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Register a new userKnowGraph@IITK at SemEval-2021 Task 11: Building Knowledge Graph for NLP Research
knowgraph @ iitk في Semeval-2021 المهمة 11: بناء الرسم البياني المعرفة للبحث عن البحوث NLP
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يبذل الأبحاث في معالجة اللغة الطبيعية تطورات سريعة، مما يؤدي إلى نشر عدد كبير من الأوراق البحثية. العثور على أوراق بحثية ذات صلة ومساهمتها في المجال هي مشكلة صعبة. في هذه الورقة، نتعلم هذا التحدي عبر مهمة Semeval 2021 11: NLPConTributiongraph، من خلال تطوير نظام للحصول على الرسم البياني المعرفة المركزة للصفحة البحثية على أدب معالجة اللغة الطبيعية. تنقسم المهمة إلى ثلاث مهام فرعية: استخراج جمل المساهمة التي توضح مساهمات مهمة في المادة البحثية، واستخراج العبارات من أحكام المساهمة، والتنبؤ بالوحدات الإعلامية في المادة البحثية مع تكوين ثلاثي الأمراض من العبارات. النظام المقترح غير ملائم إلى مجال الموضوع ويمكن تطبيقه لبناء رسم بياني للمعرفة لأي منطقة. وجدنا أن نماذج اللغة القائمة على المحولات يمكن أن تحسن بشكل كبير التقنيات الحالية واستخدام النموذج المستند إلى Scibert. تستخدم المهمة الفرعية الأولى لدينا ثنائي الاتجاه LSTM (Bilstm) مكدسة أعلى طبقات نموذج Scibert، في حين أن المهمة الفرعية الثانية تستخدم مجال عشوائي مشروط (CRF) على رأس Scibert مع Bilstm. تستخدم المهمة الفرعية الثالثة نهجا عصبي مجتمعة مقرها مع الاستدلال لتنبؤ وحدة المعلومات وتشكيل ثلاثي الزيارة من العبارات. حقق نظامنا درجة F1 من 0.38، 0.63 و 0.76 في اختبار خط أنابيب نهاية إلى نهاية، اختبار استخراج العبارات واختبار استخراج ثلاث مرات.
Research in Natural Language Processing is making rapid advances, resulting in the publication of a large number of research papers. Finding relevant research papers and their contribution to the domain is a challenging problem. In this paper, we address this challenge via the SemEval 2021 Task 11: NLPContributionGraph, by developing a system for a research paper contributions-focused knowledge graph over Natural Language Processing literature. The task is divided into three sub-tasks: extracting contribution sentences that show important contributions in the research article, extracting phrases from the contribution sentences, and predicting the information units in the research article together with triplet formation from the phrases. The proposed system is agnostic to the subject domain and can be applied for building a knowledge graph for any area. We found that transformer-based language models can significantly improve existing techniques and utilized the SciBERT-based model. Our first sub-task uses Bidirectional LSTM (BiLSTM) stacked on top of SciBERT model layers, while the second sub-task uses Conditional Random Field (CRF) on top of SciBERT with BiLSTM. The third sub-task uses a combined SciBERT based neural approach with heuristics for information unit prediction and triplet formation from the phrases. Our system achieved F1 score of 0.38, 0.63 and 0.76 in end-to-end pipeline testing, phrase extraction testing and triplet extraction testing respectively.
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We propose a cascade of neural models that performs sentence classification, phrase recognition, and triple extraction to automatically structure the scholarly contributions of NLP publications. To identify the most important contribution sentences i
n a paper, we used a BERT-based classifier with positional features (Subtask 1). A BERT-CRF model was used to recognize and characterize relevant phrases in contribution sentences (Subtask 2). We categorized the triples into several types based on whether and how their elements were expressed in text, and addressed each type using separate BERT-based classifiers as well as rules (Subtask 3). Our system was officially ranked second in Phase 1 evaluation and first in both parts of Phase 2 evaluation. After fixing a submission error in Pharse 1, our approach yields the best results overall. In this paper, in addition to a system description, we also provide further analysis of our results, highlighting its strengths and limitations. We make our code publicly available at https://github.com/Liu-Hy/nlp-contrib-graph.
This paper describes the winning system in the End-to-end Pipeline phase for the NLPContributionGraph task. The system is composed of three BERT-based models and the three models are used to extract sentences, entities and triples respectively. Exper
iments show that sampling and adversarial training can greatly boost the system. In End-to-end Pipeline phase, our system got an average F1 of 0.4703, significantly higher than the second-placed system which got an average F1 of 0.3828.
In this work, we present our approach and findings for SemEval-2021 Task 5 - Toxic Spans Detection. The task's main aim was to identify spans to which a given text's toxicity could be attributed. The task is challenging mainly due to two constraints:
the small training dataset and imbalanced class distribution. Our paper investigates two techniques, semi-supervised learning and learning with Self-Adjusting Dice Loss, for tackling these challenges. Our submitted system (ranked ninth on the leader board) consisted of an ensemble of various pre-trained Transformer Language Models trained using either of the above-proposed techniques.
This paper describes our contribution to SemEval 2021 Task 1 (Shardlow et al., 2021): Lexical Complexity Prediction. In our approach, we leverage the ELECTRA model and attempt to mirror the data annotation scheme. Although the task is a regression ta
sk, we show that we can treat it as an aggregation of several classification and regression models. This somewhat counter-intuitive approach achieved an MAE score of 0.0654 for Sub-Task 1 and MAE of 0.0811 on Sub-Task 2. Additionally, we used the concept of weak supervision signals from Gloss-BERT in our work, and it significantly improved the MAE score in Sub-Task 1.
Recently, there has been an interest in the research on factual verification and prediction over structured data like tables and graphs. To circumvent any false news incident, it is necessary to not only model and predict over structured data efficie
ntly but also to explain those predictions. In this paper, as the part of the SemEval-2021 Task 9, we tackle the problem of fact verification and evidence finding over tabular data. There are two subtasks, in which given a table and a statement/fact, the subtask A is to determine whether the statement is inferred from the tabular data and the subtask B is to determine which cells in the table provide evidence for the former subtask. We make a comparison of the baselines and state of the art approaches over the given SemTabFact dataset. We also propose a novel approach CellBERT to solve the task of evidence finding, as a form of Natural Language Inference task. We obtain a 3-way F1 score of 0.69 on subtask A and an F1 score of 0.65 on subtask B.
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