ymoslem/acl-abstracts huggingface dataset:
RoMantra: Optimizing Neural Machine Translation for Low-Resource Languages through Romanization (2024)

Neural Machine Translation (NMT) for low-resource language pairs with distinct scripts, such as Hindi-Chinese and Japanese-Hindi, poses significant challenges due to scriptural and linguistic differences. This paper investigates the efficacy of romanization as a preprocessing step to bridge these gaps. We compare baseline models trained on native scripts with models incorporating romanization in three configurations: both-side, source-side only, and target-side only. Additionally, we introduce a script restoration model that converts romanized output back to native scripts, ensuring accurate evaluation. Our experiments show that romanization, particularly when applied to both sides, improves translation quality across the studied language pairs. The script restoration model further enhances the practicality of this approach by enabling evaluation in native scripts with some performance loss. This work provides insights into leveraging romanization for NMT in low-resource, cross-script settings, presenting a promising direction for under-researched language combinations.
Domain Dynamics: Evaluating Large Language Models in English-Hindi Translation (2024)

Large Language Models (LLMs) have demonstrated impressive capabilities in machine translation, leveraging extensive pre-training on vast amounts of data. However, this generalist training often overlooks domain-specific nuances, leading to potential difficulties when translating specialized texts. In this study, we present a multi-domain test suite, collated from previously published datasets, designed to challenge and evaluate the translation abilities of LLMs. The test suite encompasses diverse domains such as judicial, education, literature (specifically religious texts), and noisy user-generated content from online product reviews and forums like Reddit. Each domain consists of approximately 250-300 sentences, carefully curated and randomized in the final compilation. This English-to-Hindi dataset aims to evaluate and expose the limitations of LLM-based translation systems, offering valuable insights into areas requiring further research and development. We have submitted the dataset to WMT24 Break the LLM
Synthetic Data and Model Dynamics based Performance Analysis for Assamese-Bodo Low Resource NMT (2024)

This paper presents details of modelling and performance analysis of Neural Machine Translation (NMT) for the low-resource Assamese-Bodo language pair, focusing on model tuning and the use of synthetic data. Given the scarcity of parallel corpora for these languages, synthetic data generation techniques, such as back-translation, were employed to enhance translation performance. The NMT architecture was used along with necessary preprocessing steps as per the NMT pipeline. Experimentation across varying model parameters have been performed and scores are recorded. The model`s performance was evaluated using the BLEU score, which showed significant improvement when synthetic data was incorporated into the training process. While a base model with gold standard data of relatively smaller size yielded Overall BLEU of 11.35, optimized tuned model with synthetic data has resulted considerable improvement in BLEU scores across the domains, with overall BLEU upto 14.74. Challenges related to data scarcity and model optimization are also discussed, along with potential future improvements.
Detecting AI-Generated Text with Pre-Trained Models Using Linguistic Features (2024)

The advent of sophisticated large language models, such as ChatGPT and other AI-driven platforms, has led to the generation of text that closely mimics human writing, making it increasingly challenging to discern whether it is human-generated or AI-generated content. This poses significant challenges to content verification, academic integrity, and detecting misleading information. To address these issues, we developed a classification system to differentiate between human-written and AI-generated texts using a diverse HC3-English dataset. This dataset leveraged linguistic analysis and structural features, including part-of-speech tags, vocabulary size, word density, active and passive voice usage, and readability metrics such as Flesch Reading Ease, perplexity, and burstiness. We employed transformer-based and deep-learning models for the classification task, such as CNN{\_}BiLSTM, RNN, BERT, GPT-2, and RoBERTa. Among these, the RoBERTa model demonstrated superior performance, achieving an impressive accuracy of 99.73. These outcomes demonstrate how cutting-edge deep learning methods can maintain information integrity in the digital realm.
Towards Understanding the Robustness of LLM-based Evaluations under Perturbations (2024)

