Despite the success of neural dialogue systems in achieving high performance on the leader-board, they cannot meet users' requirements in practice, due to their poor reasoning skills. The underlying reason is that most neural dialogue models only cap
ture the syntactic and semantic information, but fail to model the logical consistency between the dialogue history and the generated response. Recently, a new multi-turn dialogue reasoning task has been proposed, to facilitate dialogue reasoning research. However, this task is challenging, because there are only slight differences between the illogical response and the dialogue history. How to effectively solve this challenge is still worth exploring. This paper proposes a Fine-grained Comparison Model (FCM) to tackle this problem. Inspired by human's behavior in reading comprehension, a comparison mechanism is proposed to focus on the fine-grained differences in the representation of each response candidate. Specifically, each candidate representation is compared with the whole history to obtain a history consistency representation. Furthermore, the consistency signals between each candidate and the speaker's own history are considered to drive a model prefer a candidate that is logically consistent with the speaker's history logic. Finally, the above consistency representations are employed to output a ranking list of the candidate responses for multi-turn dialogue reasoning. Experimental results on two public dialogue datasets show that our method obtains higher ranking scores than the baseline models.
MeasEval aims at identifying quantities along with the entities that are measured with additional properties within English scientific documents. The variety of styles used makes measurements, a most crucial aspect of scientific writing, challenging
to extract. This paper presents ablation studies making the case for several preprocessing steps such as specialized tokenization rules. For linguistic structure, we encode dependency trees in a Deep Graph Convolution Network (DGCNN) for multi-task classification.
Scientific documents are replete with measurements mentioned in various formats and styles. As such, in a document with multiple quantities and measured entities, the task of associating each quantity to its corresponding measured entity is challengi
ng. Thus, it is necessary to have a method to efficiently extract all measurements and attributes related to them. To this end, in this paper, we propose a novel model for the task of measurement relation extraction (MRE) whose goal is to recognize the relation between measured entities, quantities, and conditions mentioned in a document. Our model employs a deep translation-based architecture to dynamically induce the important words in the document to classify the relation between a pair of entities. Furthermore, we introduce a novel regularization technique based on Information Bottleneck (IB) to filter out the noisy information from the induced set of important words. Our experiments on the recent SemEval 2021 Task 8 datasets reveal the effectiveness of the proposed model.
Pancytopenia is defined by reduction of all three formed elements of blood below the
normal reference . It may be manifestation of a wide variety of disorders , yet there exist
few published assessments of the frequencies of various etiologies , an
d these frequencies
exhibit substantial geographic variation . This study was carried out to investigate for and
to identify the causes of pancytopenia , to find out the frequency of different causes , to
determine the incidence of pancytopenia in relation to sex and age and to compare our
findings with those of other similar studies in different countries .This was a prospective
study conducted in Al-Asad and Tishreen academic hospitals in Lattakia city , Syria , ovar
a period of one year . A total of 113 patients with the diagnosis of pancytopenia were
enrolled in the study All patients underwent a detailed medical history and full physical
examination followed by blood sampling for the investigations i.e. complete blood count
with peripheral film , erythrocyte sedimentation rate ( ESR ) , liver function test , PT ,
HBsAg and Anti- HCV , ultrasonography of abdomen . All patients underwent bone
marrow aspiration and trephine biopsy for some patients. A definite female
preponderance was observed , 59 were female and 54 were male. The majority of cases
were encountered in the age group of more than sixty years old .Infiltration disorders were
the commonest cause that was observed in 38.1% followed by infections in 22.1% ,
megaloblastic anemia 12.4% , myelodysplasia 11.5%, aplastic anemia 7.1% ,
myelofibrosis 3.5% , hypersplenism 3.5% and systemic disorders 1.8% Detailed clinical
history and meticulous physical examination along with baseline hematological
investigations provide good information and help in systematic planning of further
investigations to diagnose pancytopenia's cause.
