Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSemEval-2021 Task 12: Learning with Disagreements
Semeval-2021 المهمة 12: التعلم مع الخلافات
210
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
الخلاف بين المبرمجين هو في كل مكان في جميع مجموعات البيانات المشروحة بأحكام بشرية في كل من معالجة اللغة الطبيعية ورؤية الكمبيوتر.ومع ذلك، تفترض معظم أساليب تعلم الآلات الأكثر إشرافا أن التفسير المفضل الوحيد موجود لكل عنصر، وهو في أحسن الأحوال مثالية.كان الهدف من مهمة Semeval-2021 المشتركة بشأن التعلم مع الخلافات (LE-WI-I-DI) هو توفير إطار اختبار موحد لأساليب التعلم من البيانات التي تحتوي على شروح متعددة وربما متناقضة تغطي مجموعات البيانات الأكثر شهرة التي تحتوي على معلومات حول الخلافاتتفسير اللغة وتصنيف الصور.في هذه الورقة وصفنا المهمة المشتركة ونتائجها.
Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assume that a single preferred interpretation exists for each item, which is at best an idealization. The aim of the SemEval-2021 shared task on learning with disagreements (Le-Wi-Di) was to provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images. In this paper we describe the shared task and its results.
References used
https://aclanthology.org/
rate research
Read More
Crowdsourcing has been ubiquitously used for annotating enormous collections of data. However, the major obstacles to using crowd-sourced labels are noise and errors from non-expert annotations. In this work, two approaches dealing with the noise and
errors in crowd-sourced labels are proposed. The first approach uses Sharpness-Aware Minimization (SAM), an optimization technique robust to noisy labels. The other approach leverages a neural network layer called softmax-Crowdlayer specifically designed to learn from crowd-sourced annotations. According to the results, the proposed approaches can improve the performance of the Wide Residual Network model and Multi-layer Perception model applied on crowd-sourced datasets in the image processing domain. It also has similar and comparable results with the majority voting technique when applied to the sequential data domain whereby the Bidirectional Encoder Representations from Transformers (BERT) is used as the base model in both instances.
Among the tasks motivated by the proliferation of misinformation, propaganda detection is particularly challenging due to the deficit of fine-grained manual annotations required to train machine learning models. Here we show how data from other relat
ed tasks, including credibility assessment, can be leveraged in multi-task learning (MTL) framework to accelerate the training process. To that end, we design a BERT-based model with multiple output layers, train it in several MTL scenarios and perform evaluation against the SemEval gold standard.
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.
We describe our systems of subtask1 and subtask3 for SemEval-2021 Task 6 on Detection of Persuasion Techniques in Texts and Images. The purpose of subtask1 is to identify propaganda techniques given textual content, and the goal of subtask3 is to det
ect them given both textual and visual content. For subtask1, we investigate transfer learning based on pre-trained language models (PLMs) such as BERT, RoBERTa to solve data sparsity problems. For subtask3, we extract heterogeneous visual representations (i.e., face features, OCR features, and multimodal representations) and explore various multimodal fusion strategies to combine the textual and visual representations. The official evaluation shows our ensemble model ranks 1st for subtask1 and 2nd for subtask3.
This paper presents the GX system for the Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) task. The purpose of the MCL-WiC task is to tackle the challenge of capturing the polysemous nature of words without relying on a fixed
sense inventory in a multilingual and cross-lingual setting. To solve the problems, we use context-specific word embeddings from BERT to eliminate the ambiguity between words in different contexts. For languages without an available training corpus, such as Chinese, we use neuron machine translation model to translate the English data released by the organizers to obtain available pseudo-data. In this paper, we apply our system to the English and Chinese multilingual setting and the experimental results show that our method has certain advantages.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
