Probing Pre-trained Language Models for Semantic Attributes and their Values

Simile interpretation is a crucial task in natural language processing. Nowadays, pre-trained language models (PLMs) have achieved state-of-the-art performance on many tasks. However, it remains under-explored whether PLMs can interpret similes or not. In this paper, we investigate the ability of PLMs in simile interpretation by designing a novel task named Simile Property Probing, i.e., to let the PLMs infer the shared properties of similes. We construct our simile property probing datasets from both general textual corpora and human-designed questions, containing 1,633 examples covering seven main categories. Our empirical study based on the constructed datasets shows that PLMs can infer similes’ shared properties while still underperforming humans. To bridge the gap with human performance, we additionally design a knowledge-enhanced training objective by incorporating the simile knowledge into PLMs via knowledge embedding methods. Our method results in a gain of 8.58% in the probing task and 1.37% in the downstream task of sentiment classification.

Prompt-tuning has been successfully applied to support classification tasks in natural language processing and has achieved promising performance. The main characteristic of prompt-tuning based classification is to verbalize class labels and predict masked tokens like a cloze-like task. It has the advantage to make use of knowledge in pre-trained language models (PLMs). Because prompt templates are manually designed, they are more prone to overfitting. Furthermore, traditional prompt templates are appended in the tail of an original sentence. They are far from some semantic units in a sentence. It is weak to decode semantic information of an input relevant to PLMs. To aggregate more semantic information from PLMs for masked token prediction, we propose an annotation-aware prompt-tuning model for relation extraction. In our method, entity type representations are used as entity annotations. They are implanted near the site of entities in a sentence for decoding semantic information of PLMs. It is effective to make full use of knowledge in PLMs for relation extraction. In the experiment section, our method is validated on the Chinese literature text and SemEval 2010 Task datasets and achieves 89.3% and 90.6% in terms of F1-score, respectively. It achieves the state-of-the-art performance on two public datasets. The result further demonstrates the effectiveness of our model to decode semantic information in PLMs.

In this investigation, we propose a solution for the author’s gender identification task called AGI-P. This task has several real-world applications across different fields, such as marketing and advertising, forensic linguistics, sociology, recommendation systems, language processing, historical analysis, education, and language learning. We created a new dataset to evaluate our proposed method. The dataset is balanced in terms of gender using a random sampling method and consists of 1944 samples in total. We use accuracy as an evaluation measure and compare the performance of the proposed solution (AGI-P) against state-of-the-art machine learning classifiers and fine-tuned pre-trained multilingual language models such as DistilBERT, mBERT, XLM-RoBERTa, and Multilingual DEBERTa. In this regard, we also propose a customized fine-tuning strategy that improves the accuracy of the pre-trained language models for the author gender identification task. Our extensive experimental studies reveal that our solution (AGI-P) outperforms the well-known machine learning classifiers and fine-tuned pre-trained multilingual language models with an accuracy level of 92.03%. Moreover, the pre-trained multilingual language models, fine-tuned with the proposed customized strategy, outperform the fine-tuned pre-trained language models using an out-of-the-box fine-tuning strategy. The codebase and the corpus will be publicly available for scholars in this area upon publication.

Korean is the native and official language spoken by Chinese-Korean people, and Weibo is a social media software with a huge number of users in China. Currently, there is few studies related to sentiment analysis of Korean-language Weibo texts posted by Chinese-Korean users. In this paper, we propose a sentiment classification method for Chinese-Korean Weibo based on pre-trained language model and transfer learning. Firstly, we crawled the Chinese-Korean Weibo data from Sina Weibo and label them with sentiment to get the Chinese-Korean Weibo sentiment analysis (CKWSA) dataset. Secondly, to solve the problem of few training samples of the Chinese-Korean Weibo sentiment analysis dataset, we fine-tune the classifier based on the pre-trained Korean language model on the Korean Twitter sentiment analysis dataset to obtain the Korean Twitter sentiment classification model; and further fine-tune the model on CKWSA dataset to get Chinese-Korean Weibo sentiment classification model. The experiments show that the proposed classification method based on pre-trained language model and transfer learning has great performance, and there is an improvement compared other baselines on the Chinese-Korean Weibo sentiment analysis dataset.

Metonymy resolution (MR) is a crucial challenge in natural language understanding and information retrieval. Recent large-scale pre-trained language models have shown promising results in various natural language processing (NLP) tasks, including MR. Despite these achievements, current models still struggle in many real-world scenarios. Since these models rely heavily on contextual information and ignore entity information, they are prone to extract irrelevant features and overfit when fine-tuned with less training data. In this article, we propose a mixed information bottleneck framework to address the above issues, which learns optimal data representations based on the principle of minimal sufficiency. Our model can effectively mitigate irrelevant features in context and entity by using different types of information bottlenecks for entity and context information separately while reducing the dimensionality of latent representations. We show that our approach achieves state-of-the-art performance on three benchmark datasets for location MR, outperforming previous Bert-based methods by a large margin. Ablation studies and qualitative analysis show the effectiveness of our models in reducing dimensionality while extracting more relevant features.

