Towards Explainable Medical Machine Reading Comprehension With Rationale Generation

Dialogue state tracking (DST) is at the heart of task-oriented dialogue systems. However, the scarcity of labeled data is an obstacle to building accurate and robust state tracking systems that work across a variety of domains. Existing approaches generally require some dialogue data with state information and their ability to generalize to unknown domains is limited. In this paper, we propose using machine reading comprehension (RC) in state tracking from two perspectives: model architectures and datasets. We divide the slot types in dialogue state into categorical or extractive to borrow the advantages from both multiple-choice and span-based reading comprehension models. Our method achieves near the current state-of-the-art in joint goal accuracy on MultiWOZ 2.1 given full training data. More importantly, by leveraging machine reading comprehension datasets, our method outperforms the existing approaches by many a large margin in few-shot scenarios when the availability of in-domain data is limited. Lastly, even without any state tracking data, i.e., zero-shot scenario, our proposed approach achieves greater than 90% average slot accuracy in 12 out of 30 slots in MultiWOZ 2.1.

Machine Reading Comprehension (MRC) is a challenging task in Natural Language Processing (NLP) field. It is a fast evolving field in the recent years because of the development of large-scale datasets and the deep learning technology. Even though, numerous MRC models have been developed for Question Answer (QA) system, the existing MRC models are not on par with reading comprehension. In order to assess and improve the performance of the projected datasets, an advanced level of evaluation metrics have been introduced. Therefore, this work has conducted a survey over the existing MRC models and evaluation metrics utilized in them to address the lack of MRC with human reading.

The question generation task can automatically generate large-scale questions to provide training data for reading comprehension tasks and QA systems, which are crucial for low-resource languages such as Tibetan. At present, due to the emergence of large-scale datasets and pre-trained language models in the Chinese and English domains, the task of question generation in the Chinese and English domains has been well developed, while the research on question generation in the Tibetan is still in its infancy. The main reason is the lack of datasets and the relatively backward development of various models in Tibetan. To solve the above questions, this paper constructs a Tibetan pre-trained language model TiBERT to provide a basis for the development of various downstream tasks, to expand the datasets of Tibetan machine reading comprehension, this paper proposes a Tibetan question generation model named TQGR. The model consists of two parts, the question generation and question quality assessment. The question generation adopts the classic seq2seq architecture to generate questions, and the question quality assessment improves the quality of generated questions by evaluating the fluency reward score, word repetition rate reward score and interrogative words classification auxiliary task. Finally, the experimental results show that our model has higher performance than baseline models, and ablation experiments demonstrate the effectiveness of the three mechanism.

The essence of "Internet plus" is the relationship and its intelligent connection mode, which promotes the cross-border integration of information communication technology with all walks of life and reduces the information asymmetry. At a time when so much new knowledge is generated every day in the form of text that no one can digest on their own, machine reading comprehension can process vast amounts of information for people to use. With the development of psychometric theory and network technology, computerized adaptive testing has more and more advantages than traditional testing. Most of the current selection strategies are designed based on project response theory and cognitive diagnosis theory, which cannot point out students' learning ability, while cognitive diagnosis can not only evaluate students' ability, but also point out the knowledge of the subjects. This article tries to explore the application of adaptive machine reading comprehension in the measurement system, and build a machine reading comprehension model, introduces the application value of the machine in reading comprehension, secondly to build an adaptive measurement system can be used to machine reading model, finally attempts to solve through the experiment measurement system of text slices and accurately identify the problem.

Machine Reading Comprehension (MRC) is a challenging Question - Answering (QA) task that helps the user in providing the answer to the given question. There is a lot of progress in this area due to the availability of large datasets and large pre-trained language models based on transformer architecture (BERT). Named Entity Recognition (NER) was used for neural QA systems to improve performance. However, whether NER plays a vital role in a QA system built using contextual embeddings obtained through BERT variants is not explored. To fill this gap, we investigate whether NER is helpful in improving the performance of QA systems built using BERT variants. We experimented with Squad 2.0 using SpanBERT. The Squad 2.0 dataset has both answerable and unanswerable questions. The proposed model finds the answer span if the question is answerable and, provides justification for the unanswerable questions. We perform question analysis to find the expected answer tag and then use that information to find the relevant parts of the passage in order to retrieve the answer span.

