This paper explores the significant advancements in generating Structured Query Language (SQL) from natural language, primarily driven by Large Language Models (LLMs). These advancements have led to the development of sophisticated text-to-SQL integrated applications, enabling easier database (DB) querying for users unfamiliar with SQL syntax using natural language queries. However, reliance on LLMs exposes these applications to potential attacks through the introduction of malicious prompts or by compromising models with malicious data during the training phase. Such attacks pose severe risks, including unauthorized data access or even complete DB destruction upon success. To address these concerns, we introduce a novel large-scale dataset comprising malicious and safe prompts along with their corresponding SQL queries, enabling model fine-tuning on malicious query detection tasks. Moreover, we propose the implementation of two transformer-based classification solutions to aid in the detection of malicious attacks. Finally, we present a secure agent-based text-to-SQL architecture that incorporates these solutions to enhance overall system security, resulting in a 70% security enhancement overall compared to solely relying on a conventional text-to-SQL model.

When humans solve complex problems, they typically construct, reflect, and revise sequences of ideas, hypotheses, and beliefs until a final decision or conclusion is reached. Contrary to this, current machine learning models are mostly trained to map an input to one single and fixed output. In this paper, we investigate how we can equip models with the ability to represent, construct, and evaluate a second, third, and $k$-th thought within their prediction process. Drawing inspiration from Hegel’s dialectics, we propose and evaluate the thought flow concept which constructs a sequence of predictions. We present a self-correction mechanism which (a) is trained to estimate the model’s correctness and which (b) performs iterative prediction updates based on the gradient of the correctness prediction. We introduce our method focusing initially on question answering (QA) and carry out extensive experiments which demonstrate that (i) our method is able to correct its own predictions and that (ii) it can improve model performance by a large margin. In addition, we conduct a qualitative analysis of thought flow correction patterns and explore how thought flow predictions affect users’ human-AI collaboration in a crowdsourcing study. We find that (iii) thought flows improve user performance and are perceived as more natural, correct, and intelligent regarding single and/or top-3 predictions.

The aim of this article is to propose a new neural machine translation (NMT) evaluation method based on the non-inferiority principle. In order to do that, we evaluate raw machine translation (MT) in terms of naturalness, which for this research is defined as not just the lack of fluency errors but also meeting the linguistic expectations of Galician end users when reading original texts in Galician. Our main objective is, in the first place, to validate the new methodology presented in our previous study by evaluating an NMT engine from Spanish into Galician for the social media domain that was retrained with a new Twitter corpus. This new methodology and NMT engine were applied after analyzing the conclusions of a pilot survey conducted among Twitter users to evaluate their perception of tweets translated from Spanish into Galician with our NMT engine created with a corpus of tweets. As in our preliminary study, our aim is to propose a robust quality approximation method based on the reception parameters of end users’ perceptions. This new survey was conducted in December of 2022 with the participation of 228 Galician-speaking Twitter users. Among the main changes proposed are the inclusion of more information about the participant profile, so the non-inferiority principle can be also evaluated according to these parameters; the inclusion of a new typology of tweets, the threads; the provision of context by means of presenting the tweets in their original display as shown in the Twitter app; a change in the number of tweets evaluated and the number of different questionnaires; the change in the distribution of the questionnaires; and the inclusion of an error classification human evaluation conducted by professional linguists to correlate the findings. We will present the steps carried out following the conclusions of the pilot study, describe the new study’s design, analyze the new findings, and present the final conclusions regarding the engine and the evaluation method based on the non-inferiority principle. Finally, we will also provide some examples of the use of this new methodology in the translation industry.

Datasets serve as crucial training resources and model performance trackers. However, existing datasets have exposed a plethora of problems, inducing biased models and unreliable evaluation results. In this paper, we propose a model-agnostic dataset evaluation framework for automatic dataset quality evaluation. We seek the statistical properties of the datasets and address three fundamental dimensions: reliability, difficulty, and validity, following a Classical Test Theory (CTT). Taking the named entity recognition (NER) datasets as a case study, we introduce nine statistical metrics for a statistical dataset evaluation framework. Specifically, we investigate the reliability of a NER dataset with three metrics, including Redundancy, Accuracy, and Leakage Ratio. We assess the dataset difficulty through four metrics: Unseen Entity Ratio, Entity Ambiguity Degree, Entity Density, and Model Differentiation. For validity, we introduce the Entity Imbalance Degree and Entity-Null Rate to evaluate the effectiveness of the dataset in assessing language model performance. Experimental results validate that our evaluation framework effectively assesses various aspects of the dataset quality. Furthermore, we study how the dataset scores on our statistical metrics affect the model performance and appeal for dataset quality evaluation or targeted dataset improvement before training or testing models.

