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Register a new userVolta at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables using TAPAS and Transfer Learning
Volta في Semeval-2021 المهمة 9: بيان التحقق والأدلة التي تجد مع الجداول باستخدام Tapas ونقل التعلم
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يتم استخدام الجداول على نطاق واسع في أنواع مختلفة من المستندات لتقديم المعلومات بإيجاز. يعد فهم الجداول مشكلة صعبة تتطلب فهم لغة اللغة والجدول، إلى جانب التفكير العددي والمنطقي. في هذه الورقة، نقدم أنظمتنا لحل المهمة 9 من Semeval-2021: التحقق من البيان وإصدار الأدلة مع الجداول (SEM-Tab-Facts). تتكون المهمة من اثنين من المهام: (أ) بالنظر إلى جدول وبيان، يتوقع ما إذا كان الجدول يدعم البيان و (ب) التنبؤ بالخلايا الموجودة في الجدول تقديم أدلة على / ضد البيان. نحن نايت Tune Tapas (نموذج يمتد بنية بيرت لالتقاط بنية جدولي) لكل من المهام الفرعية حيث أظهرت أداء حالة من بين الفن في مهام فهم الجدول المختلفة. في SubTask a، نقيم كيفية نقل التعلم وتوحيد الجداول للحصول على صف رأس واحد يحسن أداء Tapas. في SubTask B، نقيم مدى اختلاف استراتيجيات ضبط التوصيل المختلفة تحسين أداء Tapas. حقق أنظمتنا درجة F1 من 67.34 في التراكب الفرعي تصنيف ثلاثي الاتجاه، 72.89 في فرعية تصنيف ثنائية الاتجاه، و 62.95 في الفرعية B.
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 paper, 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.
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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.
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.
We present the TAPAS contribution to the Shared Task on Statement Verification and Evidence Finding with Tables (SemEval 2021 Task 9, Wang et al. (2021)). SEM TAB FACT Task A is a classification task of recognizing if a statement is entailed, neutral
or refuted by the content of a given table. We adopt the binary TAPAS model of Eisenschlos et al. (2020) to this task. We learn two binary classification models: A first model to predict if a statement is neutral or non-neutral and a second one to predict if it is entailed or refuted. As the shared task training set contains only entailed or refuted examples, we generate artificial neutral examples to train the first model. Both models are pre-trained using a MASKLM objective, intermediate counter-factual and synthetic data (Eisenschlos et al., 2020) and TABFACT (Chen et al., 2020), a large table entailment dataset. We find that the artificial neutral examples are somewhat effective at training the first model, achieving 68.03 test F1 versus the 60.47 of a majority baseline. For the second stage, we find that the pre-training on the intermediate data and TABFACT improves the results over MASKLM pre-training (68.03 vs 57.01).
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.
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.
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