scholarly journals Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction

2020 ◽  
Author(s):  
Tianyang Zhao ◽  
Zhao Yan ◽  
Yunbo Cao ◽  
Zhoujun Li
Keyword(s):  
Reading Comprehension ◽  
Question Answering ◽  
Relation Extraction ◽  
Effective Solution ◽  
Joint Learning ◽  
Selection Strategies ◽  
Entity Relation Extraction ◽  
Extraction Model ◽  
Machine Reading ◽  
Single Question

Recent advances cast the entity-relation extraction to a multi-turn question answering (QA) task and provide an effective solution based on the machine reading comprehension (MRC) models. However, they use a single question to characterize the meaning of entities and relations, which is intuitively not enough because of the variety of context semantics. Meanwhile, existing models enumerate all relation types to generate questions, which is inefficient and easily leads to confusing questions. In this paper, we improve the existing MRC-based entity-relation extraction model through diverse question answering. First, a diversity question answering mechanism is introduced to detect entity spans and two answering selection strategies are designed to integrate different answers. Then, we propose to predict a subset of potential relations and filter out irrelevant ones to generate questions effectively. Finally, entity and relation extractions are integrated in an end-to-end way and optimized through joint learning. Experiment results show that the proposed method significantly outperforms baseline models, which improves the relation F1 to 62.1% (+1.9%) on ACE05 and 71.9% (+3.0%) on CoNLL04. Our implementation is available at https://github.com/TanyaZhao/MRC4ERE.

scholarly journals Analyzing the Effect of Masking Length Distribution of MLM: An Evaluation Framework and Case Study on Chinese MRC Datasets

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Changchang Zeng ◽  
Shaobo Li
Keyword(s):  
Reading Comprehension ◽  
Language Processing ◽  
Question Answering ◽  
Multiple Choice ◽  
Length Distribution ◽  
Research Field ◽  
Evaluation Framework ◽  
Language Models ◽  
Training Objective ◽  
Machine Reading

Machine reading comprehension (MRC) is a challenging natural language processing (NLP) task. It has a wide application potential in the fields of question answering robots, human-computer interactions in mobile virtual reality systems, etc. Recently, the emergence of pretrained models (PTMs) has brought this research field into a new era, in which the training objective plays a key role. The masked language model (MLM) is a self-supervised training objective widely used in various PTMs. With the development of training objectives, many variants of MLM have been proposed, such as whole word masking, entity masking, phrase masking, and span masking. In different MLMs, the length of the masked tokens is different. Similarly, in different machine reading comprehension tasks, the length of the answer is also different, and the answer is often a word, phrase, or sentence. Thus, in MRC tasks with different answer lengths, whether the length of MLM is related to performance is a question worth studying. If this hypothesis is true, it can guide us on how to pretrain the MLM with a relatively suitable mask length distribution for MRC tasks. In this paper, we try to uncover how much of MLM’s success in the machine reading comprehension tasks comes from the correlation between masking length distribution and answer length in the MRC dataset. In order to address this issue, herein, (1) we propose four MRC tasks with different answer length distributions, namely, the short span extraction task, long span extraction task, short multiple-choice cloze task, and long multiple-choice cloze task; (2) four Chinese MRC datasets are created for these tasks; (3) we also have pretrained four masked language models according to the answer length distributions of these datasets; and (4) ablation experiments are conducted on the datasets to verify our hypothesis. The experimental results demonstrate that our hypothesis is true. On four different machine reading comprehension datasets, the performance of the model with correlation length distribution surpasses the model without correlation.

scholarly journals Importance of the Single-Span Task Formulation to Extractive Question-answering

2020 ◽  
Author(s):  
Marie-Anne Xu ◽  
Rahul Khanna
Keyword(s):  
Reading Comprehension ◽  
Future Development ◽  
Recent Progress ◽  
Question Answering ◽  
Span Task ◽  
Consistent Performance ◽  
Machine Reading

Recent progress in machine reading comprehension and question-answering has allowed machines to reach and even surpass human question-answering. However, the majority of these questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset (21.492% F1) compared to the single-span source datasets (~33.36% F1). While the models tested on the source datasets were slightly fine-tuned, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in questionanswering and improve existing question-answering products and methods.

