Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question which is contextualized by a long scenario description. Since a scenario contains both keyphrases for retrieval and much noise
, retrieval for SQA is extremely difficult. Moreover, it can hardly be supervised due to the lack of relevance labels of paragraphs for SQA. To meet the challenge, in this paper we propose a joint retriever-reader model called JEEVES where the retriever is implicitly supervised only using QA labels via a novel word weighting mechanism. JEEVES significantly outperforms a variety of strong baselines on multiple-choice questions in three SQA datasets.
In this paper, we introduce the task of predicting severity of age-restricted aspects of movie content based solely on the dialogue script. We first investigate categorizing the ordinal severity of movies on 5 aspects: Sex, Violence, Profanity, Subst
ance consumption, and Frightening scenes. The problem is handled using a siamese network-based multitask framework which concurrently improves the interpretability of the predictions. The experimental results show that our method outperforms the previous state-of-the-art model and provides useful information to interpret model predictions. The proposed dataset and source code are publicly available at our GitHub repository.
This paper present a description for the ROCLING 2021 shared task in dimensional sentiment analysis for educational texts. We submitted two runs in the final test. Both runs use the standard regression model. The Run1 uses Chinese version of BERT as
the base, and in Run2 we use the early version of MacBERT that Chinese version of RoBERTa-like BERT model, RoBERTa-wwm-ext. Using powerful pre-training model of BERT for text embedding to help train the model.
In this study, we proposed a novel Lexicon-based pseudo-labeling method utilizing explainable AI(XAI) approach. Existing approach have a fundamental limitation in their robustness because poor classifier leads to inaccurate soft-labeling, and it lead
to poor classifier repetitively. Meanwhile, we generate the lexicon consists of sentiment word based on the explainability score. Then we calculate the confidence of unlabeled data with lexicon and add them into labeled dataset for the robust pseudo-labeling approach. Our proposed method has three contributions. First, the proposed methodology automatically generates a lexicon based on XAI and performs independent pseudo-labeling, thereby guaranteeing higher performance and robustness compared to the existing one. Second, since lexicon-based pseudo-labeling is performed without re-learning in most of models, time efficiency is considerably increased, and third, the generated high-quality lexicon can be available for sentiment analysis of data from similar domains. The effectiveness and efficiency of our proposed method were verified through quantitative comparison with the existing pseudo-labeling method and qualitative review of the generated lexicon.
This paper introduces the system description of the hub team, which explains the related work and experimental results of our team's participation in SemEval 2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC). The da
ta of this shared task is mainly some cross-language or multi-language sentence pair corpus. The languages covered in the corpus include English, Chinese, French, Russian, and Arabic. The task goal is to judge whether the same words in these sentence pairs have the same meaning in the sentence. This can be seen as a task of binary classification of sentence pairs. What we need to do is to use our method to determine as accurately as possible the meaning of the words in a sentence pair are the same or different. The model used by our team is mainly composed of RoBERTa and Tf-Idf algorithms. The result evaluation index of task submission is the F1 score. We only participated in the English language task. The final score of the test set prediction results submitted by our team was 84.60.
Recent work on aspect-level sentiment classification has employed Graph Convolutional Networks (GCN) over dependency trees to learn interactions between aspect terms and opinion words. In some cases, the corresponding opinion words for an aspect term
cannot be reached within two hops on dependency trees, which requires more GCN layers to model. However, GCNs often achieve the best performance with two layers, and deeper GCNs do not bring any additional gain. Therefore, we design a novel selective attention based GCN model. On one hand, the proposed model enables the direct interaction between aspect terms and context words via the self-attention operation without the distance limitation on dependency trees. On the other hand, a top-k selection procedure is designed to locate opinion words by selecting k context words with the highest attention scores. We conduct experiments on several commonly used benchmark datasets and the results show that our proposed SA-GCN outperforms strong baseline models.
Technological development and intense competition in this sector
bring out the need for more accurate cost information. Sound cost
information will impact upon the quality of accounting reports and
the ability of management in making decision, whi
ch in turn
depends on selecting suitable costing techniques. This research
discusses the possibility of applying process based costing system
on the heart disease department at al-assad hospital in lattakia.
Results of this research showed application PBC system
requirements, The difficulties faced by the non-search data
availability.
The aim of this study is to adopt Activi- ty Based Costing (ABC) system using a model of six steps in one of the largest private hospital in the Jordanian capital (Amman). The result of the study reveals that adoption of ABC in Endoscopy department p
rovides more accurate information about the costs of medical services which leads to fair market price of all activities achieved in the department which is very useful for compe- tition purposes. Moreover, the result documents the ability of this system in avoiding useless ac- tivities and control costs. Additionally, the result exhibits the ability of ABC system in providing data base and useful information that helps in planning, control and evaluation performance. The study recommends that private hospitals use ABC system which could help them to work in a competitive environment. Also the study recom- mends the necessity of private hospitals to make changes in its accounting information system to t the use of ABC
This study aim to apply Time-Driven Activity-Based Costing (TDABC) in Syrian industrial environment to define the ability of (TDABC) in recognition ofidle resources and calculating the cost of it, because of the critical role of this recognition in r
educing production cost and inducing entity’s competitive force.
(TDABC) has been applied in one of the industrial entities in Damascus Rural. Applying (TDABC) in this entity led to the presence of unused production capacity in one of the entity's sections which representing 27% of the total costs of the resources division, in addition to determine the cost of this unused production capacity, while the adopted accounting cost system in the entity couldn't discover unused production capacity in any section of entity.
This study revealed that (TDABC) succeeded in defining idle resources in the chosen entity, and showed the simplicity of applying this method.
