Large Pre-trained Language Models Research Articles

Building a Large Corpus and Pre-trained Language Models from National and Local Assembly Minutes

Building a Large Corpus and Pre-trained Language Models from National and Local Assembly Minutes

A Narrative Review on Human Anxiety Detection in Social Media using Natural Language Processing

Currently, with the boundless proliferation of social media platforms, it is undoubted that the world is in the middle of a significant phase of social media evolution. These virtually created user environments allow users to create, share, and interact with content. Most people who interact with social media make a pitch to share their mood, tension, feelings, and behaviour unhesitatingly with the community. Mostly, these data can be taken as a mirror that reflects the mental health status of a person, such as stress, anxiety, and suffering. With increased social media penetration, people tend to share their disturbances rather than suffer alone. Anxiety can be taken as an ordinary human emotion which prepares the human body for potentially vulnerable situations. It would be beneficial if there were any system that could predict if a person is anxious before going through a critical situation that would have to be clinically treated. This study is a narrative review of anxiety detection over the past ten years. Furthermore, this qualitative study discusses an overview of anxiety detection using Natural Language Processing (NLP) and key concepts highlighting emerging trends. We conclude that the existing state-of-the-art social media anxiety detection mechanisms can be outperformed using deep learning based on large pre-trained language models. Furthermore, it can be safely stated that a specific pre-trained large language model for social media anxiety detection is a crucial necessity yet to be fulfilled.

Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation

Relational Web tables provide valuable resources for numerous downstream applications, making table understanding, especially column annotation that identifies semantic types and relations of columns, a hot topic in the field of data management. Despite recent efforts to improve different tasks in table understanding by using the power of large pre-trained language models, existing methods heavily rely on large-scale and high-quality labeled instances, while they still suffer from the data sparsity problem due to the imbalanced data distribution among different classes. In this paper, we propose the Watchog framework, which employs contrastive learning techniques to learn robust representations for tables by leveraging a large-scale unlabeled table corpus with minimal overhead. Our approach enables the learned table representations to enhance fine tuning with much fewer additional labeled instances than in prior studies for downstream column annotation tasks. Besides, we further proposed optimization techniques for semi-supervised settings. Experimental results on popular benchmarking datasets illustrate the superiority of our proposed techniques in two column annotation tasks under different settings. In particular, our Watchog framework effectively alleviates the class imbalance issue caused by a long-tailed label distribution. In the semi-supervised setting, Watchog outperforms the best-known method by up to 26% and 41% in Micro and Macro F1 scores, respectively, on the task of semantic type detection.

Event-Centric Temporal Knowledge Graph Construction: A Survey

Textual documents serve as representations of discussions on a variety of subjects. These discussions can vary in length and may encompass a range of events or factual information. Present trends in constructing knowledge bases primarily emphasize fact-based common sense reasoning, often overlooking the temporal dimension of events. Given the widespread presence of time-related information, addressing this temporal aspect could potentially enhance the quality of common-sense reasoning within existing knowledge graphs. In this comprehensive survey, we aim to identify and evaluate the key tasks involved in constructing temporal knowledge graphs centered around events. These tasks can be categorized into three main components: (a) event extraction, (b) the extraction of temporal relationships and attributes, and (c) the creation of event-based knowledge graphs and timelines. Our systematic review focuses on the examination of available datasets and language technologies for addressing these tasks. An in-depth comparison of various approaches reveals that the most promising results are achieved by employing state-of-the-art models leveraging large pre-trained language models. Despite the existence of multiple datasets, a noticeable gap exists in the availability of annotated data that could facilitate the development of comprehensive end-to-end models. Drawing insights from our findings, we engage in a discussion and propose four future directions for research in this domain. These directions encompass (a) the integration of pre-existing knowledge, (b) the development of end-to-end systems for constructing event-centric knowledge graphs, (c) the enhancement of knowledge graphs with event-centric information, and (d) the prediction of absolute temporal attributes.

Quantifying gender bias towards politicians in cross-lingual language models.