Data-driven neural word embeddings (NWEs), grounded in distributional semantics, can capture various ranges of linguistic regularities, which can be further enriched by incorporating structured knowledge resources. This work proposes a novel post-processing approach for injecting semantic relationships into the vector space of both static and contextualized NWEs. Current solutions to retrofitting (RF) word embeddings often oversimplify the integration of semantic knowledge, neglecting the nuanced differences between relationships, which may result in suboptimal performance. Instead of applying multi-thresholding to distance boundaries in metric learning, we compute taxonomic similarity to dynamically adjust these boundaries during the semantic specialization of word embeddings. Benchmark evaluations on both static and contextualized word embeddings demonstrate that our dynamic-fitting (DF) approach produces SOTA correlation results of 0.78 and 0.76 on SimLex-999 and SimVerb-3500, respectively, highlighting the effectiveness of incorporating multiple semantic relationships in refining vector semantics. Our approach also outperforms existing RF methods in both supervised and unsupervised semantic relationships recognition tasks. It achieves top accuracy scores for hypernymy detection on the BLESS, WBLESS, and BIBLESS datasets (0.97, 0.89, and 0.83, respectively) and an F1 score of over 0.60 on four types of semantic relationship classification in the shared Subtask-2 of CogALex-V, surpassing all participant systems. In the analogy reasoning task of the Bigger Analogy Test Set, our approach outperforms existing RF methods on inferring relational similarity. These consistent improvements across various lexical semantics tasks suggest that our DF approach can effectively integrate distributional semantics with symbolic knowledge resources, thereby enhancing the representation capacity of word embeddings in downstream applications.

Part of speech (POS) tagging, though considered to be preliminary to any Natural Language Processing (NLP) task, is crucial to account for, especially in low resource language like Khasi that lacks any form of formal corpus. POS tagging is context sensitive. Therefore, the task is challenging. In this paper, we attempt to investigate a deep learning approach to the POS tagging problem in Khasi. A deep learning model called Robustly Optimized BERT Pretraining Approach (RoBERTa) is pretrained for language modelling task. We then create RoBERTa for POS (RoPOS) tagging, a model that performs POS tagging by fine-tuning the pretrained RoBERTa and leveraging its embeddings for downstream POS tagging. The existing tagset that has been designed, customarily, for the Khasi language is employed for this work, and the corresponding tagged dataset is taken as our base corpus. Further, we propose additional tags to this existing tagset to meet the requirements of the language and have increased the size of the existing Khasi POS corpus. Other machine learning and deep learning models have also been tried and tested for the same task, and a comparative analysis is made on the various models employed. Two different setups have been used for the RoPOS model, and the best testing accuracy achieved is 92 per cent. Comparative analysis of RoPOS with the other models indicates that RoPOS outperforms the others when used for inferencing on texts that are outside the domain of the POS tagged training dataset.

The overall translation quality reached by current machine translation (MT) systems for high-resourced language pairs is remarkably good. Standard methods of evaluation are not suitable nor intended to uncover the many translation errors and quality deficiencies that still persist. Furthermore, the quality of standard reference translations is commonly questioned and comparable quality levels have been reached by MT alone in several language pairs. Navigating further research in these high-resource settings is thus difficult. In this paper, we propose a methodology for creating more reliable document-level human reference translations, called “optimal reference translations,” with the simple aim to raise the bar of what should be deemed “human translation quality.” We evaluate the obtained document-level optimal reference translations in comparison with “standard” ones, confirming a significant quality increase and also documenting the relationship between evaluation and translation editing.