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 be diverse enough: multi-choice MRC requires model to highlight and integrate all potential critical evidence globally; while extractive MRC focuses on higher local boundary preciseness for answer extraction. Among previous works, there lacks a unified design with pertinence for the overall discriminative MRC tasks. To fill in above gap, we propose a lightweight POS-Enhanced Iterative Co-Attention Network (POI-Net) as the first attempt of unified modeling with pertinence, to handle diverse discriminative MRC tasks synchronously. Nearly without introducing more parameters, our lite unified design brings model significant improvement with both encoder and decoder components. The evaluation results on four discriminative MRC benchmarks consistently indicate the general effectiveness and applicability of our model, and the code is available at https://github.com/Yilin1111/poi-net.

Despite the remarkable progress on Machine Reading Comprehension (MRC) with the help of open-source datasets, recent studies indicate that most of the current MRC systems unfortunately suffer from weak robustness against adversarial samples. To address this issue, we attempt to take sentence syntax as the leverage in the answer predicting process which previously only takes account of phrase-level semantics. Furthermore, to better utilize the sentence syntax and improve the robustness, we propose a Syntactic Leveraging Network, which is designed to deal with adversarial samples by exploiting the syntactic elements of a question. The experiment results indicate that our method is promising for improving the generalization and robustness of MRC models against the influence of adversarial samples, with performance well-maintained.

Machine Reading Comprehension (MRC) is the process where computers or, machines are taught to understand a paragraph or more technically called a context. Like humans, machines also need to be evaluated for their understanding on question answering. MRC is one of the formidable sub-domains in the Natural Language Processing (NLP) domain, which has seen considerable progress over the years. In recent years, many novel datasets have tried to challenge the Machine Reading Comprehension (MRC) models with inference based question answering. With the advancement in NLP, many models have surpassed human-level performance on these datasets, albeit ignoring the obvious disparity between genuine human-level performance and state-of-the-art performance. This highlights the need for attention on the collective improvement of existing datasets, metrics, and models towards “real” prehension. Addressing the lack of sanity in the domain, this paper performs a comparative study on various transformer based models and tries to highlight the success factors of each model. Subsequently, we discuss an MRC model that performs comparatively better, if not the best, on question answering and give directions for future research.KeywordsTransformerNatural language processingReading comprehensionQuestion answeringDeep learning

This paper presents a system of large-scale language model adaptation for daily generated big-size text corpus using MapReduce in a cloud environment. Our large-scale trigram language model, consis...

Reading comprehension is one of the most critical abilities in human intelligence, and machine reading comprehension is one of the most popular research directions in the field of natural language processing. Machine reading is one of the key aspects to achieve artificial intelligence. This paper first analyzes the current state of development of machine reading comprehension, and based on the current problems in machine reading comprehension, this paper applies various attention mechanisms in the reading comprehension model, so that the information of articles and questions can be fused with each other to extract the key information and predict the answers more accurately. This paper also designs experiments and verifies that this method has a higher accuracy rate compared with other methods.

Retrieval-Augmented Language Models (RALMs) have significantly improved performance in open-domain question answering (QA) by leveraging external knowledge.However, RALMs still struggle with unanswerable queries, where the retrieved contexts do not contain the correct answer, and with conflicting information, where different sources provide contradictory answers due to imperfect retrieval.This study introduces an in-context learning-based approach to enhance the reasoning capabilities of RALMs, making them more robust in imperfect retrieval scenarios.Our method incorporates Machine Reading Comprehension (MRC) demonstrations, referred to as cases, to boost the model's capabilities to identify unanswerabilities and conflicts among the retrieved contexts.Experiments on two open-domain QA datasets show that our approach increases accuracy in identifying unanswerable and conflicting scenarios without requiring additional fine-tuning.This work demonstrates that in-context learning can effectively enhance the robustness of RALMs in open-domain QA tasks.