Recent research has demonstrated that large pre-trained language models reflect societal biases expressed in natural language. The present paper introduces a simple method for probing language models to conduct a multilingual study of gender bias towards politicians. We quantify the usage of adjectives and verbs generated by language models surrounding the names of politicians as a function of their gender. To this end, we curate a dataset of 250k politicians worldwide, including their names and gender. Our study is conducted in seven languages across six different language modeling architectures. The results demonstrate that pre-trained language models' stance towards politicians varies strongly across analyzed languages. We find that while some words such as dead, and designated are associated with both male and female politicians, a few specific words such as beautiful and divorced are predominantly associated with female politicians. Finally, and contrary to previous findings, our study suggests that larger language models do not tend to be significantly more gender-biased than smaller ones.

API Entity and Relation Joint Extraction from Text via Dynamic Prompt-tuned Language Model

Extraction of Application Programming Interfaces (APIs) and their semantic relations from unstructured text (e.g., Stack Overflow) is a fundamental work for software engineering tasks (e.g., API recommendation). However, existing approaches are rule based and sequence labeling based. They must manually enumerate the rules or label data for a wide range of sentence patterns, which involves a significant amount of labor overhead and is exacerbated by morphological and common-word ambiguity. In contrast to matching or labeling API entities and relations, this article formulates heterogeneous API extraction and API relation extraction task as a sequence-to-sequence generation task and proposes the API Entity-Relation Joint Extraction framework (AERJE), an API entity-relation joint extraction model based on the large pre-trained language model. After training on a small number of ambiguous but correctly labeled data, AERJE builds a multi-task architecture that extracts API entities and relations from unstructured text using dynamic prompts. We systematically evaluate AERJE on a set of long and ambiguous sentences from Stack Overflow. The experimental results show that AERJE achieves high accuracy and discrimination ability in API entity-relation joint extraction, even with zero or few-shot fine-tuning.

Language model as an Annotator: Unsupervised context-aware quality phrase generation

Phrase mining is a fundamental text mining task that aims to identify quality phrases from context. Nevertheless, the scarcity of extensive gold labels datasets, demanding substantial annotation efforts from experts, renders this task exceptionally challenging. Furthermore, the emerging, infrequent, and domain-specific nature of quality phrases present further challenges in dealing with this task. Therefore, in this paper, we propose LMPhrase, a novel unsupervised context-aware quality phrase mining framework built upon large pre-trained language models (LMs). Specifically, we first mine quality phrases as silver labels by employing a parameter-free probing technique called Perturbed Masking on the pre-trained language model BERT (coined as Annotator). In contrast to typical statistic-based or distantly-supervised methods, our silver labels, derived from large pre-trained language models, take into account rich contextual information contained in the LMs. As a result, they bring distinct advantages in preserving informativeness, concordance, and completeness of quality phrases. Secondly, training a discriminative span prediction model heavily relies on massive annotated data and is likely to face the risk of overfitting silver labels. Alternatively, motivated by recent success in formulating language understanding problems such as named entity recognition and sentiment analysis as generation tasks, we formalize phrase tagging task as the sequence generation problem by directly fine-tuning on the Sequence-to-Sequence (Seq2Seq) pre-trained language model BART with silver labels (coined as Generator). Finally, we merge the quality phrases from both the Annotator and Generator as the final predictions, considering their complementary nature and distinct characteristics. Extensive experiments show that our LMPhrase consistently outperforms all the existing competitors across two different granularity phrase mining tasks, where each task is tested on two different domain datasets. The promising results show the superiority of our framework with pre-trained language model.

KC-GEE: knowledge-based conditioning for generative event extraction

AbstractEvent extraction is an important, but challenging task. Many existing techniques decompose it into event and argument detection/classification subtasks, which are complex structured prediction problems. Generation-based extraction techniques lessen the complexity of the problem formulation and are able to leverage the reasoning capabilities of large pretrained language models. However, they still suffer from poor zero-shot generalizability and are ineffective in handling long contexts such as documents. We propose a generative event extraction model, KC-GEE, that addresses these limitations. A key contribution of KC-GEE is a novel knowledge-based conditioning technique that injects the schema of candidate event types as the prefix into each layer of an encoder-decoder language model. This enables effective zero-shot learning and improves supervised learning. Our experiments on two benchmark datasets demonstrate the strong performance of our KC-GEE model. It achieves particularly strong results in the challenging document-level extraction task and in the zero-shot learning setting, outperforming state-of-the-art models by up to 5.4 absolute F1 points.