Degrees, unlike entities or events, refer to comparative qualities and are closely tied to gradable adjectives such as “tall.” Degree expressions have been explored in second language (L2) research, covering areas such as learnability, first language (L1) transfer, contrastive analysis, and acquisition difficulty. However, a computational approach to the learning of degree expressions in L2 contexts, particularly for L1 Chinese learners of English, has not been thoroughly investigated. This study aims to fill this gap by utilizing natural language processing (NLP) methods, drawing insights from recent advancements in large language models (LLMs). This study extends Cong (2024)’s general-purpose assessment pipeline to specifically analyze degree expressions, predicting that surprisal metrics will correlate with proficiency levels and distinct developmental stages of L2 learners. Crucially, we address the limitations of surprisal metrics in capturing underuse or avoidance—common in L2 development—by integrating frequency-based analyses. Using an NLP pipeline developed with Stanza, we automatically identified and analyzed degree expressions, constructing linear mixed-effects models to track L2 development trajectories. Our findings reveal that as proficiency increases, learners use complex degree expressions more frequently, supporting theories linking difficulty and learnability. Higher surprisal values are associated with lower proficiency in using degree expressions, and these surprisals are more predictive of degree expressions proficiency than classic NLP measures. These results add further evidence that LLMs and NLP tools provide valuable insights into L2 development, specifically in the domain of degree expressions, expanding upon previous research and offering new approaches for understanding L2 learning processes.

Semantic representation is the task of conveying the meaning of a natural language utterance by converting it to a logical form that can be processed and understood by machines. It is utilised by many applications in natural language processing (NLP), particularly in tasks relevant to natural language understanding (NLU). Due to the widespread use of semantic parsing in NLP, many semantic representation schemes with different forms have been proposed; Universal Conceptual Cognitive Annotation (UCCA) is one of them. UCCA is a cross-lingual semantic annotation framework that allows easy annotation without requiring substantial linguistic knowledge. UCCA-annotated datasets have been released so far for English, French, German, Russian, and Hebrew. In this paper, we present a UCCA-annotated Turkish dataset of 400 sentences that are obtained from the METU-Sabanci Turkish Treebank. We provide the UCCA annotation specifications defined for the Turkish language so that it can be extended further. We followed a semi-automatic annotation approach, where an external semantic parser is utilised for the initial annotation of the dataset, which is manually revised by two annotators. We used the same semantic parser model to evaluate the dataset with zero-shot and few-shot learning, demonstrating that even a small sample set from the target language in the training data has a notable impact on the performance of the parser (15.6% and 2.5% gain over zero-shot for labelled and unlabelled results, respectively).

Sharing ideas through communication with peers is the primary mode of human interaction. Consequently, extensive research has been conducted in the area of conversational AI, leading to an increase in the availability and diversity of conversational tasks, datasets, and methods. However, with numerous tasks being explored simultaneously, the current landscape of conversational AI has become fragmented. Consequently, initiating a well-thought-out model for a dialogue agent can pose significant challenges for a practitioner. Toward highlighting the critical ingredients needed for a practitioner to design a dialogue agent from scratch, the current study provides a comprehensive overview of the primary characteristics of a dialogue agent, the supporting tasks, their corresponding open-domain datasets, and the methods used to benchmark these datasets. We observe that different methods have been used to tackle distinct dialogue tasks. However, building separate models for each task is costly and does not leverage the correlation among the several tasks of a dialogue agent. As a result, recent trends suggest a shift toward building unified foundation models. To this end, we propose Unit, a Unified dialogue dataset constructed from conversations of varying datasets for different dialogue tasks capturing the nuances for each of them. We then train a Unified dialogue foundation model, GPT-2$^{\textrm{U}}$ and present a concise comparative performance of GPT-2$^{\textrm{U}}$ against existing large language models. We also examine the evaluation strategies used to measure the performance of dialogue agents and highlight the scope for future research in the area of conversational AI with a thorough discussion of popular models such as ChatGPT.