Traditional evaluation metrics like BLEU and ROUGE fall short when capturing the nuanced qualities of generated text, particularly when there is no single ground truth. In this paper, we explore the potential of Large Language Models (LLMs), specifically Google Gemini 1, to serve as automatic evaluators for non-standardized metrics in summarization and dialog-based tasks. We conduct experiments across multiple prompting strategies to examine how LLMs fare as quality annotators when compared with human judgments on the SummEval and USR datasets, asking the model to generate both a score as well as a justification for the score. Furthermore, we explore the robustness of the LLM evaluator by using perturbed inputs. Our findings suggest that while LLMs show promise, their alignment with human evaluators is limited, they are not robust against perturbations and significant improvements are required for their standalone use as reliable evaluators for subjective metrics.
Exploring Kolmogorov Arnold Networks for Interpretable Mental Health Detection and Classification from Social Media Text (2024)

Mental health analysis from social media text demands both high accuracy and interpretability for responsible healthcare applications. This paper explores Kolmogorov Arnold Networks (KANs) for mental health detection and classification, demonstrating their superior performance compared to Multi-Layer Perceptrons (MLPs) in accuracy while requiring fewer parameters. To further enhance interpretability, we leverage the Local Interpretable Model Agnostic Explanations (LIME) method to identify key features, resulting in a simplified KAN model. This allows us to derive governing equations for each class, providing a deeper understanding of the relationships between texts and mental health conditions.
Human vs Machine: An Automated Machine-Generated Text Detection Approach (2024)

With the advancement of natural language processing (NLP) and sophisticated Large Language Models (LLMs), distinguishing between human-written texts and machine-generated texts is quite difficult nowadays. This paper presents a systematic approach to classifying machine-generated text from human-written text with a combination of the transformer-based model and textual feature-based post-processing technique. We extracted five textual features: readability score, stop word score, spelling and grammatical error count, unique word score and human phrase count from both human-written and machine-generated texts separately and trained three machine learning models (SVM, Random Forest and XGBoost) with these scores. Along with exploring traditional machine-learning models, we explored the BiLSTM and transformer-based distilBERT models to enhance the classification performance. By training and evaluating with a large dataset containing both human-written and machine-generated text, our best-performing framework achieves an accuracy of 87.5{\%}.
Survey on Computational Approaches to Implicature (2024)

This paper explores the concept of solving implicature in Natural Language Processing (NLP), highlighting its significance in understanding indirect communication. Drawing on foundational theories by Austin, Searle, and Grice, we discuss how implicature extends beyond literal language to convey nuanced meanings. We review existing datasets, including the Pragmatic Understanding Benchmark (PUB), that assess models' capabilities in recognizing and interpreting implicatures. Despite recent advances in large language models (LLMs), challenges remain in effectively processing implicature due to limitations in training data and the complexities of contextual interpretation. We propose future directions for research, including the enhancement of datasets and the integration of pragmatic reasoning tasks, to improve LLMs' understanding of implicature and facilitate better human-computer interaction.
Exploring User Dissatisfaction: Taxonomy of Implicit Negative Feedback in Virtual Assistants (2024)

The success of virtual assistants relies on continuous performance monitoring to ensure their competitive edge in the market. This entails assessing their ability to understand user intents and execute tasks effectively. While user feedback is pivotal for measuring satisfaction levels, relying solely on explicit feedback proves impractical. Thus, extracting implicit user feedback from conversations of user and virtual assistant is a more efficient approach. Additionally, along with learning whether a task is performed correctly or not, it is extremely important to understand the reasons behind any incorrect execution. In this paper, we introduce a framework designed to identify dissatisfactory conversations, systematically analyze these conversations, and generate comprehensive reports detailing the reasons for user dissatisfaction. By implementing a feedback classifier, we identify conversations that indicate user dissatisfaction, which serves as a sign of implicit negative feedback. To analyze negative feedback conversations more deeply, we develop a lightweight pipeline called an issue categorizer ensemble with multiple models to understand the reasons behind such dissatisfactory conversations. We subsequently augment the identified discontented instances to generate additional data and train our models to prevent such failures in the future. Our implementation of this simple framework, called AsTrix (Assisted Triage and Fix), led to significant enhancements in the performance of our smartphone-based In-House virtual assistant, with successful task completion rates increasing from 83.1{\%} to 92.6{\%} between June 2022 and March 2024. Moreover, by automating the deeper analysis process targeting just five major issue types contributing to the dissatisfaction, we significantly address approximately 62{\%} of the negative feedback conversation data.
Pronominal Anaphora Resolution in Konkani language incorporating Gender Agreement (2024)