A lightweight approach based on prompt for few-shot relation extraction

Few-shot relation extraction (FSRE) aims to predict the relation between two entities in a sentence using a few annotated samples. Many works solve the FSRE problem by training complex models with a huge number of parameters, which results in longer processing times to obtain results. Some recent works focus on introducing relation information into Prototype Networks in various ways. However, most of these methods obtain entity and relation representations by fine-tuning large pre-trained language models. This implies that a copy of the complete pre-trained model needs to be saved after fine-tuning for each specific task, leading to a shortage of computing and space resources. To address this problem, in this paper, we introduce a light approach that utilizes prompt-learning to assist in fine-tuning model by adjusting fewer parameters. To obtain a better prototype of relation, we design a new enhanced fusion module to fuse relation information and original prototype. We conduct extensive experiments on the common FSRE datasets FewRel 1.0 and FewRel 2.0 to varify the advantages of our method, the results show that our model achieves state-of-the-art performance.

Enhancing code summarization with action word prediction

Code summarization refers to automatically generating concise description in natural language from a code snippet. Good code summaries could effectively facilitate program comprehension and software maintenance. In recent years, various learning-based code summarization techniques have achieved impressive performance. Most of these models treat code summarization as an end-to-end model and directly generate the summaries, which ignores the fact that action words are crucial to code summaries. An essential characteristic of code summaries is the concentration of action word distribution. For instance, in the Funcom dataset, the top forty most-common action words account for 72% of all samples. To incorporate this valuable prior domain knowledge into code summarization models, we develop a method for assisting code summarization through an additional action word prediction module, where an action predictor is employed to predict the primary action in the code summary, which is then used as a prompt to enhance the performance of the summary generation model. Our approach can be conveniently integrated into the existing models. We evaluate our approach on two Java datasets and a C/C++ dataset. The results show that our approach can efficiently improve the performance of the code summarization models. Furthermore, our action word prediction module can enhance the performance of a large pre-trained language model by prompting it with the predicted action words. This work suggests that a precise action word prediction model can significantly improve the performance of code summarization through the proposed action word guidance mechanism.

BERT models for Brazilian Portuguese: Pretraining, evaluation and tokenization analysis

Recent advances in language representation using neural networks have made it viable to transfer the learned internal states of large pretrained language models (LMs) to downstream natural language processing (NLP) tasks. This transfer learning approach improves the overall performance on many tasks and is highly beneficial when labeled data is scarce, making pretrained LMs valuable resources specially for languages with few annotated training examples. In this work, we train BERT (Bidirectional Encoder Representations from Transformers) models for Brazilian Portuguese, which we nickname BERTimbau. We evaluate our models on three downstream NLP tasks: sentence textual similarity, recognizing textual entailment, and named entity recognition. Our models improve the state-of-the-art in all of these tasks, outperforming Multilingual BERT and confirming the effectiveness of large pretrained LMs for Portuguese. We release our models to the community hoping to provide strong baselines for future NLP research: https://github.com/neuralmind-ai/portuguese-bert.

Exploring How AI Answering Models Understand and Respond in Context.

Abstract: Question answering (QA) is an important capability for artificial intelligence systems to assist humans by providing relevant information. In recent years, large pretrained language models like BERT and GPT have shown promising results on QA tasks. This paper explores how two state-of-the-art models, BERT and GPT-4, understand questions and generate answers in conversational contexts. We first provide an overview of the architectures and pretrained objectives of both models. Then we conduct experiments on two QA datasets to evaluate each model's ability to reason about questions, leverage context and background knowledge, and provide natural and logically consistent responses. Quantitative results reveal the strengths and weaknesses of each model, with BERT demonstrating stronger reasoning abilities but GPT-4 generating more human-like responses. Through qualitative error analysis, we identify cases where each model fails and propose explanations grounded in their underlying architectures and pretraining approaches. This analysis provides insights into the current capabilities and limitations of large pretrained models for open-domain conversational QA. The results suggest directions for improving both types of models, including combining their complementary strengths, increasing reasoning ability, and incorporating more conversational context. This work highlights important considerations in developing AI systems that can intelligently understand and respond to natural language questions.

