Papers Machine Reading Comprehension
“Machine Reading Comprehension” 태그가 달린 논문 562편 · 필터 해제
Data Augmentation for Biomedical Factoid Question Answering
We study the effect of seven data augmentation (da) methods in factoid question answering, focusing on the biomedical domain, where obtaining training instances is particularly difficult. We experiment with data from the…
Data AugmentationInformation RetrievalLanguage ModelingLanguage Modelling+8Improving Zero-Shot Event Extraction via Sentence Simplification
The success of sites such as ACLED and Our World in Data have demonstrated the massive utility of extracting events in structured formats from large volumes of textual data in the form of news, social media, blogs and di…
Event Argument ExtractionEvent ExtractionExtractive Question-AnsweringMachine Reading Comprehension+4Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension
Multilingual pre-trained models are able to zero-shot transfer knowledge from rich-resource to low-resource languages in machine reading comprehension (MRC). However, inherent linguistic discrepancies in different langua…
Cross-Lingual TransferDisentanglementMachine Reading ComprehensionReading Comprehension+1Clozer: Adaptable Data Augmentation for Cloze-style Reading Comprehension
Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and provides performance lift by adapting unlabelled data to downstream task. Unfortunately, existing adaptations mainly involve deterministic rules …
Data AugmentationMachine Reading ComprehensionMultiple-choiceReading ComprehensionLite Unified Modeling for Discriminative Reading Comprehension
As a broad and major category in machine reading comprehension (MRC), the generalized goal of discriminative MRC is answer prediction from the given materials. However, the focuses of various discriminative MRC tasks may…
DecoderMachine Reading ComprehensionMulti-Choice MRCPOS+1VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension
One of the emerging research trends in natural language understanding is machine reading comprehension (MRC) which is the task to find answers to human questions based on textual data. Existing Vietnamese datasets for MR…
Language ModellingMachine Reading ComprehensionNatural Language InferenceNatural Language Understanding+6AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension
Recent machine reading comprehension datasets such as ReClor and LogiQA require performing logical reasoning over text. Conventional neural models are insufficient for logical reasoning, while symbolic reasoners cannot d…
Logical ReasoningMachine Reading ComprehensionMultiple-choiceReading ComprehensionFeeding What You Need by Understanding What You Learned
Machine Reading Comprehension (MRC) reveals the ability to understand a given text passage and answer questions based on it. Existing research works in MRC rely heavily on large-size models and corpus to improve the perf…
Machine Reading ComprehensionReading ComprehensionBioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task
Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural ne…
Domain AdaptationMachine Reading ComprehensionReading ComprehensionTransfer LearningRelational Surrogate Loss Learning
Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e.g., average precision and F1 score. This paper aims to address this problem by …
image-classificationImage ClassificationMachine Reading ComprehensionMachine Translation+3Deep Understanding based Multi-Document Machine Reading Comprehension
Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings. First, to under…
Machine Reading ComprehensionReading ComprehensionTriviaQAPretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words
The standard BERT adopts subword-based tokenization, which may break a word into two or more wordpieces (e.g., converting "lossless" to "loss" and "less"). This will bring inconvenience in following situations: (1) what …
ChunkingCloze TestMachine Reading ComprehensionNatural Language Understanding+4Using calibrator to improve robustness in Machine Reading Comprehension
Machine Reading Comprehension(MRC) has achieved a remarkable result since some powerful models, such as BERT, are proposed. However, these models are not robust enough and vulnerable to adversarial input perturbation and…
Machine Reading ComprehensionReading ComprehensionRepresentation LearningFedQAS: Privacy-aware machine reading comprehension with federated learning
Machine reading comprehension (MRC) of text data is one important task in Natural Language Understanding. It is a complex NLP problem with a lot of ongoing research fueled by the release of the Stanford Question Answerin…
Conversational Question AnsweringFederated LearningMachine Reading ComprehensionNatural Language Understanding+3JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension
Question Answering (QA) is a task in which a machine understands a given document and a question to find an answer. Despite impressive progress in the NLP area, QA is still a challenging problem, especially for non-Engli…
ArticlesMachine Reading ComprehensionQuestion AnsweringReading ComprehensionA Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension
Although great progress has been made for Machine Reading Comprehension (MRC) in English, scaling out to a large number of languages remains a huge challenge due to the lack of large amounts of annotated training data in…
graph constructionMachine Reading ComprehensionMachine TranslationReading ComprehensionCooperative Self-training of Machine Reading Comprehension
Pretrained language models have significantly improved the performance of downstream language understanding tasks, including extractive question answering, by providing high-quality contextualized word embeddings. Howeve…
Extractive Question-AnsweringMachine Reading ComprehensionQuestion AnsweringQuestion Generation+4Data Augmentation for Biomedical Factoid Question Answering
We study the effect of seven data augmentation (DA) methods in factoid question answering, focusing on the biomedical domain, where obtaining training instances is particularly difficult. We experiment with data from the…
Data AugmentationInformation RetrievalLanguage ModelingLanguage Modelling+8An MRC Framework for Semantic Role Labeling
Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two subtasks: predicate disambiguation and argument labeling. Prior work deals with these two tas…
Computational EfficiencyMachine Reading ComprehensionMultiple-choiceReading Comprehension+2Event Detection via Derangement Reading Comprehension
Event detection (ED), aiming to detect events from texts and categorize them, is vital to understanding the actual happenings in real life. Recently, ED without triggers has been proposed and gained benefits since it re…
Event DetectionMachine Reading ComprehensionReading Comprehension