This paper describes the models designated for the MEDIQA 2019 shared tasks by the team PANLP. We take advantages of the recent advances in pre-trained bidirectional transformer language models such as BERT (Devlin et al., 2018) and MT-DNN (Liu et al., 2019b). We find that pre-trained language models can significantly outperform traditional deep learning models. Transfer learning from the NLI task to the RQE task is also experimented, which proves to be useful in improving the results of fine-tuning MT-DNN large. A knowledge distillation process is implemented, to distill the knowledge contained in a set of models and transfer it into an single model, whose performance turns out to be comparable with that obtained by the ensemble of that set of models. Finally, for test submissions, model ensemble and a re-ranking process are implemented to boost the performances. Our models participated in all three tasks and ranked the 1st place for the RQE task, and the 2nd place for the NLI task, and also the 2nd place for the QA task.

Pre-trained language models (PLMs) have recently emerged as a powerful tool, showcasing exceptional performance not just in natural language understanding but also in the realm of biological research. The advantage of PLMs lies in their ability to leverage the structural similarity between biological sequences and natural language. PLMs offer novel solutions for protein research and drug design applications. By pre-training on extensive unlabeled biological sequences and then fine-tuning for specific tasks, PLMs have delivered remarkable results. To summarize the growing landscape of PLMs in biological research, this paper integrates exemplary PLMs and common datasets, demonstrating the potential and application prospects of PLMs in prediction and generation tasks.

Detecting fake reviews can help customers make better purchasing decisions and maintain a positive online business environment. In recent years, pre-trained language models have significantly improved the performance of natural language processing tasks. These models are able to generate different representation vectors for each word in different contexts, thus solving the challenge of multiple meanings of a word, which traditional word vector methods such as Word2Vec cannot solve, and, therefore, better capturing the text’s contextual information. In addition, we consider that reviews generally contain rich opinion and sentiment expressions, while most pre-trained language models, including BERT, lack the consideration of sentiment knowledge in the pre-training stage. Based on the above considerations, we propose a new fake review detection model based on a pre-trained language model and convolutional neural network, which is called BSTC. BSTC considers BERT, SKEP, and TextCNN, where SKEP is a pre-trained language model based on sentiment knowledge enhancement. We conducted a series of experiments on three gold-standard datasets, and the findings illustrate that BSTC outperforms state-of-the-art methods in detecting fake reviews. It achieved the highest accuracy on all three gold-standard datasets—Hotel, Restaurant, and Doctor—with 93.44%, 91.25%, and 92.86%, respectively.

Aspect-Based Sentiment Analysis (ABSA) is one of the highly challenging tasks in natural language processing. It extracts fine-grained sentiment information in user-generated reviews, as it aims at predicting the polarities towards predefined aspect categories or relevant entities in free text. Previous deep learning approaches usually rely on large-scale pre-trained language models and the attention mechanism, which applies the complete computed attention weights and does not place any restriction on the attention assignment. We argue that the original attention mechanism is not the ideal configuration for ABSA, as for most of the time only a small portion of terms are strongly related to the sentiment polarity of an aspect or entity. In this paper, we propose a masked attention mechanism customized for ABSA, with two different approaches to generate the mask. The first method sets an attention weight threshold that is determined by the maximum of all weights, and keeps only attention scores above the threshold. The second selects the top words with the highest weights. Both remove the lower score parts that are assumed to be less relevant to the aspect of focus. By ignoring part of input that is claimed irrelevant, a large proportion of input noise is removed, keeping the downstream model more focused and reducing calculation cost. Experiments on the Multi-Aspect Multi-Sentiment (MAMS) and SemEval-2014 datasets show significant improvements over state-of-the-art pre-trained language models with full attention, which displays the value of the masked attention mechanism. Recent work shows that simple self-attention in Transformer quickly degenerates to a rank-1 matrix, and masked attention may be another cure for that trend.

Demographic biases and social stereotypes are common in pretrained language models (PLMs), and a burgeoning body of literature focuses on removing the unwanted stereotypical associations from PLMs. However, when fine-tuning these bias-mitigated PLMs in downstream natural language processing (NLP) applications, such as sentiment classification, the unwanted stereotypical associations resurface or even get amplified. Since pretrain&fine-tune is a major paradigm in NLP applications, separating the debiasing procedure of PLMs from fine-tuning would eventually harm the actual downstream utility. In this paper, we propose a unified debiasing framework Causal-Debias to remove unwanted stereotypical associations in PLMs during fine-tuning. Specifically, Causal-Debias mitigates bias from a causal invariant perspective by leveraging the specific downstream task to identify bias-relevant and label-relevant factors. We propose that bias-relevant factors are non-causal as they should have little impact on downstream tasks, while label-relevant factors are causal. We perform interventions on non-causal factors in different demographic groups and design an invariant risk minimization loss to mitigate bias while maintaining task performance. Experimental results on three downstream tasks show that our proposed method can remarkably reduce unwanted stereotypical associations after PLMs are fine-tuned, while simultaneously minimizing the impact on PLMs and downstream applications. © 2023 Association for Computational Linguistics.

