ymoslem/acl-abstracts huggingface dataset:
How Useful is Continued Pre-Training for Generative Unsupervised Domain Adaptation? (2024)

Recent breakthroughs in scale have enabled the emergence of powerful generative language models, and the ability to fine-tune these models on various tasks by casting them into prompts or instructions. In this landscape, the problem of Unsupervised Domain Adaptation (UDA), or the problem of leveraging knowledge from a labeled source domain to an unlabeled target domain, has been left behind, with recent UDA methods still addressing discriminative classification. In particular, two popular UDA approaches, involving Continued Pre-Training (CPT) and learning domain invariant representations, have been under-explored in the generative setting, signaling a gap. In this work, we evaluate the utility of CPT for generative UDA. We first perform an empirical evaluation to measure the trade-offs between CPT and strong methods promoting domain invariance. We further evaluate how well the benefits of CPT extend to different architectures, tuning methods and data regimes. We then motivate the use of CPT by studying to what degree it benefits classification performance on the target domain. Finally, we attempt to understand the mechanism behind which CPT improves classification performance on the unlabeled target domain. Our findings suggest that a implicitly learns the downstream task while predicting masked words informative to that task. Our work connects the body of UDA research with that of instruction tuning, enabling an initial step towards a wider applicability of modern language models.
Word Boundary Information Isn`t Useful for Encoder Language Models (2024)

All existing transformer-based approaches to NLP using subword tokenisation algorithms encode whitespace (word boundary information) through the use of special space symbols (such as {\#}{\#} or {\_}) forming part of tokens. These symbols have been shown to a) lead to reduced morphological validity of tokenisations, and b) give substantial vocabulary redundancy. As such, removing these symbols has been shown to have a beneficial effect on the processing of morphologically complex words for transformer encoders in the pretrain-finetune paradigm. In this work, we explore whether word boundary information is at all useful to such models. In particular, we train transformer encoders across four different training scales, and investigate several alternative approaches to including word boundary information, evaluating on two languages (English and Finnish) with a range of tasks across different domains and problem set-ups: sentence classification datasets, NER (for token-level classification), and two classification datasets involving complex words (Superbizarre and FLOTA). Overall, through an extensive experimental setup that includes the pre-training of 35 models, we find no substantial improvements from our alternative approaches, suggesting that modifying tokenisers to remove word boundary information isn`t leading to a loss of useful information.
Beyond Link Prediction: On Pre-Training Knowledge Graph Embeddings (2024)

Knowledge graph embeddings (KGEs) provide low-dimensional representations of the entities and relations in a knowledge graph (KG) in order to reason about the KG and to inject structured knowledge into various downstream applications. Most prior work, however, focuses almost exclusively on training and evaluating KGE models for the task of link prediction. In this work, we explore KGE models as general-purpose representations of KGs and study their suitability (i) for more generally capturing properties of the KG and (ii) for downstream tasks such as entity classification and regression. For (i), we designed a new set of graph-structure prediction tasks to assess whether models capture different structures in the graph. For (ii), we investigate whether models provide useful features for a variety of downstream tasks. We found that strong link prediction performance was neither an indication that models generally capture patterns in the graph, nor that they were more useful in downstream tasks. As a result, we included our proposed graph-structure prediction tasks as additional training objectives and found that models trained with this multi-task approach generally, but not always, performed better at both graph-structure prediction and downstream tasks. However, the most suitable choice of pre-training tasks varies across KGE models and types of downstream tasks, suggesting opportunities for more research into the relation between pre-training KGE models and their usability on downstream applications.
Learn it or Leave it: Module Composition and Pruning for Continual Learning (2024)

In real-world environments, continual learning is essential for machine learning models, as they need to acquire new knowledge incrementally without forgetting what they have already learned. While pretrained language models have shown impressive capabilities on various static tasks, applying them to continual learning poses significant challenges, including avoiding catastrophic forgetting, facilitating knowledge transfer, and maintaining parameter efficiency. In this paper, we introduce MoCL-P, a novel lightweight continual learning method that addresses these challenges simultaneously. Unlike traditional approaches that continuously expand parameters for newly arriving tasks, MoCL-P integrates task representation-guided module composition with adaptive pruning, effectively balancing knowledge integration and computational overhead. Our evaluation across three continual learning benchmarks with up to 176 tasks shows that MoCL-P achieves state-of-the-art performance and improves parameter efficiency by up to three times, demonstrating its potential for practical applications where resource requirements are constrained.
Text-Guided Alternative Image Clustering (2024)