NA-Aware Machine Reading Comprehension for Document-Level Relation Extraction

2021 ◽  
pp. 580-595
Author(s):  
Zhenyu Zhang ◽  
Bowen Yu ◽  
Xiaobo Shu ◽  
Tingwen Liu
Keyword(s):  
Reading Comprehension ◽  
Relation Extraction ◽  
Machine Reading ◽  
Document Level

scholarly journals Rception: Wide and Deep Interaction Networks for Machine Reading Comprehension (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13987-13988
Author(s):  
Xuanyu Zhang ◽  
Zhichun Wang
Keyword(s):  
Neural Networks ◽  
Reading Comprehension ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Question Answering ◽  
Local Information ◽  
Interaction Networks ◽  
Layer By Layer ◽  
Global Context ◽  
Machine Reading

Most of models for machine reading comprehension (MRC) usually focus on recurrent neural networks (RNNs) and attention mechanism, though convolutional neural networks (CNNs) are also involved for time efficiency. However, little attention has been paid to leverage CNNs and RNNs in MRC. For a deeper understanding, humans sometimes need local information for short phrases, sometimes need global context for long passages. In this paper, we propose a novel architecture, i.e., Rception, to capture and leverage both local deep information and global wide context. It fuses different kinds of networks and hyper-parameters horizontally rather than simply stacking them layer by layer vertically. Experiments on the Stanford Question Answering Dataset (SQuAD) show that our proposed architecture achieves good performance.

scholarly journals UQuAD1.0: Development of an Urdu Question Answering Training Data for Machine Reading Comprehension

2021 ◽  
Author(s):  
Samreen Ahmed ◽  
shakeel khoja
Keyword(s):  
Reading Comprehension ◽  
Machine Translation ◽  
Large Scale ◽  
Question Answering ◽  
Training Data ◽  
Significant Progress ◽  
Rule Based ◽  
Low Resource ◽  
Machine Reading ◽  
Answer Format

<p>In recent years, low-resource Machine Reading Comprehension (MRC) has made significant progress, with models getting remarkable performance on various language datasets. However, none of these models have been customized for the Urdu language. This work explores the semi-automated creation of the Urdu Question Answering Dataset (UQuAD1.0) by combining machine-translated SQuAD with human-generated samples derived from Wikipedia articles and Urdu RC worksheets from Cambridge O-level books. UQuAD1.0 is a large-scale Urdu dataset intended for extractive machine reading comprehension tasks consisting of 49k question Answers pairs in question, passage, and answer format. In UQuAD1.0, 45000 pairs of QA were generated by machine translation of the original SQuAD1.0 and approximately 4000 pairs via crowdsourcing. In this study, we used two types of MRC models: rule-based baseline and advanced Transformer-based models. However, we have discovered that the latter outperforms the others; thus, we have decided to concentrate solely on Transformer-based architectures. Using XLMRoBERTa and multi-lingual BERT, we acquire an F<sub>1</sub> score of 0.66 and 0.63, respectively.</p>

scholarly journals MMM: Multi-Stage Multi-Task Learning for Multi-Choice Reading Comprehension

2020 ◽  
Vol 34 (05) ◽  
pp. 8010-8017 ◽  
Author(s):  
Di Jin ◽  
Shuyang Gao ◽  
Jiun-Yu Kao ◽  
Tagyoung Chung ◽  
Dilek Hakkani-tur
Keyword(s):  
Reading Comprehension ◽  
Question Answering ◽  
Limited Data ◽  
Learning Stage ◽  
Learning Framework ◽  
Task Learning ◽  
Multi Stage ◽  
Comprehension Skills ◽  
Machine Reading ◽  
Reading Comprehension Skills

Machine Reading Comprehension (MRC) for question answering (QA), which aims to answer a question given the relevant context passages, is an important way to test the ability of intelligence systems to understand human language. Multiple-Choice QA (MCQA) is one of the most difficult tasks in MRC because it often requires more advanced reading comprehension skills such as logical reasoning, summarization, and arithmetic operations, compared to the extractive counterpart where answers are usually spans of text within given passages. Moreover, most existing MCQA datasets are small in size, making the task even harder. We introduce MMM, a Multi-stage Multi-task learning framework for Multi-choice reading comprehension. Our method involves two sequential stages: coarse-tuning stage using out-of-domain datasets and multi-task learning stage using a larger in-domain dataset to help model generalize better with limited data. Furthermore, we propose a novel multi-step attention network (MAN) as the top-level classifier for this task. We demonstrate MMM significantly advances the state-of-the-art on four representative MCQA datasets.