Pretrained language models augmented with in-domain corpora show impressive results in biomedicine and clinical Natural Language Processing (NLP) tasks in English. However, there has been minimal work in low-resource languages. Although some pioneering works have shown promising results, many scenarios still need to be explored to engineer effective pretrained language models in biomedicine for low-resource settings. This study introduces the BioBERTurk family and four pretrained models in Turkish for biomedicine. To evaluate the models, we also introduced a labeled dataset to classify radiology reports of head CT examinations. Two parts of the reports, impressions and findings, are evaluated separately to observe the performance of models on longer and less informative text. We compared the models with the Turkish BERT (BERTurk) pretrained with general domain text, multilingual BERT (mBERT), and LSTM+attention-based baseline models. The first model initialized from BERTurk and then further pretrained with biomedical corpus performs statistically better than BERTurk, multilingual BERT, and baseline for both datasets. The second model continues to pretrain the BERTurk model by using only radiology Ph.D. theses to test the effect of task-related text. This model slightly outperformed all models on the impression dataset and showed that using only radiology-related data for continual pre-training could be effective. The third model continues to pretrain by adding radiology theses to the biomedical corpus but does not show a statistically meaningful difference for both datasets. The final model combines radiology and biomedicine corpora with the corpus of BERTurk and pretrains a BERT model from scratch. This model is the worst-performing model of the BioBERT family, even worse than BERTurk and multilingual BERT.

Figurative language generation (FLG) is the task of reformulating a given text to include a desired figure of speech, such as a hyperbole, a simile, and several others, while still being faithful to the original context. This is a fundamental, yet challenging task in Natural Language Processing (NLP), which has recently received increased attention due to the promising performance brought by pre-trained language models. Our survey provides a systematic overview of the development of FLG, mostly in English, starting with the description of some common figures of speech, their corresponding generation tasks, and datasets. We then focus on various modelling approaches and assessment strategies, leading us to discussing some challenges in this field, and suggesting some potential directions for future research. To the best of our knowledge, this is the first survey that summarizes the progress of FLG including the most recent development in NLP. We also organize corresponding resources, e.g., article lists and datasets, and make them accessible in an open repository. We hope this survey can help researchers in NLP and related fields to easily track the academic frontier, providing them with a landscape and a roadmap of this area.

With the recent advances in deep learning, different approaches to improving pre-trained language models (PLMs) have been proposed. PLMs have advanced state-of-the-art (SOTA) performance on various natural language processing (NLP) tasks such as machine translation, text classification, question answering, text summarization, information retrieval, recommendation systems, named entity recognition, etc. In this paper, we provide a comprehensive review of prior embedding models as well as current breakthroughs in the field of PLMs. Then, we analyse and contrast the various models and provide an analysis of the way they have been built (number of parameters, compression techniques, etc.). Finally, we discuss the major issues and future directions for each of the main points.

With the advent of Neural Networks, Neural Machine Translation (NMT) has been a booming area of research in Natural Language Processing. Indic languages possess less significant parallel and monolingual corpus than English and other European languages. Even in Indic languages, many Indo-Aryan and Dravidian languages fall into low-resource languages, and techniques like Back Translation, Unsupervised Neural Machine Translation (UNMT), and Transfer Learning help achieve better translation accuracy. This paper uses the UNMT approach with a pre-trained Cross-Lingual Language Model (XLM) for the English to Kannada language pair. Our proposed methodology has achieved a 0.61 BLEU score in English to Kannada and a 0.32 BLEU score in Kannada to English translation.

Pretrained language models (PLMs) have achieved remarkable results in various natural language processing tasks. As the performance of the model increases, it is also accompanied by more computational consumption and longer inference time, which makes deploying PLMs in edge devices for low-latency applications challenging. To address this issue, recent studies have recommended applying either model compression or early-exiting techniques to accelerate the inference. However, model compression permanently discards the modules of the model, leading to a decline in model performance. Train the PLMs backbone and the early-exiting classifier separately with early-exiting strategies. It not only brings extra training cost but also loses semantic information from higher layers, resulting in unreliable decisions of early-exiting classifiers. In this study, a weighted ensemble self-distillation method was proposed to improve the early-exiting strategy, which well balanced the performance and the inference time. It enables early-exiting classifiers to obtain rich semantic information from different layers with an attention mechanism according to the contribution of each layer to the final prediction. Furthermore, it simultaneously performs weighted ensemble self-distillation and fine-tuning of the PLMs backbone so that the PLMs can be fine-tuned in the training process of the early-exiting classifier to preserve the performance as much as possible. The experimental results show that the inference of the proposed model was accelerated at the minimum cost of performance loss, thus outperforming the previous early-exiting models. The code is available at: https://github.com/JunKong5/WestBERT.