A semantically enhanced text retrieval framework with abstractive summarization

AbstractRecently, large pretrained language models (PLMs) have led a revolution in the information retrieval community. In most PLMs‐based retrieval frameworks, the ranking performance broadly depends on the model structure and the semantic complexity of the input text. Sequence‐to‐sequence generative models for question answering or text generation have proven to be competitive, so we wonder whether these models can improve ranking effectiveness by enhancing input semantics. This article introduces SE‐BERT, a semantically enhanced bidirectional encoder representation from transformers (BERT) based ranking framework that captures more semantic information by modifying the input text. SE‐BERT utilizes a pretrained generative language model to summarize both sides of the candidate passage and concatenate them into a new input sequence, allowing BERT to acquire more semantic information within the constraints of the input sequence's length. Experimental results from two Text Retrieval Conference datasets demonstrate that our approach's effectiveness increasing as the length of the input text increases.

A self-supervised language model selection strategy for biomedical question answering

Large neural-based Pre-trained Language Models (PLM) have recently gained much attention due to their noteworthy performance in many downstream Information Retrieval (IR) and Natural Language Processing (NLP) tasks. PLMs can be categorized as either general-purpose, which are trained on resources such as large-scale Web corpora, and domain-specific which are trained on in-domain or mixed-domain corpora. While domain-specific PLMs have shown promising performance on domain-specific tasks, they are significantly more computationally expensive compared to general-purpose PLMs as they have to be either retrained or trained from scratch. The objective of our work in this paper is to explore whether it would be possible to leverage general-purpose PLMs to show competitive performance to domain-specific PLMs without the need for expensive retraining of the PLMs for domain-specific tasks. By focusing specifically on the recent BioASQ Biomedical Question Answering task, we show how different general-purpose PLMs show synergistic behaviour in terms of performance, which can lead to overall notable performance improvement when used in tandem with each other. More concretely, given a set of general-purpose PLMs, we propose a self-supervised method for training a classifier that systematically selects the PLM that is most likely to answer the question correctly on a per-input basis. We show that through such a selection strategy, the performance of general-purpose PLMs can become competitive with domain-specific PLMs while remaining computationally light since there is no need to retrain the large language model itself. We run experiments on the BioASQ dataset, which is a large-scale biomedical question-answering benchmark. We show that utilizing our proposed selection strategy can show statistically significant performance improvements on general-purpose language models with an average of 16.7% when using only lighter models such as DistilBERT and DistilRoBERTa, as well as 14.2% improvement when using relatively larger models such as BERT and RoBERTa and so, their performance become competitive with domain-specific large language models such as PubMedBERT.

Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey

Large, pre-trained language models (PLMs) such as BERT and GPT have drastically changed the Natural Language Processing (NLP) field. For numerous NLP tasks, approaches leveraging PLMs have achieved state-of-the-art performance. The key idea is to learn a generic, latent representation of language from a generic task once, then share it across disparate NLP tasks. Language modeling serves as the generic task, one with abundant self-supervised text available for extensive training. This article presents the key fundamental concepts of PLM architectures and a comprehensive view of the shift to PLM-driven NLP techniques. It surveys work applying the pre-training then fine-tuning, prompting, and text generation approaches. In addition, it discusses PLM limitations and suggested directions for future research.

Efficient dynamic feature adaptation for cross language sentiment analysis with biased adversarial training

Fine-tuning a large multi-lingual pretrained language model demonstrates impressive results in cross-language understanding. However, it still suffers when the training and test data have different distributions owing to various languages and domains. On one hand, annotating target data for different languages or domains is time-consuming or infeasible. On the other hand, fine-tuning a large language model often incurs high computational costs. In this paper, we aim to develop an efficient and effective adaptation framework for cross-language sentiment analysis based on a fixed pretrained multi-lingual model. Specifically, we propose a Dynamic Feature Adaptation (DFA) module to fully leverage the features from different layers of the pretrained model such that its large backbone is not involved during adaptation training. Furthermore, we observe that traditional adversarial domain adaptation training could compromise the discriminative information of the model by pushing source and target features towards each other. The source features obtained with supervised training preserved the discriminability of the model, which should be less affected. Therefore, we propose a novel Biased Adversarial Training (BAT) method, that encourages only the target features towards source features. Extensive experimental results on various cross-lingual and cross-lingual-and-domain sentiment analysis tasks demonstrate the superiority of the proposed framework. Additionally, several ablation studies are conducted to validate the effectiveness of each proposed module.