Entity Relation Extraction Model Based Negative Feedback Attention

2021 ◽  
Author(s):  
Jun Oin ◽  
Liting Liao ◽  
Jing Liu ◽  
Weidong Li ◽  
Lu Liu ◽  
...  
Keyword(s):  
Negative Feedback ◽  
Relation Extraction ◽  
Model Based ◽  
Entity Relation Extraction ◽  
Extraction Model

scholarly journals A Deep Cascade Model for Multi-Document Reading Comprehension

2019 ◽  
Vol 33 ◽  
pp. 7354-7361 ◽  
Author(s):  
Ming Yan ◽  
Jiangnan Xia ◽  
Chen Wu ◽  
Bin Bi ◽  
Zhongzhou Zhao ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Large Scale ◽  
Question Answering ◽  
Answer Extraction ◽  
Benchmark Datasets ◽  
Previous State ◽  
Document Extraction ◽  
Machine Reading ◽  
System Effectiveness ◽  
Extraction Experiment

A fundamental trade-off between effectiveness and efficiency needs to be balanced when designing an online question answering system. Effectiveness comes from sophisticated functions such as extractive machine reading comprehension (MRC), while efficiency is obtained from improvements in preliminary retrieval components such as candidate document selection and paragraph ranking. Given the complexity of the real-world multi-document MRC scenario, it is difficult to jointly optimize both in an end-to-end system. To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the documentlevel and paragraph-level ranking of candidate texts to more precise answer extraction with machine reading comprehension. Specifically, irrelevant documents and paragraphs are first filtered out with simple functions for efficiency consideration. Then we jointly train three modules on the remaining texts for better tracking the answer: the document extraction, the paragraph extraction and the answer extraction. Experiment results show that the proposed method outperforms the previous state-of-the-art methods on two large-scale multidocument benchmark datasets, i.e., TriviaQA and DuReader. In addition, our online system can stably serve typical scenarios with millions of daily requests in less than 50ms.

scholarly journals A sui generis QA approach using RoBERTa for adverse drug event identification

2021 ◽  
Vol 22 (S11) ◽  
Author(s):  
Harshit Jain ◽  
Nishant Raj ◽  
Suyash Mishra
Keyword(s):  
Question Answering ◽  
Adverse Drug Events ◽  
Short Term Memory ◽  
Domain Adaptation ◽  
Relation Extraction ◽  
Biomedical Literature ◽  
Drug Event ◽  
Textual Data ◽  
Entity Relation Extraction ◽  
End To End

Abstract Background Extraction of adverse drug events from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around entity-relation extraction using bidirectional long short term memory networks (Bi-LSTM) which does not attain the best feature representations. Results In this paper, we introduce a question answering framework that exploits the robustness, masking and dynamic attention capabilities of RoBERTa by a technique of domain adaptation and attempt to overcome the aforementioned limitations. With formulation of an end-to-end pipeline, our model outperforms the prior work by 9.53% F1-Score. Conclusion An end-to-end pipeline that leverages state of the art transformer architecture in conjunction with QA approach can bolster the performances of entity-relation extraction tasks in the biomedical domain. In particular, we believe our research would be helpful in identification of potential adverse drug reactions in mono as well as combination therapy related textual data.

scholarly journals Evaluation of Single-Span Models on Extractive Multi-Span Question-Answering

2021 ◽  
Vol 12 (1) ◽  
pp. 19-29
Author(s):  
Marie-Anne Xu ◽  
Rahul Khanna
Keyword(s):  
Reading Comprehension ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Future Development ◽  
Question Answering ◽  
Consistent Performance ◽  
Machine Reading ◽  
Entire Dataset

Machine Reading Comprehension (MRC), particularly extractive close-domain question-answering, is a prominent field in Natural Language Processing (NLP). Given a question and a passage or set of passages, a machine must be able to extract the appropriate answer from the passage(s). However, the majority of these existing questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Runtime of base models on the entire dataset is approximately one day while the runtime for all models on a third of the dataset is a little over two days. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset compared to the single-span source datasets. While the models tested on the source datasets were slightly fine-tuned in order to return multiple answers, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in question-answering and improve existing question-answering products and methods.

Sign in / Sign up

Export Citation Format

Share Document