With recent advances in natural language generation, risks associated with the rapid proliferation and misuse of generative language models for malicious purposes steadily increase. Artificial text detection (ATD) has emerged to develop resources and computational methods to mitigate these risks, such as generating fake news and scientific article reviews. This paper introduces corpus of artificial texts (CoAT), a large-scale corpus of human-written and generated texts for the Russian language. CoAT spans six domains and comprises outputs from 13 text generation models (TGMs), which differ in the number of parameters, architectural choices, pre-training objectives, and downstream applications. We detail the data collection methodology, conduct a linguistic analysis of the corpus, and present a detailed analysis of the ATD experiments with widely used artificial text detectors. The results demonstrate that the detectors perform well on the seen TGMs, but fail to generalise to unseen TGMs and domains. We also find it challenging to identify the author of the given text, and human annotators significantly underperform the detectors. We release CoAT, the codebase, two ATD leaderboards, and other materials used in the paper.

Research suggests that the texts produced using machine translation (MT) do not fully represent the linguistic traits of the natural language. Yet, the ever-increasing quality and access to MT is resulting in its steady adoption by both language professionals and general users. According to contact linguistic theories, such adoption might result in MT-specific language traits permeating the target languages. This work takes a first step into considering the changes that a language might endure over time by observing the variation of linguistic trends along a series of MT generations. We train ten sequential engines using each to produce the target side of the training corpus of the following and calculate a number of metrics to observe linguistic diversity at a lexical, morphological, and syntactic level for a large, fixed test set. Quantitative results show an initial loss of lexical diversity, which, albeit gradually, only continues at a much slower pace in the following MT generations. In turn, structural variations and, in particular, morphological variations across generations are less marked, which might indicate a more stable behaviour regarding grammatical consistency. Overall, the resulting MT language seems increasingly homogeneous, marked by the reduced presence or disappearance of low-frequency words, and compact, with a decreasing proportion of function words relative to content words.

Language identification (LID) is a crucial preliminary process in the field of Automatic Speech Recognition (ASR) that involves the identification of a spoken language from audio samples. Contemporary systems that can process speech in multiple languages require users to expressly designate one or more languages prior to utilisation. The LID task assumes a significant role in scenarios where ASR systems are unable to comprehend the spoken language in multilingual settings, leading to unsuccessful speech recognition outcomes. The present study introduces convolutional recurrent neural network (CRNN)-based LID, designed to operate on the mel-frequency cepstral coefficient (MFCC) characteristics of audio samples. Furthermore, we replicate certain state-of-the-art methodologies, specifically the convolutional neural network (CNN) and Attention-based convolutional recurrent neural network (CRNN with Attention), and conduct a comparative analysis with our CRNN-based approach. We conducted comprehensive evaluations on thirteen distinct Indian languages, and our model resulted in over 98 per cent classification accuracy. The LID model exhibits high-performance levels ranging from 97 per cent to 100 per cent for languages that are linguistically similar. The proposed LID model exhibits a high degree of extensibility to additional languages and demonstrates a strong resistance to noise, achieving 91.2 per cent accuracy in a noisy setting when applied to a European Language (EU) dataset.

A vast amount of clinical data are still stored in unstructured text. Automatic extraction of medical information from these data poses several challenges: high costs of clinical expertise, restricted computational resources, strict privacy regulations, and limited interpretability of model predictions. Recent domain adaptation and prompting methods using lightweight masked language models showed promising results with minimal training data and allow for application of well-established interpretability methods. We are first to present a systematic evaluation of advanced domain-adaptation and prompting methods in a lower-resource medical domain task, performing multi-class section classification on German doctor’s letters. We evaluate a variety of models, model sizes (further-pre)training and task settings, and conduct extensive class-wise evaluations supported by Shapley values to validate the quality of small-scale training data and to ensure interpretability of model predictions. We show that in few-shot learning scenarios, a lightweight, domain-adapted pretrained language model, prompted with just 20 shots per section class, outperforms a traditional classification model, by increasing accuracy from $48.6\%$ to $79.1\%$. By using Shapley values for model selection and training data optimization, we could further increase accuracy up to $84.3\%$. Our analyses reveal that pretraining of masked language models on general-language data is important to support successful domain-transfer to medical language, so that further-pretraining of general-language models on domain-specific documents can outperform models pretrained on domain-specific data only. Our evaluations show that applying prompting based on general-language pretrained masked language models combined with further-pretraining on medical-domain data achieves significant improvements in accuracy beyond traditional models with minimal training data. Further performance improvements and interpretability of results can be achieved, using interpretability methods such as Shapley values. Our findings highlight the feasibility of deploying powerful machine learning methods in clinical settings and can serve as a process-oriented guideline for lower-resource languages and domains such as clinical information extraction projects.

