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Register a new userNEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension
Neuer في مهمة Semeval-2021 4: تمثيل موجز كامل عن طريق ملء الإجابات موضع تساؤل لمطابقة فهم القراءة
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تهدف مهمة Sereval 4 إلى إيجاد خيار مناسب من المرشحين المتعددين لحل مهمة فهم القراءة في الجهاز.تقترح معظم الأساليب الموجودة على Concat السؤال والخيار معا لتشكيل نموذج على دراية بالسياق.ومع ذلك، نقول أن التسلسلات المباشرة يمكن أن توفر فقط سياقا محظوظا فقط لمهمة MRC، مما يتجاهل المواقف المحددة للخيار بالنسبة للسؤال.في هذه الورقة، نقترح نموذج رواية MRC عن طريق تعبئة الخيارات في السؤال لإنتاج سياق جيد المحبوس (يعرف بأنه ملخص) يمكن أن تكشف بشكل أفضل عن العلاقة بين الخيار والسؤال.نقوم بإجراء سلسلة من التجارب على مجموعة البيانات المعينة، وتظهرت النتائج أن نهجنا يفوق النظرات الأخرى النظراء إلى حد كبير.
SemEval task 4 aims to find a proper option from multiple candidates to resolve the task of machine reading comprehension. Most existing approaches propose to concat question and option together to form a context-aware model. However, we argue that straightforward concatenation can only provide a coarse-grained context for the MRC task, ignoring the specific positions of the option relative to the question. In this paper, we propose a novel MRC model by filling options into the question to produce a fine-grained context (defined as summary) which can better reveal the relationship between option and question. We conduct a series of experiments on the given dataset, and the results show that our approach outperforms other counterparts to a large extent.
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This paper describes the winning system for subtask 2 and the second-placed system for subtask 1 in SemEval 2021 Task 4: ReadingComprehension of Abstract Meaning. We propose to use pre-trianed Electra discriminator to choose the best abstract word fr
om five candidates. An upper attention and auto denoising mechanism is introduced to process the long sequences. The experiment results demonstrate that this contribution greatly facilitatesthe contextual language modeling in reading comprehension task. The ablation study is also conducted to show the validity of our proposed methods.
This paper introduces the SemEval-2021 shared task 4: Reading Comprehension of Abstract Meaning (ReCAM). This shared task is designed to help evaluate the ability of machines in representing and understanding abstract concepts.Given a passage and the
corresponding question, a participating system is expected to choose the correct answer from five candidates of abstract concepts in cloze-style machine reading comprehension tasks. Based on two typical definitions of abstractness, i.e., the imperceptibility and nonspecificity, our task provides three subtasks to evaluate models' ability in comprehending the two types of abstract meaning and the models' generalizability. Specifically, Subtask 1 aims to evaluate how well a participating system models concepts that cannot be directly perceived in the physical world. Subtask 2 focuses on models' ability in comprehending nonspecific concepts located high in a hypernym hierarchy given the context of a passage. Subtask 3 aims to provide some insights into models' generalizability over the two types of abstractness. During the SemEval-2021 official evaluation period, we received 23 submissions to Subtask 1 and 28 to Subtask 2. The participating teams additionally made 29 submissions to Subtask 3. The leaderboard and competition website can be found at https://competitions.codalab.org/competitions/26153. The data and baseline code are available at https://github.com/boyuanzheng010/SemEval2021-Reading-Comprehension-of-Abstract-Meaning.
In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within a Machine Learning (ML) framew
ork for predicting lexical complexity. In particular, we use BERT contextualized word embeddings to represent the semantic meaning of the target word and its context. We participated in the sub-task of predicting the complexity score of single words
This paper describes our system for SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning. To accomplish this task, we utilize the Knowledge-Enhanced Graph Attention Network (KEGAT) architecture with a novel semantic space transformation str
ategy. It leverages heterogeneous knowledge to learn adequate evidences, and seeks for an effective semantic space of abstract concepts to better improve the ability of a machine in understanding the abstract meaning of natural language. Experimental results show that our system achieves strong performance on this task in terms of both imperceptibility and nonspecificity.
Most question answering tasks focuses on predicting concrete answers, e.g., named entities. These tasks can be normally achieved by understanding the contexts without additional information required. In Reading Comprehension of Abstract Meaning (ReCA
M) task, the abstract answers are introduced. To understand abstract meanings in the context, additional knowledge is essential. In this paper, we propose an approach that leverages the pre-trained BERT Token embeddings as a prior knowledge resource. According to the results, our approach using the pre-trained BERT outperformed the baselines. It shows that the pre-trained BERT token embeddings can be used as additional knowledge for understanding abstract meanings in question answering.
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