Machine Comprehension

task ID: machinecomprehension

Related Papers

A Strong Lexical Matching Method for the Machine Comprehension Test

Authors: nan

Publication: TIPSTER (2015)

DOI: nan

Machine comprehension of text is the overarching goal of a great deal of re- search in natural language processing. The Machine Comprehension Test (Richard- son et al., 2013) was recently …...

34433

Attention-Based Convolutional Neural Network for Machine Comprehension

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

Understanding open-domain text is one of the primary challenges in NLP. Machine com- prehension benchmarks evaluate a system’s ability to understand text based on the text content only. In this …...

27326

Consensus Attention-based Neural Networks for Chinese Reading Comprehension

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

AbstractReading comprehension has embraced a booming in recent NLP research. Several institutes have released the Cloze-style reading comprehension data, and these have greatly accelerated the research of machine comprehension. In …...

4449

Natural Language Comprehension with the EpiReader

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

We present EpiReader, a novel model for ma- chine comprehension of text. Machine com- prehension of unstructured, real-world text is a major research goal for natural language pro- cessing. Current …...

33713

Reading Wikipedia to Answer Open-Domain Questions

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThis paper proposes to tackle open-domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article. This …...

36991

Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractWe develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network. Given a high performing MC model in one domain, our technique aims to …...

33043

ELiRF-UPV at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractThis paper describes the participation of ELiRF-UPV team at task 11, Machine Comprehension using Commonsense Knowledge, of SemEval-2018. Our approach is based on the use of word embeddings, NumberBatch Embeddings, …...

25953

Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractThis paper describes our submission for the International Workshop on Semantic Evaluation (SemEval-2018) shared task 11– Machine Comprehension using Commonsense Knowledge (Ostermann et al., 2018b). We use a deep neural …...

25947

MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

We introduce a large dataset of narrative texts and questions about these texts, intended to be used in a machine comprehension task that requires reasoning using commonsense knowledge. Our dataset …...

17456

Ruminating Reader: Reasoning with Gated Multi-hop Attention

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractTo answer the question in machine comprehension (MC) task, the models need to establish the interaction between the question and the context. To tackle the problem that the single-pass model …...

40676

SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractThis report summarizes the results of the SemEval 2018 task on machine comprehension using commonsense knowledge. For this machine comprehension task, we created a new corpus, MCScript. It contains a …...

26005

Augmenting Neural Networks with First-order Logic

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractToday, the dominant paradigm for training neural networks involves minimizing task loss on a large dataset. Using world knowledge to inform a model, and yet retain the ability to perform …...

39928

Have You Seen That Number? Investigating Extrapolation in Question Answering Models

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractNumerical reasoning in machine reading comprehension (MRC) has shown drastic improvements over the past few years. While the previous models for numerical MRC are able to interpolate the learned numerical …...

31427