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AUOR في Semeval-2021 المهمة 4: استخدام Admenings Token Bert Token المدربة مسبقا للحصول على سؤال الرد على معنى مجردة
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تركز معظم مهام الإجابة على معظم الأسئلة على التنبؤ بإجابات ملموسة، مثل الكيانات المسماة.يمكن تحقيق هذه المهام عادة عن طريق فهم السياقات دون وجود معلومات إضافية مطلوبة.في قراءة الفهم من المهمة المعنى التجريدي (إعادة التقييم)، يتم تقديم الإجابات المجردة.لفهم معاني مجردة في السياق، المعرفة الإضافية ضرورية.في هذه الورقة، نقترح نهج يهدف إلى أن يشرف رصيد بيرت المدرب مسبقا كموارد معرفة مسبقة.وفقا للنتائج، فإن نهجنا باستخدام بيرت المدربة مسبقا تفوقت على الأساس.إنه يدل على أنه يمكن استخدام Abeddings Token Bertken المدربة مسبقا كمعرفة إضافية لفهم المعاني المجردة في الإجابة على الأسئلة.
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 (ReCAM) 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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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.
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 describes our system for Task 4 of SemEval-2021: Reading Comprehension of Abstract Meaning (ReCAM). We participated in all subtasks where the main goal was to predict an abstract word missing from a statement. We fine-tuned the pre-trained
masked language models namely BERT and ALBERT and used an Ensemble of these as our submitted system on Subtask 1 (ReCAM-Imperceptibility) and Subtask 2 (ReCAM-Nonspecificity). For Subtask 3 (ReCAM-Intersection), we submitted the ALBERT model as it gives the best results. We tried multiple approaches and found that Masked Language Modeling(MLM) based approach works the best.
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.
Predicting the complexity level of a word or a phrase is considered a challenging task. It is even recognized as a crucial step in numerous NLP applications, such as text rearrangements and text simplification. Early research treated the task as a bi
nary classification task, where the systems anticipated the existence of a word's complexity (complex versus uncomplicated). Other studies had been designed to assess the level of word complexity using regression models or multi-labeling classification models. Deep learning models show a significant improvement over machine learning models with the rise of transfer learning and pre-trained language models. This paper presents our approach that won the first rank in the SemEval-task1 (sub stask1). We have calculated the degree of word complexity from 0-1 within a text. We have been ranked first place in the competition using the pre-trained language models Bert and RoBERTa, with a Pearson correlation score of 0.788.
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