WADER at S em E val-2023 Task 9: A Weak-labelling framework for Data augmentation in t E xt Regression Tasks (2023)

Abstract: Intimacy is an essential element of human relationships and language is a crucial means of conveying it. Textual intimacy analysis can reveal social norms in different contexts and serve as a benchmark for testing computational models’ ability to understand social information. In this paper, we propose a novel weak-labeling strategy for data augmentation in text regression tasks called WADER. WADER uses data augmentation to address the problems of data imbalance and data scarcity and provides a method for data augmentation in cross-lingual, zero-shot tasks. We benchmark the performance of State-of-the-Art pre-trained multilingual language models using WADER and analyze the use of sampling techniques to mitigate bias in data and optimally select augmentation candidates. Our results show that WADER outperforms the baseline model and provides a direction for mitigating data imbalance and scarcity in text regression tasks.

## introduction intimacy is considered a fundamental element of human relationships, as recognized by several scholars @xcite @xcite . research indicates that intimacy can be modeled computationally and that textual intimacy is a crucial aspect of language @xcite . analyzing textual intimacy can reveal social norms in various contexts and serve as a benchmark to test computational models' ability to understand social information @xcite . moreover, intimacy plays a critical role in human development and well-being @xcite , and language is an essential means of conveying it in a social context. individuals negotiate intimacy in language to fulfill fundamental and strategic needs while respecting social norms. task 9 of semeval 2023 @xcite aims to quantify intimacy in a multilingual context, with evaluation on tweets from 10 languages. the training corpus for the task consists of tweets in english, spanish, italian, portuguese, french, and chinese. the testing corpus additionally contains tweets from hindi, arabic, dutch and korean. the novelty of our strategy, wader (weaklabeling strategy for data augmentation in text regression tasks) is the use of data augmentation to a) solve the problem of an imbalance distribution of data, b) augment data for a cross-lingual zero-shot set-up. wader uses the distribution to selectively sample texts with lower representation in the label distribution, uses translation to augment sentences and validates the augmentations against a baseline model, using a distribution based sampling approach. we finetune state-of-the-art pre-trained language models including xlm roberta @xcite and xlnet @xcite . real world datasets are plagued by the problems of data imbalance and data scarcity, and wader provides a direction for mitigating these problems for text regression tasks. wader ranks 32nd overall across languages, 34th on seen languages and 29th on unseen languages. our code has been released on github. 1 the main contributions of this paper are as follows: the paper is organized as follows: section 2 provides background information on the research, including a review of relevant literature, details about the task at hand, and information on the data used. section 3 presents an overview of our approach, followed by a discussion of the experimental set-up in section 4. the results of our study are analyzed in section 5, and the paper concludes with a summary of findings and future directions for research in section 6. ## experimental setup we use the original test and train set. further, we take 15% of the train set, sampled randomly from each language as our validation set. we build our models using open source available implementations of the xlm-roberta and xlnet available on huggingface. we use xlnet-base-cased xlm-roberta-base 2 and 3 . we use adam (kingma and ba, 2014) as our optimiser. the size of the the embeddings are @xmath0. the batch size is taken as 8 and the learning rate is 4e-5. we train the models for 2 epochs. experiments are performed on google colab cloud gpu. google translate api has been used to perform translations. these hyperparameters are common for all system settings including our two baselines: 1) xlm roberta finetuned on only gold data, 2) xlnet finetuned on only gold data. final submission is reported on ensemble 6, configured as per the desciption in section 3.3. ## results and discussion as we can observe from ## conclusion and future work this paper proposes a novel data augmentation framework, wader, for text regression tasks that use weak-labeling strategies to solve the problems of data imbalance and data scarcity. we also provide a method for data augmentation in cross-lingual, zero-shot tasks. our approach uses sampling techniques to mitigate bias in data and optimally select augmentation candidates. we benchmarked the performance of state-of-the-art pre-trained multilingual language models xlm roberta using wader and achieved promising results. our findings demonstrate the importance of data augmentation for mitigating data imbalance and scarcity in text regression tasks. this study's contributions provide a direction for future research in the field of computational linguistic and its applications to social information analysis.