Reading Comprehension

AbstractThis paper proposes evaluation methods for reading comprehension of English to Japanese translation outputs. The methods were designed not only to evaluate the performance of current systems, but to evaluate …...

In this paper, we report results on answering questions for the reading comprehension task, using a machine learning approach. We eval- uated our approach on the Remedia data set, a …...

In this paper we describe a method for analyzing the reading comprehension task. First, we describe a method of classifying facts (information) into cat- egories or levels; where each level …...

A reading comprehension (RC) system attempts to understand a document and returns an answer sentence when posed with a question. RC resembles the ad hoc question answering (QA) task that …...

AbstractThis paper describes a methodology for testing and evaluating the performance of Machine Reading systems through Question Answering and Reading Comprehension Tests. The methodology is being used in QA4MRE (QA …...

We introduce a method for learning to align sentences in monolingual parallel articles for text simplification. In our approach, word keyness is integrated to prefer aligning essential words in sentences. …...

Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this pa- per, we focus on …...

This paper presents our investigation of the ability of 33 readability indices to ac- count for the reading comprehension dif- ficulty posed by texts for people with autism. The evaluation …...

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 …...

AbstractEmotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances …...

AbstractKnowing the quality of reading comprehension (RC) datasets is important for the development of natural-language understanding systems. In this study, two classes of metrics were adopted for evaluating RC datasets: …...

AbstractHumans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading comprehension systems that can do the same. In this paper, …...

AbstractWe show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot. This reduction has several advantages: …...

AbstractReading Comprehension (RC) of text is one of the fundamental tasks in natural language processing. In recent years, several end-to-end neural network models have been proposed to solve RC tasks. …...

AbstractThis paper reports on a pilot experiment that compares two different machine translation (MT) paradigms in reading comprehension tests. To explore a suitable methodology, we set up a pilot experiment …...

AbstractComplex questions in reading comprehension tasks require integrating information from multiple sentences. In this work, to answer such questions involving temporal and causal relations, we generate event graphs from text …...

AbstractReading comprehension (RC)—in contrast to information retrieval—requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, …...

AbstractThis shared task is a typical question answering task. Compared with the normal question and answer system, it needs to give the answer to the question based on the text …...

AbstractReading comprehension has recently seen rapid progress, with systems matching humans on the most popular datasets for the task. However, a large body of work has highlighted the brittleness of …...

AbstractScenario-based question answering (SQA) has attracted increasing research attention. It typically requires retrieving and integrating knowledge from multiple sources, and applying general knowledge to a specific case described by a …...

AbstractReading comprehension models have been successfully applied to extractive text answers, but it is unclear how best to generalize these models to abstractive numerical answers. We enable a BERT-based reading …...

AbstractOpen-domain question answering remains a challenging task as it requires models that are capable of understanding questions and answers, collecting useful information, and reasoning over evidence. Previous work typically formulates …...

AbstractMany Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources. While this is easy for humans, the present …...

AbstractStandard accuracy metrics indicate that modern reading comprehension systems have achieved strong performance in many question answering datasets. However, the extent these systems truly understand language remains unknown, and existing …...

AbstractRecent work in the field of automatic summarization and headline generation focuses on maximizing ROUGE scores for various news datasets. We present an alternative, extrinsic, evaluation metric for this task, …...

AbstractMachine comprehension of texts longer than a single sentence often requires coreference resolution. However, most current reading comprehension benchmarks do not contain complex coreferential phenomena and hence fail to evaluate …...

AbstractModels for reading comprehension (RC) commonly restrict their output space to the set of all single contiguous spans from the input, in order to alleviate the learning problem and avoid …...

AbstractInnovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the …...

AbstractIn this work, we analyze how human gaze during reading comprehension is conditioned on the given reading comprehension question, and whether this signal can be beneficial for machine reading comprehension. …...

AbstractBuilding general reading comprehension systems, capable of solving multiple datasets at the same time, is a recent aspirational goal in the research community. Prior work has focused on model architecture …...

AbstractDespite achieving prominent performance on many important tasks, it has been reported that neural networks are vulnerable to adversarial examples. Previously studies along this line mainly focused on semantic tasks …...

AbstractThe release of BERT revolutionized the development of NLP. Various BERT-based reading comprehension models have been proposed, thus updating the performance ranking of reading comprehension tasks. However, the above BERT-based …...

AbstractPosing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation …...

AbstractReading comprehension (RC) on social media such as Twitter is a critical and challenging task due to its noisy, informal, but informative nature. Most existing RC models are developed on …...

AbstractRecent studies have revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets. This prevents the community from reliably measuring the progress of …...

AbstractWe find that the performance of state-of-the-art models on Natural Language Inference (NLI) and Reading Comprehension (RC) analysis/stress sets can be highly unstable. This raises three questions: (1) How will …...

AbstractWhen a model attribution technique highlights a particular part of the input, a user might understand this highlight as making a statement about counterfactuals (Miller, 2019): if that part of …...

AbstractUnderstanding how events are semantically related to each other is the essence of reading comprehension. Recent event-centric reading comprehension datasets focus mostly on event arguments or temporal relations. While these …...

AbstractRecent work on unsupervised question answering has shown that models can be trained with procedurally generated question-answer pairs and can achieve performance competitive with supervised methods. In this work, we …...

AbstractMany datasets have been created for training reading comprehension models, and a natural question is whether we can combine them to build models that (1) perform better on all of …...

AbstractWhile there is increasing concern about the interpretability of neural models, the evaluation of interpretability remains an open problem, due to the lack of proper evaluation datasets and metrics. In …...

AbstractRecent efforts to create challenge benchmarks that test the abilities of natural language understanding models have largely depended on human annotations. In this work, we introduce the “Break, Perturb, Build” …...

AbstractDespite the success of state-of-the-art pre-trained language models (PLMs) on a series of multi-hop reasoning tasks, they still suffer from their limited abilities to transfer learning from simple to complex …...

AbstractDiscriminative pre-trained language models (PrLMs) can be generalized as denoising auto-encoders that work with two procedures, ennoising and denoising. First, an ennoising process corrupts texts with arbitrary noising functions to …...

AbstractTwo of the most fundamental issues in Natural Language Understanding (NLU) at present are: (a) how it can established whether deep learning-based models score highly on NLU benchmarks for the …...

AbstractFor a natural language understanding benchmark to be useful in research, it has to consist of examples that are diverse and difficult enough to discriminate among current and near-future state-of-the-art …...

AbstractIn the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks such as SuperGLUE and …...