Lexical complexity plays an important role in reading comprehension. lexical complexity prediction (LCP) can not only be used as a part of Lexical Simplification systems, but also as a stand-alone application to help people better reading. This paper presents the winning system we submitted to the LCP Shared Task of SemEval 2021 that capable of dealing with both two subtasks. We first perform fine-tuning on numbers of pre-trained language models (PLMs) with various hyperparameters and different training strategies such as pseudo-labelling and data augmentation. Then an effective stacking mechanism is applied on top of the fine-tuned PLMs to obtain the final prediction. Experimental results on the Complex dataset show the validity of our method and we rank first and second for subtask 2 and 1.
Multi-wordsContext1: SEM confirmed many of the observations made by confocal microscopy. Complexity score: 0.64473 Context2: SJ and SVJ carried out confocal microscopy on whole-mounts of stria vascularis. Complexity score: 0.7750
Single word
Context1:They shall be to you for a refuge from the avenger of blood. Complexity score: 0.3475 Context2: There will be a pavilion for a shade in the daytime from the heat, and for a refuge and for a shelter from storm and from rain.
Neural relation extraction models have shown promising results in recent years; however, the model performance drops dramatically given only a few training samples. Recent works try leveraging the advance in few-shot learning to solve the low resource problem, where they train label-agnostic models to directly compare the semantic similarities among context sentences in the embedding space. However, the label-aware information, i.e., the relation label that contains the semantic knowledge of the relation itself, is often neglected for prediction. In this work, we propose a framework considering both label-agnostic and label-aware semantic mapping information for low resource relation extraction. We show that incorporating the above two types of mapping information in both pretraining and fine-tuning can significantly improve the model performance on low-resource relation extraction tasks.
The objective of this research is to push the frontiers in Automated Machine Learning, specifically targeting Deep Learning. We analyse ChaLearn's Automated Deep Learning challenge whose design features include: (i) Code submissions entirely blind-tested, on five classification problems during development, then ten others during final testing. (ii) Raw data from various modalities (image, video, text, speech, tabular data), formatted as tensors. (iii) Emphasis on "any-time learning" strategies by imposing fixed time/memory resources and using the Area under Learning curve as metric. (iv) Baselines provided, including "Baseline 3", combining top-ranked solutions of past rounds (AutoCV, AutoNLP, AutoSpeech,and AutoSeries). (v) No Deep Learning imposed. Principal findings: (1) The top two winners passed all final tests without failure, a significant step towards true automation. Their solutions were open-sourced.(2) Despite our effort to format all datasets uniformly to encourage generic solutions, the participants adopted specific workflows for each modality. (3) Any-time learning was addressed successfully, without sacrificing final performance. (4) Although some solutions improved over Baseline 3, it strongly influenced many. (5) Deep Learning solutions dominated, but Neural Architecture Search was impractical within the time budget imposed. Most solutions relied on fixed-architecture pre-trained networks, with fine-tuning. Ablation studies revealed the importance of meta-learning, ensembling, and efficient data loading, while data-augmentation is not critical.
This paper describes the winning system for SemEval-2021 Task 7: Detecting and Rating Humor and Offense. Our strategy is stacking diverse pre-trained language models (PLMs) such as RoBERTa and ALBERT. We first perform fine-tuning on these two PLMs with various hyperparameters and different training strategies. Then a valid stacking mechanism is applied on top of the fine-tuned PLMs to get the final prediction. Experimental results on the dataset released by the organizer of the task show the validity of our method and we win first place and third place for subtask 2 and 1a.
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