Cogito Ergo Summ: Abstractive Summarization of Biomedical Papers via Semantic Parsing Graphs and Consistency Rewards

The automatic synthesis of biomedical publications catalyzes a profound research interest elicited by literature congestion. Current sequence-to-sequence models mainly rely on the lexical surface and seldom consider the deep semantic interconnections between the entities mentioned in the source document. Such superficiality translates into fabricated, poorly informative, redundant, and near-extractive summaries that severely restrict their real-world application in biomedicine, where the specialized jargon and the convoluted facts further emphasize task complexity. To fill this gap, we argue that the summarizer should acquire semantic interpretation over input, exploiting structured and unambiguous representations to capture and conserve the most relevant parts of the text content. This paper presents CogitoErgoSumm, the first framework for biomedical abstractive summarization equipping large pre-trained language models with rich semantic graphs. Precisely, we infuse graphs from two complementary semantic parsing techniques with different goals and granularities—Event Extraction and Abstract Meaning Representation, also designing a reward signal to maximize information content preservation through reinforcement learning. Extensive quantitative and qualitative evaluations on the CDSR dataset show that our solution achieves competitive performance according to multiple metrics, despite using 2.5x fewer parameters. Results and ablation studies indicate that our joint text-graph model generates more enlightening, readable, and consistent summaries. Code available at: https://github.com/disi-unibo-nlp/cogito-ergo-summ.

Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction

The typical way for relation extraction is fine-tuning large pre-trained language models on task-specific datasets, then selecting the label with the highest probability of the output distribution as the final prediction. However, the usage of the Top-k prediction set for a given sample is commonly overlooked. In this paper, we first reveal that the Top-k prediction set of a given sample contains useful information for predicting the correct label. To effectively utilizes the Top-k prediction set, we propose Label Graph Network with Top-k Prediction Set, termed as KLG. Specifically, for a given sample, we build a label graph to review candidate labels in the Top-k prediction set and learn the connections between them. We also design a dynamic k selection mechanism to learn more powerful and discriminative relation representation. Our experiments show that KLG achieves the best performances on three relation extraction datasets. Moreover, we observe thatKLG is more effective in dealing with long-tailed classes.

Session-based cyberbullying detection in social media: A survey

Cyberbullying is a pervasive problem in online social media, where a bully abuses a victim through a social media session. By investigating cyberbullying perpetrated through social media sessions, recent research has looked into mining patterns and features for modelling and understanding the two defining characteristics of cyberbullying: repetitive behaviour and power imbalance. In this survey paper, we define a framework that encapsulates four different steps session-based cyberbullying detection should go through, and discuss the multiple challenges that differ from single text-based cyberbullying detection. Based on this framework, we provide a comprehensive overview of session-based cyberbullying detection in social media, delving into existing efforts from a data and methodological perspective. Our review leads us to proposing evidence-based criteria for a set of best practices to create session-based cyberbullying datasets. In addition, we perform benchmark experiments comparing the performance of state-of-the-art session-based cyberbullying detection models as well as large pre-trained language models across two different datasets. Through our review, we also put forth a set of open challenges as future research directions.

Contextualized medication event extraction with striding NER and multi-turn QA

This paper describes contextualized medication event extraction for automatically identifying medication change events with their contexts from clinical notes. The striding named entity recognition (NER) model extracts medication name spans from an input text sequence using a sliding-window approach. Specifically, the striding NER model separates the input sequence into a set of overlapping subsequences of 512 tokens with 128 tokens of stride, processing each subsequence using a large pre-trained language model and aggregating the outputs from the subsequences. The event and context classification has been done with multi-turn question-answering (QA) and span-based models. The span-based model classifies the span of each medication name using the span representation of the language model. In the QA model, event classification is augmented with questions in classifying the change events of each medication name and the context of the change events, while the model architecture is a classification style that is the same as the span-based model. We evaluated our extraction system on the n2c2 2022 Track 1 dataset, which is annotated for medication extraction (ME), event classification (EC), and context classification (CC) from clinical notes. Our system is a pipeline of the striding NER model for ME and the ensemble of the span-based and QA-based models for EC and CC. Our system achieved a combined F-score of 66.47% for the end-to-end contextualized medication event extraction (Release 1), which is the highest score among the participants of the n2c2 2022 Track 1.