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Register a new userBreakingBERT@IITK at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables
Breakingbert @ Iitk في Semeval-2021 المهمة 9: التحقق من البيان وأدلة العثور على الجداول
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في الآونة الأخيرة، كان هناك مصلحة في البحث عن التحقق من الواقع والتنبؤ على البيانات المنظمة مثل الجداول والرسوم البيانية. للتحايل على أي حادث أخبار كاذبة، من الضروري عدم النموذج والتنبؤ فقط على البيانات المنظمة بكفاءة ولكن أيضا لشرح تلك التنبؤات. في هذه الورقة، كجزء من مهمة SEMEVAL-2021 9، نتعامل مع مشكلة التحقق من الحقائق وأدلة العثور على بيانات جدولية. هناك نوعان فرعي، حيث نظأ طاولة وبيان / حقيقة، يتمثل الترجمة الفرعية في تحديد ما إذا كان البيان استنتجا من البيانات الجدولية، ويقوم SubTask B بتحديد الخلايا الموجودة في الجدول تقديم أدلة على التراكب الفرعي السابق. نحن نقدم مقارنة بين خطوط الأساس وحالة النهج الفنية على مجموعة بيانات Semtabfact المعينة. نقترح أيضا نهج رواية Cellbert لحل مهمة العثور على الأدلة، كشكل من أشكال مهمة الاستدلال باللغة الطبيعية. نحصل على درجة F1 ذات الاتجاهين من 0.69 على التراكب الفرعي ونتيجة F1 من 0.65 على التراكب الفرعي B.
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 efficiently 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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This paper describes our approach for Task 9 of SemEval 2021: Statement Verification and Evidence Finding with Tables. We participated in both subtasks, namely statement verification and evidence finding. For the subtask of statement verification, we
extend the TAPAS model to adapt to the unknown' class of statements by finetuning it on an augmented version of the task data. For the subtask of evidence finding, we finetune the DistilBERT model in a Siamese setting.
Tables are widely used in various kinds of documents to present information concisely. Understanding tables is a challenging problem that requires an understanding of language and table structure, along with numerical and logical reasoning. In this p
aper, we present our systems to solve Task 9 of SemEval-2021: Statement Verification and Evidence Finding with Tables (SEM-TAB-FACTS). The task consists of two subtasks: (A) Given a table and a statement, predicting whether the table supports the statement and (B) Predicting which cells in the table provide evidence for/against the statement. We fine-tune TAPAS (a model which extends BERT's architecture to capture tabular structure) for both the subtasks as it has shown state-of-the-art performance in various table understanding tasks. In subtask A, we evaluate how transfer learning and standardizing tables to have a single header row improves TAPAS' performance. In subtask B, we evaluate how different fine-tuning strategies can improve TAPAS' performance. Our systems achieve an F1 score of 67.34 in subtask A three-way classification, 72.89 in subtask A two-way classification, and 62.95 in subtask B.
Understanding tables is an important and relevant task that involves understanding table structure as well as being able to compare and contrast information within cells. In this paper, we address this challenge by presenting a new dataset and tasks
that addresses this goal in a shared task in SemEval 2020 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS). Our dataset contains 981 manually-generated tables and an auto-generated dataset of 1980 tables providing over 180K statement and over 16M evidence annotations. SEM-TAB-FACTS featured two sub-tasks. In sub-task A, the goal was to determine if a statement is supported, refuted or unknown in relation to a table. In sub-task B, the focus was on identifying the specific cells of a table that provide evidence for the statement. 69 teams signed up to participate in the task with 19 successful submissions to subtask A and 12 successful submissions to subtask B. We present our results and main findings from the competition.
This paper describes the system submitted in the SemEval-2021 Statement Verification and Evidence Finding with Tables task. The system relies on candidate generation for logical forms on the table based on keyword matching and dependency parsing on the claim statements.
This paper describes our system for verifying statements with tables at SemEval-2021 Task 9. We developed a two-stage verifying system based on the latest table-based pre-trained model GraPPa. Multiple networks are devised to verify different types o
f statements in the competition dataset and an adaptive model ensembling technique is applied to ensemble models in both stages. A statement-slot-based symbolic operation module is also used in our system to further improve the performance and stability of the system. Our model achieves second place in the 3-way classification and fourth place in the 2-way classification evaluation. Several ablation experiments show the effectiveness of different modules proposed in this paper.
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