Linguistic synesthesia as a productive figurative language usage has received little attention in the field of Natural Language Processing (NLP). Although linguistic synesthesia is similar to metaphor concerning involving conceptual mappings and showing great usefulness in the NLP tasks such as sentiment analysis and stance detection, the well-studied methods of metaphor detection cannot be applied to the detection of linguistic synesthesia directly. This study incorporates comprehensive linguistic features (i.e., character and radical information, word segmentation information, and part-of-speech tagging) into a neural model to detect linguistic synesthetic usages in a sentence automatically. In particular, we employ a span-based boundary detection model to extract sensory words. In addition, a joint model is proposed to detect the original and synesthetic modalities of the sensory words collectively. Based on the experiments, our model is shown to achieve state-of-the-art results on the dataset for linguistic synesthesia detection. The results prove that leveraging culturally enriched linguistic features and joint learning are effective in linguistic synesthesia detection. Furthermore, as the proposed model leverages non-language-specific linguistic features, the model would be applied to the detection of linguistic synesthesia in other languages.

Everyone is talking about bots. Much of the discussion has focused on downsides. It is too easy to use bots to cheat, but there are also many ways to use bots to improve your writing. Good writers use thesauruses. It is not cheating to use bots as a modern version of a thesaurus. It is also not cheating to use recommendation systems in a responsible way.

Navigating the complexities of language diversity is a central challenge in developing robust natural language processing systems, especially in specialized domains like banking. The Moroccan Dialect of Arabic (Darija) serves as a common language that blends cultural complexities, historical impacts, and regional differences, which presents unique challenges for language models due to its divergence from Modern Standard Arabic and influence from French, Spanish, and Tamazight. To tackle these challenges, this paper introduces Darija Banking, a novel Darija dataset aimed at enhancing intent classification in the banking domain. DarijaBanking comprises over 1800 parallel high-quality queries in Darija, Modern Standard Arabic (MSA), English, and French, organized into 24 intent classes. We experimented various intent classification methods, including full fine-tuning of monolingual and multilingual models, zero-shot learning, retrieval-based approaches, and Large Language Model prompting. Furthermore, we propose BERTouch, a BERT-based language model fine-tuned on intent detection in Darija, which outperforms state-of-the-art models, including OpenAI’s GPT-4, achieving F1 scores of 0,98 and 0,96 on both Darija and MSA, respectively. The results provide insights into enhancing Moroccan Darija banking intent detection systems, highlighting the value of domain-specific data annotation and balancing precision and cost-effectiveness.

Syntactic parsing is one of the areas in Natural Language Processing. The development of large-scale multilingual language models has enabled cross-lingual parsing approaches, which allows us to develop parsers for languages that do not have treebanks available. However, these approaches rely on the assumption that languages share orthographic representations and lexical entries. In this article, we investigate methods for developing a dependency parser for Xibe, a low-resource language that is written in a unique script. We first investigate lexicalized monolingual dependency parsing experiments to examine the effectiveness of word, part-of-speech, and character embeddings as well as pre-trained language models. Results show that character embeddings can significantly improve performance, while pre-trained language models decrease performance since they do not recognize the Xibe script. We also train delexicalized monolingual models, which yield competitive results to the best lexicalized model. Since the monolingual models are trained on a very small training set, we also investigate lexicalized and delexicalized cross-lingual models. We use six closely related languages as source language, which cover a wide range of scripts. In this setting, the delexicalized models achieve higher performance than lexicalized models. A final experiment shows that we can increase performance of the cross-lingual model by combining source languages and selecting the most similar sentences to Xibe as training set. However, all cross-lingual parsing results are still considerably lower than the monolingual model. We attribute the low performance of cross-lingual methods to syntactic and annotation differences as well as to the impoverished input of Universal Dependency Part-of-Speech tags that the delexicalized model has access to.