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Type-specific Multi-Head Shared-Encoder Model for Commonsense Machine Reading Comprehension
http://doi.org/10.5626/JOK.2023.50.5.376
Machine reading comprehension (MRC) is a task introduced to a machine that can understand natural languages by solving various tasks based on given context. To evaluate natural language understanding of machine, a machine must make commonsense inference under full comprehension of a given context. To enhance model obtaining such abilities, we proposed a multi-task learning scheme and a model for commonsense MRC. Contributions of this study are as follows: 1) a method of task-specific dataset configuration is proposed; 2) a type-specific multi-head shared-encoder model with multi-task learning scheme including batch sampling and loss scaling is developed; and 3) when the method is evaluated on CosmosQA dataset (commonsense MRC), the accuracy was improved by 2.38% compared to the performance at baseline with fine-tuning.
Korean Dependency Parsing using Subtree Linking based on Machine Reading Comprehension
Jinwoo Min, Seung-Hoon Na, Jong-Hoon Shin, Young-Kil Kim, Kangil Kim
http://doi.org/10.5626/JOK.2022.49.8.617
In Korean dependency parsing, biaffine attention models have shown state-of-the-art performances; they first obtain head-level and modifier-level representations by applying two multi-layer perceptrons (MLP) on the encoded contextualized word representation, perform the attention by regarding modifier-level representation as a query and head-level one as a key, and take the resulting attention score as a probability of forming a dependency arc between the corresponding two words. However, given two target words (i.e., candidate head and modifier), biaffine attention methods are basically limited to their word-level representations, not being aware of the explicit boundaries of their phrases or subtrees. Thus, without relying on semantically and syntactically enriched phrase-level and subtree-level representations, biaffine attention methods might be not effective in the case that determining a dependency arc is not simple but complicated such as identifying a dependency between “far-distant” words, where these cases may often require subtree or phrase-level information surrounding target words. To address this drawback, this paper presents the use of dependency paring framework based on machine reading comprehension (MRC) that explicitly utilizes the subtree-level information by mapping a given child subtree and its parent subtree to a question and an answer, respectively. The experiment results on standard datasets of Korean dependency parsing shows that the MRC-based dependency paring outperforms the biaffine attention model. In particular, the results further given observations that improvements in performances are likely strong in long sentences, comparing to short ones.
Training Data Augmentation Technique for Machine Comprehension by Question-Answer Pairs Generation Models based on a Pretrained Encoder-Decoder Model
http://doi.org/10.5626/JOK.2022.49.2.166
The goal of Machine Reading Comprehension (MRC) research is to find answers to questions in documents. MRC research requires large-scale, high-quality data. However, individual researchers or small research institutes have limitations in constructing them. To overcome the limitations, in this paper, we propose an MRC data augmentation technique using a pre-training language model. This MRC data augmentation technique consists of a Q&A pair generation model and a data validation model. The Q&A pair generation model consists of an answer extraction model and a question generation model. Both models are constructed by fine-tuning the BART model. The data validation model is added to increase the reliability of the augmented data. It is used to verify the generated augmented data. The validation model is used by fine-tuning the ELECTRA model as an MRC model. To see the performance improvement of the MRC model through the data augmentation technique, we applied the data augmentation technique to KorQuAD v1.0 data. As a result of the experiment, compared to the previous model, the Exact Match(EM) Score increased up to 7.2 and the F1 Score increased up to 5.7.
Data Augmentation Methods for Improving the Performance of Machine Reading Comprehension
Sunkyung Lee, Eunseong Choi, Seonho Jeong, Jongwuk Lee
http://doi.org/10.5626/JOK.2021.48.12.1298
Machine reading comprehension is a method of understanding the meaning and performing inference over a given text by computers, and it is one of the most essential techniques for understanding natural language. The question answering task yields a way to test the reasoning ability of intelligent systems. Nowadays, machine reading comprehension techniques performance has significantly improved following the recent progress of deep neural networks. Nevertheless, there may be challenges in improving performance when data is sparse. To address this issue, we leverage word-level and sentence-level data augmentation techniques through text editing, while minimizing changes to the existing models and cost. In this work, we propose data augmentation methods for a pre-trained language model, which is most widely used in English question answering tasks, to confirm the improved performance over the existing models.
Analysis of the Semantic Answer Types to Understand the Limitations of MRQA Models
Doyeon Lim, Haritz Puerto San Roman, Sung-Hyon Myaeng
http://doi.org/10.5626/JOK.2020.47.3.298
Recently, the performance of Machine Reading Question Answering (MRQA) models has surpassed humans on datasets such as SQuAD. For further advances in MRQA techniques, new datasets are being introduced. However, they are rarely based on a deep understanding of the QA capabilities of the existing models tested on the previous datasets. In this study, we analyze the SQuAD dataset quantitatively and qualitatively to demonstrate how the MRQA models answer the questions. It turns out that the current MRQA models rely heavily on the use of wh-words and Lexical Answer Types (LAT) in the questions instead of using the meanings of the entire questions and the evidence documents. Based on this analysis, we present the directions for new datasets so that they can facilitate the advancement of current QA techniques centered around the MRQA models.
Korean Machine Reading Comprehension with S²-Net
Cheoneum Park, Changki Lee, Sulyn Hong, Yigyu Hwang, Taejoon Yoo, Hyunki Kim
http://doi.org/10.5626/JOK.2018.45.12.1260
Machine reading comprehension is the task of understanding a given context and identifying the right answer in context. Simple recurrent unit (SRU) solves the vanishing gradient problem in recurrent neural network (RNN) by using neural gate such as gated recurrent unit (GRU), and removes previous hidden state from gate input to improve speed. Self-matching network is used in r-net, and this has a similar effect as coreference resolution can show similar semantic context information by calculating attention weight for its RNN sequence. In this paper, we propose a S²-Net model that add self-matching layer to an encoder using stacked SRUs and constructs a Korean machine reading comprehension dataset. Experimental results reveal the proposed S²-Net model has EM 70.81% and F1 82.48% performance in Korean machine reading comprehension.
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