Traditional image clustering techniques only find a single grouping within visual data. In particular, they do not provide a possibility to explicitly define multiple types of clustering. This work explores the potential of large vision-language models to facilitate alternative image clustering. We propose Text-Guided Alternative Image Consensus Clustering (TGAICC), a novel approach that leverages user-specified interests via prompts to guide the discovery of diverse clusterings. To achieve this, it generates a clustering for each prompt, groups them using hierarchical clustering, and then aggregates them using consensus clustering. TGAICC outperforms image- and text-based baselines on four alternative image clustering benchmark datasets. Furthermore, using count-based word statistics, we are able to obtain text-based explanations of the alternative clusterings. In conclusion, our research illustrates how contemporary large vision-language models can transform explanatory data analysis, enabling the generation of insightful, customizable, and diverse image clusterings.
QAVSA: Question Answering using Vector Symbolic Algebras (2024)

With the advancement of large pretrained language models (PLMs), many question answering (QA) benchmarks have been developed in order to evaluate the reasoning capabilities of these models. Augmenting PLMs with external knowledge in the form of Knowledge Graphs (KGs) has been a popular method to improve their reasoning capabilities, and a common method to reason over KGs is to use Graph Neural Networks (GNNs). As an alternative to GNNs to augment PLMs, we propose a novel graph reasoning module using Vector Symbolic Algebra (VSA) graph representations and a k-layer MLP. We demonstrate that our VSA-based model performs as well as QA-GNN, a model combining a PLM and a GNN-module, on 3 multiple-choice question answering (MCQA) datasets. Our model has a simpler architecture than QA-GNN and also converges 39{\%} faster during training.
Tracking linguistic information in transformer-based sentence embeddings through targeted sparsification (2024)

Analyses of transformer-based models have shown that they encode a variety of linguistic information from their textual input. While these analyses have shed a light on the relation between linguistic information on one side, and internal architecture and parameters on the other, a question remains unanswered: how is this linguistic information reflected in sentence embeddings? Using datasets consisting of sentences with known structure, we test to what degree information about chunks (in particular noun, verb or prepositional phrases), such as grammatical number, or semantic role, can be localized in sentence embeddings. Our results show that such information is not distributed over the entire sentence embedding, but rather it is encoded in specific regions. Understanding how the information from an input text is compressed into sentence embeddings helps understand current transformer models and help build future explainable neural models.
Learning New Tasks from a Few Examples with Soft-Label Prototypes (2024)

Existing approaches to few-shot learning in NLP rely on large language models (LLMs) and/or fine-tuning of these to generalise on out-of-distribution data. In this work, we propose a novel few-shot learning approach based on soft-label prototypes (SLPs) designed to collectively capture the distribution of different classes across the input domain space. We focus on learning previously unseen NLP tasks from very few examples (4, 8, 16) per class and experimentally demonstrate that our approach achieves superior performance on the majority of tested tasks in this data-lean setting while being highly parameter efficient. We also show that our few-shot adaptation method can be integrated into more generalised learning settings, primarily meta-learning, to yield superior performance against strong baselines.
Learned Transformer Position Embeddings Have a Low-Dimensional Structure (2024)

Position embeddings have long been essential for sequence-order encoding in transformer models, yet their structure is underexplored. This study uses principal component analysis (PCA) to quantitatively compare the dimensionality of absolute position and word embeddings in BERT and ALBERT. We find that, unlike word embeddings, position embeddings occupy a low-dimensional subspace, typically utilizing under 10{\%} of the dimensions available. Additionally, the principal vectors are dominated by a few low-frequency rotational components, a structure arising independently across models.
Multi-label Learning with Random Circular Vectors (2024)

The extreme multi-label classification (XMC) task involves learning a classifier that can predict from a large label set the most relevant subset of labels for a data instance. While deep neural networks (DNNs) have demonstrated remarkable success in XMC problems, the task is still challenging because it must deal with a large number of output labels, which make the DNN training computationally expensive. This paper addresses the issue by exploring the use of random circular vectors, where each vector component is represented as a complex amplitude. In our framework, we can develop an output layer and loss function of DNNs for XMC by representing the final output layer as a fully connected layer that directly predicts a low-dimensional circular vector encoding a set of labels for a data instance. We conducted experiments on synthetic datasets to verify that circular vectors have better label encoding capacity and retrieval ability than normal real-valued vectors. Then, we conducted experiments on actual XMC datasets and found that these appealing properties of circular vectors contribute to significant improvements in task performance compared with a previous model using random real-valued vectors, while reducing the size of the output layers by up to 99{\%}.
Mitigating Semantic Leakage in Cross-lingual Embeddings via Orthogonality Constraint (2024)