Konkani is a low-resource language, spoken mainly on the central west coast of India. Approximately 2.3 million people speak Konkani (Office of the Registrar General Census Commissioner, India,2011). It is also the official language of the state of Goa. It belongs to the Southern Indo-Aryan language group. The official Script for writing the Konkani language is Devanagari. Despite this, being a low-resource language has hampered its development on the digital platform, Konkani has yet to significantly impact its digital presence. To improve this situation, contribution to Natural Language Understanding in the Konkani language is important. This paper aims to resolve pronominal anaphora in the Konkani language using a rule-based method incorporating gender agreement. This is required in NLP applications like text summarization, machine translation, and question-answering systems. While research on English and other foreign languages, as well as Indian languages like Tamil, Kannada, Malayalam, Bengali, and Marathi, have been done, no work has been done on the Konkani language thus far. This is the very first attempt made to resolve anaphora in Konkani.
Story-Yarn : An Interactive Story Building Application (2024)

Story building is an important part of language and overall development of a child. Developing an interactive and artificial intelligence (AI) based solution to create stories for children is an open and challenging problem. Methods combining large language models (LLMs) and knowledge graphs (KGs) have further enabled high quality and coherent story generation. In this work, we present a platform, Story Yarn, developed for interactive story creation for children. We customise a KG, using children stories, which captures relationships between components of stories. This customised KG is then used along with LLM to collaboratively create a story. We have also built a simple app to facilitate user interaction. This platform can aid the creative development of children, and can be used at home or in schools.
End to End Multilingual Coreference Resolution for Indian Languages (2024)

This paper describes an approach on an end to end model for Multilingual Coreference Resolution (CR) for low resource languages such as Tamil, Malayalam and Hindi. We have done fine tune the XLM-Roberta large model on multilingual training dataset using specific languages with linguistic features and without linguistic features. XLM-R with linguistic features achieves better results than the baseline system. This shows that giving the linguistic knowledge enriches the system performance. The performance of the system is comparable with the state of the art systems.
LangBot-Language Learning Chatbot (2024)

Chatbots are being widely used in educational domain to revolutionize how students interact and learn along with traditional methods of learning. This paper presents our work on LangBot, a chatbot developed for learning Tamil language. LangBot developed integrates the interactive features of chatbots with the study material of the Tamil courses offered by Tamil Virtual Academy, Government of Tamil Nadu. LangBot helps students in enhancing their learning skills and increases their interest in learning the language. Using semi-automatic methods, we generate question and answers related to all topics in the courses. We then develop a generative language model and also Retrieval Augmented Generation (RAG) so that the system can incorporate new syllabus changes. We have performed manual user studies. The results obtained are encouraging. This approach offers learners an interactive tool that aligns with their syllabus. It is observed that this enriches the overall learning experience.
Towards Efficient Audio-Text Keyword Spotting: Quantization and Multi-Scale Linear Attention with Foundation Models (2024)

Open Vocabulary Keyword Spotting is essential in numerous applications, from virtual assistants to security systems, as it allows systems to identify specific words or phrases in continuous speech. In this paper, we propose a novel end-to-end method for detecting user-defined open vocabulary keywords by leveraging linguistic patterns for the correlation between audio and text modalities. Our approach utilizes quantized pre-trained foundation models for robust audio embeddings and a unique lightweight Multi-Scale Linear Attention (MSLA) network that aligns speech and text representations for effective cross-modal agreement. We evaluate our method on two distinct datasets, comparing its performance against other baselines. The results highlight the effectiveness of our approach, achieving significant improvements over the Cross-Modality Correspondence Detector (CMCD) method, with a 16.08{\%} increase in AUC and a 17.2{\%} reduction in EER metrics on the Google Speech Commands dataset. These findings demonstrate the potential of our method to advance keyword spotting across various real-world applications.
Utilizing POS-Driven Pitch Contour Analysis for Enhanced Tamil Text-to-Speech Synthesis (2024)

