@inproceedings{li-etal-2022-lingjing,
title = "{L}ing{J}ing at {S}em{E}val-2022 Task 1: Multi-task Self-supervised Pre-training for Multilingual Reverse Dictionary",
author = "Li, Bin and
Weng, Yixuan and
Xia, Fei and
He, Shizhu and
Sun, Bin and
Li, Shutao",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.4",
doi = "10.18653/v1/2022.semeval-1.4",
pages = "29--35",
abstract = "This paper introduces the approach of Team LingJing{'}s experiments on SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings (CODWOE). This task aims at comparing two types of semantic descriptions and including two sub-tasks: the definition modeling and reverse dictionary track. Our team focuses on the reverse dictionary track and adopts the multi-task self-supervised pre-training for multilingual reverse dictionaries. Specifically, the randomly initialized mDeBERTa-base model is used to perform multi-task pre-training on the multilingual training datasets. The pre-training step is divided into two stages, namely the MLM pre-training stage and the contrastive pre-training stage. The experimental results show that the proposed method has achieved good performance in the reverse dictionary track, where we rank the 1-st in the Sgns targets of the EN and RU languages. All the experimental codes are open-sourced at https://github.com/WENGSYX/Semeval.",
}
@inproceedings{xia-etal-2022-lingjing,
title = "{L}ing{J}ing at {S}em{E}val-2022 Task 3: Applying {D}e{BERT}a to Lexical-level Presupposed Relation Taxonomy with Knowledge Transfer",
author = "Xia, Fei and
Li, Bin and
Weng, Yixuan and
He, Shizhu and
Sun, Bin and
Li, Shutao and
Liu, Kang and
Zhao, Jun",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.30",
doi = "10.18653/v1/2022.semeval-1.30",
pages = "239--246",
abstract = "This paper presents the results and main findings of our system on SemEval-2022 Task 3 Presupposed Taxonomies: Evaluating Neural Network Semantics (PreTENS). This task aims at semantic competence with specific attention on the evaluation of language models, which is a task with respect to the recognition of appropriate taxonomic relations between two nominal arguments. Two sub-tasks including binary classification and regression are designed for the evaluation. For the classification sub-task, we adopt the DeBERTa-v3 pre-trained model for fine-tuning datasets of different languages. Due to the small size of the training datasets of the regression sub-task, we transfer the knowledge of classification model (i.e., model parameters) to the regression task. The experimental results show that the proposed method achieves the best results on both sub-tasks. Meanwhile, we also report negative results of multiple training strategies for further discussion. All the experimental codes are open-sourced at https://github.com/WENGSYX/Semeval.",
}