based system.Abstract:
We study automatic question generation for sentences from text passages in reading comprehension. We introduce an attention-based sequence learning model for the task and investigate the effect of encoding sentence- vs. paragraph-level information. In contrast to all previous work, our model does not rely on hand-crafted rules or a sophisticated NLP pipeline; it is instead trainable end-to-end via sequence-to-sequence learning. Automatic evaluation results show that our system significantly outperforms the state-of-the-art rule-based system. In human evaluations, questions generated by our system are also rated as being more natural (i.e.,, grammaticality, fluency) and as more difficult to answer (in terms of syntactic and lexical divergence from the original text and reasoning needed to answer).read more
Citations
More filters
Posted Content
Learning to Ask Unanswerable Questions for Machine Reading Comprehension
TL;DR: The authors proposed a data augmentation technique by automatically generating relevant unanswerable questions according to an answerable question paired with its corresponding paragraph that contains the answer, which effectively captures the interactions between the question and the paragraph.
Posted Content
EQG-RACE: Examination-Type Question Generation
TL;DR: This paper proposes an innovative Examination-type Question Generation approach (EQG-RACE) to generate exam-like questions based on a dataset extracted from RACE, and establishes a new QG prototype with a reshaped dataset and QG method, which provides an important benchmark for related research in future work.
Posted Content
Let's Ask Again: Refine Network for Automatic Question Generation.
Preksha Nema,Akash Kumar Mohankumar,Mitesh M. Khapra,Balaji Vasan Srinivasan,Balaraman Ravindran +4 more
TL;DR: This work proposes Refine Network (RefNet), a method which tries to mimic the human process of generating questions by first creating an initial draft and then refining it, and outperforms existing state-of-the-art methods by 7-16% on all of these datasets.
Posted Content
FFCI: A Framework for Interpretable Automatic Evaluation of Summarization.
TL;DR: This paper constructs a novel dataset for focus, coverage, and inter-sentential coherence, and develops automatic methods for evaluating each of the four dimensions of FFCI based on cross-comparison of evaluation metrics and model-based evaluation methods.
Posted Content
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems
Siamak Shakeri,Cicero Nogueira dos Santos,Henghui Zhu,Patrick Ng,Feng Nan,Zhiguo Wang,Ramesh Nallapati,Bing Xiang +7 more
TL;DR: This article proposed an end-to-end approach for synthetic QA data generation, which consists of a single transformer-based encoder-decoder network that is trained end to end to generate both answers and questions.
References
More filters
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Proceedings ArticleDOI
Glove: Global Vectors for Word Representation
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Proceedings ArticleDOI
Bleu: a Method for Automatic Evaluation of Machine Translation
TL;DR: This paper proposed a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run.
Proceedings Article
Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
Proceedings ArticleDOI
Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation
Kyunghyun Cho,Bart van Merriënboer,Caglar Gulcehre,Dzmitry Bahdanau,Fethi Bougares,Holger Schwenk,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +8 more
TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.