Accurately aligning contextual representations in cross-lingual sentence embeddings is key for effective parallel data mining. A common strategy for achieving this alignment involves disentangling semantics and language in sentence embeddings derived from multilingual pre-trained models. However, we discover that current disentangled representation learning methods suffer from semantic leakage{---}a term we introduce to describe when a substantial amount of language-specific information is unintentionally leaked into semantic representations. This hinders the effective disentanglement of semantic and language representations, making it difficult to retrieve embeddings that distinctively represent the meaning of the sentence. To address this challenge, we propose a novel training objective, ORthogonAlity Constraint LEarning (ORACLE), tailored to enforce orthogonality between semantic and language embeddings. ORACLE builds upon two components: intra-class clustering and inter-class separation. Through experiments on cross-lingual retrieval and semantic textual similarity tasks, we demonstrate that training with the ORACLE objective effectively reduces semantic leakage and enhances semantic alignment within the embedding space.
Whitening Not Recommended for Classification Tasks in LLMs (2024)

Sentence embedding is a cornerstone in NLP. Whitening has been claimed to be an effective method to improve embeddings obtained from Large Language Models (LLMs) for sentence embedding. However, we find that the effectiveness of whitening is model-dependent and task-dependent. In particular, whitening degenerates embeddings for classification tasks. The conclusion is supported by extensive experiments. A by-product of our research is embedding evaluation platform for LLMs called SentEval+
Evaluating Document Simplification: On the Importance of Separately Assessing Simplicity and Meaning Preservation (2024)

Text simplification intends to make a text easier to read while preserving its core meaning. Intuitively and as shown in previous works, these two dimensions (simplification and meaning preservation) are often-times inversely correlated. An overly conservative text will fail to simplify sufficiently, whereas extreme simplification will degrade meaning preservation. Yet, popular evaluation metrics either aggregate meaning preservation and simplification into a single score (SARI, LENS), or target meaning preservation alone (BERTScore, QuestEval). Moreover, these metrics usually require a set of references and most previous work has only focused on sentence-level simplification. In this paper, we focus on the evaluation of document-level text simplification and compare existing models using distinct metrics for meaning preservation and simplification. We leverage existing metrics from similar tasks and introduce a reference-less metric variant for simplicity, showing that models are mostly biased towards either simplification or meaning preservation, seldom performing well on both dimensions. Making use of the fact that the metrics we use are all reference-less, we also investigate the performance of existing models when applied to unseen data (where reference simplifications are unavailable).
Malmon: A Crowd-Sourcing Platform for Simple Language (2024)

This paper presents a crowd-sourcing platform designed to address the need for parallel corpora in the field of Automatic Text Simplification (ATS). ATS aims to automatically reduce the linguistic complexity of text to aid individuals with reading difficulties, such as those with cognitive disorders, dyslexia, children, and non-native speakers. ATS does not only facilitate improved reading comprehension among these groups but can also enhance the preprocessing stage for various NLP tasks through summarization, contextual simplification, and paraphrasing. Our work introduces a language independent, openly accessible platform that crowdsources training data for ATS models, potentially benefiting low-resource languages where parallel data is scarce. The platform can efficiently aid in the collection of parallel corpora by providing a user-friendly data-collection environment. Furthermore, using human crowd-workers for the data collection process offers a potential resource for linguistic research on text simplification practices. The paper discusses the platform`s architecture, built with modern web technologies, and its user-friendly interface designed to encourage widespread participation. Through gamification and a robust admin panel, the platform incentivizes high-quality data collection and engagement from crowdworkers.
Automatic Generation and Evaluation of Reading Comprehension Test Items with Large Language Models (2024)

Reading comprehension tests are used in a variety of applications, reaching from education to assessing the comprehensibility of simplified texts. However, creating such tests manually and ensuring their quality is difficult and time-consuming. In this paper, we explore how large language models (LLMs) can be used to generate and evaluate multiple-choice reading comprehension items. To this end, we compiled a dataset of German reading comprehension items and developed a new protocol for human and automatic evaluation, including a metric we call text informativity, which is based on guessability and answerability. We then used this protocol and the dataset to evaluate the quality of items generated by Llama 2 and GPT-4. Our results suggest that both models are capable of generating items of acceptable quality in a zero-shot setting, but GPT-4 clearly outperforms Llama 2. We also show that LLMs can be used for automatic evaluation by eliciting item reponses from them. In this scenario, evaluation results with GPT-4 were the most similar to human annotators. Overall, zero-shot generation with LLMs is a promising approach for generating and evaluating reading comprehension test items, in particular for languages without large amounts of available data.
An Extensible Massively Multilingual Lexical Simplification Pipeline Dataset using the MultiLS Framework (2024)