BackgroundExtractive methods for machine reading comprehension (MRC) tasks have achieved comparable or better accuracy than human performance on benchmark data sets. However, such models are not as successful when adapted to complex domains such as health care. One of the main reasons is that the context that the MRC model needs to process when operating in a complex domain can be much larger compared with an average open-domain context. This causes the MRC model to make less accurate and slower predictions. A potential solution to this problem is to reduce the input context of the MRC model by extracting only the necessary parts from the original context.ObjectiveThis study aims to develop a method for extracting useful contexts from long articles as an additional component to the question answering task, enabling the MRC model to work more efficiently and accurately.MethodsExisting approaches to context extraction in MRC are based on sentence selection strategies, in which the models are trained to find the sentences containing the answer. We found that using only the sentences containing the answer was insufficient for the MRC model to predict correctly. We conducted a series of empirical studies and observed a strong relationship between the usefulness of the context and the confidence score output of the MRC model. Our investigation showed that a precise input context can boost the prediction correctness of the MRC and greatly reduce inference time. We proposed a method to estimate the utility of each sentence in a context in answering the question and then extract a new, shorter context according to these estimations. We generated a data set to train 2 models for estimating sentence utility, based on which we selected more precise contexts that improved the MRC model’s performance.ResultsWe demonstrated our approach on the Question Answering Data Set for COVID-19 and Biomedical Semantic Indexing and Question Answering data sets and showed that the approach benefits the downstream MRC model. First, the method substantially reduced the inference time of the entire question answering system by 6 to 7 times. Second, our approach helped the MRC model predict the answer more correctly compared with using the original context (F1-score increased from 0.724 to 0.744 for the Question Answering Data Set for COVID-19 and from 0.651 to 0.704 for the Biomedical Semantic Indexing and Question Answering). We also found a potential problem where extractive transformer MRC models predict poorly despite being given a more precise context in some cases.ConclusionsThe proposed context extraction method allows the MRC model to achieve improved prediction correctness and a significantly reduced MRC inference time. This approach works technically with any MRC model and has potential in tasks involving processing long texts.

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. While many datasets have been developed for MRC research for other languages, there is a lack of such resources for the Vietnamese language. Although many datasets and methodologies have been developed for English and Chinese, many Vietnamese machine reading comprehension limitations need to be solved further. Existing Vietnamese datasets for MRC research concentrate solely on answerable questions. However, in reality, questions can be unanswerable for which the correct answer is not stated in the given textual data. To address the weakness, we provide the research community with a benchmark dataset named UIT-ViQuAD 2.0 for evaluating the MRC task and question answering systems for the Vietnamese language. We use UIT-ViQuAD 2.0 as a benchmark dataset for the shared task on Vietnamese machine reading comprehension (VLSP2021-MRC) at the Eighth Workshop on Vietnamese Language and Speech Processing (VLSP 2021). This task attracted 77 participant teams from 34 universities and other organizations. Each participant was provided with the training data, including 28,457 annotated question-answer pairs, and returned the result on a public test set of more than 3,821 questions and a private test set of 3,712 questions. In this article, we present details of the organization of the shared task, an overview of the methods employed by shared-task participants, and the results. The highest performances in this competition are 77.24% (in EM) and 67.43% (in F1-score) on the private test set. The Vietnamese MRC systems proposed by the top 3 teams use XLM-RoBERTa, a powerful pre-trained language model based on the transformer architecture that has achieved state-of-the-art results on many natural language processing tasks. We believe that releasing the UIT-ViQuAD 2.0 dataset motivates more researchers to improve Vietnamese machine reading comprehension.

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.

The success of large-scale contextual language models has attracted great interest in probing what is encoded in their representations. In this work, we consider a new question: to what extent contextual representations of concrete nouns are aligned with corresponding visual representations? We design a probing model that evaluates how effective are text-only representations in distinguishing between matching and non-matching visual representations. Our findings show that language representations alone provide a strong signal for retrieving image patches from the correct object categories. Moreover, they are effective in retrieving specific instances of image patches; textual context plays an important role in this process. Visually grounded language models slightly outperform text-only language models in instance retrieval, but greatly under-perform humans. We hope our analyses inspire future research in understanding and improving the visual capabilities of language models.