DPR at S em E val-2021 Task 8: Dynamic Path Reasoning for Measurement Relation Extraction (2021)

Abstract: 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 challenging. 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.

## introduction one of the key indicators of scientific writing is the quantities description of various experiments and results. while the mentions of all measurements could provide a rigorous understanding of the topic, it might make the reading and automatic processing of the text more difficult. as such, designing effective methods to recognize the mentions of measurements and also the conditions in which they are valid is necessary. according to the definition of the semeval 2021 task 8 @xcite , a measurement might consist of the following components: (i) measure entity: a span referring to an entity that one of its properties has been measured and its value is provided in the document; (ii) measured property: a span referring to the characteristics of an entity that has been measured; (iii) figure 1 : a document annotated with the measured entities (i.e., @xcite ), quantity (i.e., @xcite ), measured property (i.e., @xcite ) and qualifier (i.e., @xcite ) (best viewed in color). quantity: a span in the document that refers to a value and possibly it comes with a unit; and (iv) qualifier: a span referring to a condition in which more information about the quantity, measured property or measured entity is provided. figure 1 shows a sample document annotated with the aforementioned entities. in this paper, we collectively name all of these four types as measurement component. as it is shown in the provided example, documents might contain multiple entities, properties, quantities and qualifiers that are scattered in different parts of the document. as such, finding which measurement components are associated with each other is not straightforward. in this paper, this task is called measurement relation extraction (mre) that aims to recognize what is the relationship between two given measurement components. more specifically, the following relation types are considered: (i) has-property: indicates the selected property is one of the characteristics of the selected entity; (ii) has-quantity: indicates the selected quantity is provided for the selected entity or property; (iii) qualifies: indicates the selected qualifier provides more information about the selected entity or quantity; (iv) none: indicates that there is no relation between the selected measurement components. for instance, in the given example document in figure 1 , the following relations between different measurement components exist: ( 1 ) me 1 has property pr 1 ; (2) pr 1 has quantity qt 1 ; (3) me 2 has quantity qt 2 ; (4) me 3 has quantity qt 3 ; and (5) ql 1 qualifies me 3 ; finding the relation between a pair of measurement components is challenging and it requires consideration about the position of the given entities and the context in which they are used. generally, this task can be formulated as a typical relation extraction (re) task whose goal is to identify the semantic relation between two given named entity mentions. for re, it has been shown that contextual information such as dependency path between the two given entities is important. as such, in this paper, we also aim to exploit the contextual information for a pair of measurement entities to predict the relation between them. to this end, the main question to answer is how we can extract the contextual information that is helpful for this task. one simple solution is to use the dependency path between the two measurement components. however, this might not be perfect due to various reasons such as lack of high-quality dependency parser designed especially for scientific domain and the fact that the dependency tree is ignorant of the downstream task (i.e., mre) thus might not be efficient to extract important context from. therefore, in this paper, we aim to propose a novel method to dynamically infer the important context for the mre task. more specifically, we introduce a deep architecture to infer which words should be selected from the given document to form the important context from which the relation between the given measurement components can be inferred. the proposed deep architecture exploits a translation-based perspective to achieve this goal. in addition, in this paper, we propose a novel method to efficiently regularize the representations of the input words based on the inferred important context. in particular, our method is based on the information bottleneck (ib) theory in which the inferred context is treated as information bottleneck to exclude noisy information in the input document representation. we conduct extensive experiments on the semeval 2021 task 8 dataset. our experiments reveal the effectiveness of the proposed model for the task of mre.