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كامبريدج في Semeval-2021 المهمة 2: النموذج WIC العصبي مع زيادة البيانات واستكشاف التمثيل
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تصف هذه الورقة نظام فريق Cambridge المقدم إلى المهمة المشتركة SEMEVAL-2021 على الغموض المتعدد اللغات واللغة الاجتماعية في السياق.بناء فوق نموذج لغة ملثم مسبقا مدرب مسبقا، يتم تدريب نظامنا أولا مسبقا على بيانات خارج المجال، ثم ضبطها على بيانات داخل المجال.نوضح فعالية استراتيجية التدريب التي تتراوح خطوتين مقترح وفوائد تكبير البيانات من كل من الأمثلة القائمة والموارد الجديدة.نحن مزيد من التحقيق في التمثيلات المختلفة وإظهار أن إضافة الميزات المستندة إلى عن بعد مفيدة في مهمة الغموض للكلمة في السياق.ينتج نظامنا نتائج تنافسية للغاية في المسار المتبادل دون تدريب على أي بيانات عبر اللغات؛وتحقق نتائج أحدث النتائج في المسار متعدد اللغات، الترتيب الأول في لغتين (العربية والروسية) والثانية في الفرنسية من أصل 171 نظام مقدم.
This paper describes the system of the Cambridge team submitted to the SemEval-2021 shared task on Multilingual and Cross-lingual Word-in-Context Disambiguation. Building on top of a pre-trained masked language model, our system is first pre-trained on out-of-domain data, and then fine-tuned on in-domain data. We demonstrate the effectiveness of the proposed two-step training strategy and the benefits of data augmentation from both existing examples and new resources. We further investigate different representations and show that the addition of distance-based features is helpful in the word-in-context disambiguation task. Our system yields highly competitive results in the cross-lingual track without training on any cross-lingual data; and achieves state-of-the-art results in the multilingual track, ranking first in two languages (Arabic and Russian) and second in French out of 171 submitted systems.
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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.
In this paper, we introduce the first SemEval task on Multilingual and Cross-Lingual Word-in-Context disambiguation (MCL-WiC). This task allows the largely under-investigated inherent ability of systems to discriminate between word senses within and
across languages to be evaluated, dropping the requirement of a fixed sense inventory. Framed as a binary classification, our task is divided into two parts. In the multilingual sub-task, participating systems are required to determine whether two target words, each occurring in a different context within the same language, express the same meaning or not. Instead, in the cross-lingual part, systems are asked to perform the task in a cross-lingual scenario, in which the two target words and their corresponding contexts are provided in two different languages. We illustrate our task, as well as the construction of our manually-created dataset including five languages, namely Arabic, Chinese, English, French and Russian, and the results of the participating systems. Datasets and results are available at: https://github.com/SapienzaNLP/mcl-wic.
This paper describes and examines different systems to address Task 6 of SemEval-2021: Detection of Persuasion Techniques In Texts And Images, Subtask 1. The task aims to build a model for identifying rhetorical and psycho- logical techniques (such a
s causal oversimplification, name-calling, smear) in the textual content of a meme which is often used in a disinformation campaign to influence the users. The paper provides an extensive comparison among various machine learning systems as a solution to the task. We elaborate on the pre-processing of the text data in favor of the task and present ways to overcome the class imbalance. The results show that fine-tuning a RoBERTa model gave the best results with an F1-Micro score of 0.51 on the development set.
We describe the University of Alberta systems for the SemEval-2021 Word-in-Context (WiC) disambiguation task. We explore the use of translation information for deciding whether two different tokens of the same word correspond to the same sense of the
word. Our focus is on developing principled theoretical approaches which are grounded in linguistic phenomena, leading to more explainable models. We show that translations from multiple languages can be leveraged to improve the accuracy on the WiC task.
This paper describes our submission to the SemEval-2021 shared task on Lexical Complexity Prediction. We approached it as a regression problem and present an ensemble combining four systems, one feature-based and three neural with fine-tuning, freque
ncy pre-training and multi-task learning, achieving Pearson scores of 0.8264 and 0.7556 on the trial and test sets respectively (sub-task 1). We further present our analysis of the results and discuss our findings.
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