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Register a new userSemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC)
SEMEVAL-2021 المهمة 2: الغموض متعددة اللغات والتبلغة في السياق (MCL-WIC)
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في هذه الورقة، نقدم المهمة السامية الأولى على الغموض المتعددة اللغات والبلاية في السياق (MCL-WIC). تتيح هذه المهمة القدرة الكامنة التي تم التحقيق بها إلى حد كبير من القدرة المتأصلة إلى حد كبير في التمييز بين حواس الكلمات داخل وبصور اللغات المراد تقييمها، مما أدى إلى تقييم متطلبات جرد ذات معنى ثابت. مؤطرة كتصنيف ثنائي، مهمتنا مقسمة إلى جزأين. في المهمة الفرعية متعددة اللغات، يتعين على النظم المشاركة تحديد ما إذا كانت كلمتين مستهدفتين، كل منهما يحدث في سياق مختلف داخل نفس اللغة، معبرا عن نفس المعنى أم لا. بدلا من ذلك، في الجزء اللغوي، يطلب من النظم إجراء المهمة في سيناريو عبر اللغات، حيث يتم توفير الكلمات المستهدفة والسياقتين المقابلين بلغتين مختلفتين. نوضح مهمتنا، وكذلك بناء مجموعة بياناتنا التي أنشأنا يدويا بما في ذلك خمس لغات، وهي العربية والصينية والإنجليزية والفرنسية والروسية، ونتائج النظم المشاركة. مجموعات البيانات والنتائج متوفرة في: https://github.com/sapienzanlp/mcl-wic.
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.
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In this work, we present our approach for solving the SemEval 2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC). The task is a sentence pair classification problem where the goal is to detect whether a given word co
mmon to both the sentences evokes the same meaning. We submit systems for both the settings - Multilingual (the pair's sentences belong to the same language) and Cross-Lingual (the pair's sentences belong to different languages). The training data is provided only in English. Consequently, we employ cross-lingual transfer techniques. Our approach employs fine-tuning pre-trained transformer-based language models, like ELECTRA and ALBERT, for the English task and XLM-R for all other tasks. To improve these systems' performance, we propose adding a signal to the word to be disambiguated and augmenting our data by sentence pair reversal. We further augment the dataset provided to us with WiC, XL-WiC and SemCor 3.0. Using ensembles, we achieve strong performance in the Multilingual task, placing first in the EN-EN and FR-FR sub-tasks. For the Cross-Lingual setting, we employed translate-test methods and a zero-shot method, using our multilingual models, with the latter performing slightly better.
This paper presents a word-in-context disambiguation system. The task focuses on capturing the polysemous nature of words in a multilingual and cross-lingual setting, without considering a strict inventory of word meanings. The system applies Natural
Language Processing algorithms on datasets from SemEval 2021 Task 2, being able to identify the meaning of words for the languages Arabic, Chinese, English, French and Russian, without making use of any additional mono- or multilingual resources.
We experiment with XLM RoBERTa for Word in Context Disambiguation in the Multi Lingual and Cross Lingual setting so as to develop a single model having knowledge about both settings. We solve the problem as a binary classification problem and also ex
periment with data augmentation and adversarial training techniques. In addition, we also experiment with a 2-stage training technique. Our approaches prove to be beneficial for better performance and robustness.
In this paper, we introduce our system that we participated with at the multilingual and cross-lingual word-in-context disambiguation SemEval 2021 shared task. In our experiments, we investigated the possibility of using an all-words fine-grained wor
d sense disambiguation system trained purely on sense-annotated data in English and draw predictions on the semantic equivalence of words in context based on the similarity of the ranked lists of the (English) WordNet synsets returned for the target words decisions had to be made for. We overcame the multi,-and cross-lingual aspects of the shared task by applying a multilingual transformer for encoding the texts written in either Arabic, English, French, Russian and Chinese. While our results lag behind top scoring submissions, it has the benefit that it not only provides a binary flag whether two words in their context have the same meaning, but also provides a more tangible output in the form of a ranked list of (English) WordNet synsets irrespective of the language of the input texts. As our framework is designed to be as generic as possible, it can be applied as a baseline for basically any language (supported by the multilingual transformed architecture employed) even in the absence of any additional form of language specific training data.
Identifying whether a word carries the same meaning or different meaning in two contexts is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisati
on, information retrieval and information extraction. Most of the previous work in this area rely on language-specific resources making it difficult to generalise across languages. Considering this limitation, our approach to SemEval-2021 Task 2 is based only on pretrained transformer models and does not use any language-specific processing and resources. Despite that, our best model achieves 0.90 accuracy for English-English subtask which is very compatible compared to the best result of the subtask; 0.93 accuracy. Our approach also achieves satisfactory results in other monolingual and cross-lingual language pairs as well.
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