We present preliminary findings on the MultiLS dataset, developed in support of the 2024 Multilingual Lexical Simplification Pipeline (MLSP) Shared Task. This dataset currently comprises of 300 instances of lexical complexity prediction and lexical simplification across 10 languages. In this paper, we (1) describe the annotation protocol in support of the contribution of future datasets and (2) present summary statistics on the existing data that we have gathered. Multilingual lexical simplification can be used to support low-ability readers to engage with otherwise difficult texts in their native, often low-resourced, languages.
SIERA: An Evaluation Metric for Text Simplification using the Ranking Model and Data Augmentation by Edit Operations (2024)

Automatic evaluation metrics are indispensable for text simplification (TS) research. The past TS research adopts three evaluation aspects: fluency, meaning preservation and simplicity. However, there is little consensus on a metric to measure simplicity, a unique aspect of TS compared with other text generation tasks. In addition, many of the existing metrics require reference simplified texts for evaluation. Thus, the cost of collecting reference texts is also an issue. This study proposes a new automatic evaluation metric, SIERA, for sentence simplification. SIERA employs a ranking model for the order relation of simplicity, which is trained by pairs of the original and simplified sentences. It does not require reference sentences for either training or evaluation. The sentence pairs for training are further augmented by the proposed method that utlizes edit operations to generate intermediate sentences with the simplicity between the original and simplified sentences. Using three evaluation datasets for text simplification, we compare SIERA with other metrics by calculating the correlations between metric values and human ratings. The results showed SIERA`s superiority over other metrics with a reservation that the quality of evaluation sentences is consistent with that of the training data.
Transfer Learning for Russian Legal Text Simplification (2024)

We present novel results in legal text simplification for Russian. We introduce the first dataset for such a task in Russian - a parallel corpus based on the data extracted from {\textquotedblleft}Rossiyskaya Gazeta Legal Papers{\textquotedblright}. In this study we discuss three approaches for text simplification which involve T5 and GPT model architectures. We evaluate the proposed models on a set of metrics: ROUGE, SARI and BERTScore. We also analysed the models' results on such readability indices as Flesch-Kinkaid Grade Level and Gunning Fog Index. And, finally, we performed human evaluation of simplified texts generated by T5 and GPT models; expertise was carried out by native speakers of Russian and Russian lawyers. In this research we compared T5 and GPT architectures for text simplification task and found out that GPT handles better when it is fine-tuned on dataset of coped texts. Our research makes a big step in improving Russian legal text readability and accessibility for common people.
Accessible Communication: a systematic review and comparative analysis of official English Easy-to-Understand (E2U) language guidelines (2024)

Easy-to-Understand (E2U) language varieties have been recognized by the United Nation`s Convention on the Rights of Persons with Disabilities (2006) as a means to guarantee the fundamental right to Accessible Communication. Increased awareness has driven changes in European (European Commission, 2015, 2021; European Parliament, 2016) and International legislation (ODI, 2010), prompting public-sector and other institutions to offer domain-specific content into E2U language to prevent communicative exclusion of those facing cognitive barriers (COGA, 2017; Maa{\ss}, 2020; Perego, 2020). However, guidance on what it is that makes language actually {\textquoteleft}easier to understand' is still fragmented and vague. For this reason, we carried out a systematic review of official guidelines for English Plain Language and Easy Language to identify the most effective lexical, syntactic and adaptation strategies that can reduce complexity in verbal discourse according to official bodies. This article will present the methods and preliminary results of the guidelines analysis.
LanguageTool as a CAT tool for Easy-to-Read in Spanish (2024)

Easy-to-Read (E2R) is an approach to content creation that emphasizes simplicity and clarity in language to make texts more accessible to readers with cognitive challenges or learning disabilities. The Spanish version of E2R is called Lectura F{\'a}cil (LF). E2R and its variants, such as LF, focus on straightforward language and structure to enhance readability. The manual production of such texts is both time and resource expensive. In this work, we have developed LFWriteAssist, an authoring support tool that aligns with the guidelines of LF. It is underpinned by the functionalities of LanguageTool, a free and open source grammar, style and spelling checker. Our tool assists in ensuring compliance with LF standard, provides definitions for complex, polysemic, or infrequently used terms, and acronym extensions. The tool is primarily targeted at LF creators, as it serves as an authoring aid, identifying any rule infringements and assisting with language simplifications. However, it can be used by anyone who seek to enhance text readability and inclusivity. The tool`s code is made available as open source, thereby contributing to the wider effort of creating inclusive and comprehensible content.