A novel approach to text-to-speech synthesis that integrates pitch contour labels derived from the highest occurrence analysis for each Part-of-Speech (POS) tag. Using the Stanford POS Tagger, grammatical tags are assigned to words, and the most frequently occurring pitch contour labels associated with these tags are analyzed, focusing on both unigram and bigram statistics. The primary goal is to identify the pitch contour for each POS tag based on its frequency of occurrence. These pitch contour labels are incorporated into the output of the synthesized waveform using the TD-PSOLA (Time Domain Pitch Synchronous Overlap and Add) signal processing algorithm. The resulting waveform is evaluated using Mean Opinion Scores (MOS), demonstrating significant enhancements in quality and producing a prosodically rich synthetic speech.
RoundTripOCR: A Data Generation Technique for Enhancing Post-OCR Error Correction in Low-Resource Devanagari Languages (2024)

Optical Character Recognition (OCR) technology has revolutionized the digitization of printed text, enabling efficient data extraction and analysis across various domains. Just like Machine Translation systems, OCR systems are prone to errors. In this work, we address the challenge of data generation and post-OCR error correction, specifically for low-resource languages. We propose an approach for synthetic data generation for Devanagari languages, RoundTripOCR, that tackles the scarcity of the post-OCR Error Correction datasets for low-resource languages. We release post-OCR text correction datasets for Hindi, Marathi, Bodo, Nepali, Konkani and Sanskrit. We also present a novel approach for OCR error correction by leveraging techniques from machine translation. Our method involves translating erroneous OCR output into a corrected form by treating the OCR errors as mistranslations in a parallel text corpus, employing pre-trained transformer models to learn the mapping from erroneous to correct text pairs, effectively correcting OCR errors.
MULTILATE: A Synthetic Dataset on AI-Generated MULTImodaL hATE Speech (2024)

One of the pressing challenges society faces today is the rapid proliferation of online hate speech, exacerbated by the rise of AI-generated multimodal hate content. This new form of synthetically produced hate speech presents unprecedented challenges in detection and moderation. In response to the growing presence of such harmful content across social media platforms, this research introduces a groundbreaking solution:
We Care: Multimodal Depression Detection and Knowledge Infused Mental Health Therapeutic Response Generation (2024)

The detection of depression through non-verbal cues has gained significant attention. Previous research predominantly centred on identifying depression within the confines of controlled laboratory environments, often with the supervision of psychologists or counsellors. Unfortunately, datasets generated in such controlled settings may struggle to account for individual behaviours in real-life situations. In response to this limitation, we present the Extended D-vlog dataset, encompassing a collection of 1,261 YouTube vlogs. Additionally, the emergence of large language models (LLMs) like GPT3.5, and GPT4 has sparked interest in their potential that LLMs can act like mental health professionals. Yet, the readiness of these LLM models to be used in real-life settings is still a concern as they can give wrong responses that can harm the users. We introduce a virtual agent serving as an initial contact for mental health patients, offering Cognitive Behavioral Therapy (CBT)-based responses. It comprises two core functions: 1. Identifying depression in individuals, and 2. Delivering CBT-based therapeutic responses. Our Mistral model achieved impressive scores of 70.1{\%} and 30.9{\%} for distortion assessment and classification, along with a Bert score of 88.7{\%}. Moreover, utilizing the TVLT model on our Multimodal Extended D-vlog Dataset yielded outstanding results, with an impressive F1-score of 67.8{\%}
CM\_CLIP: Unveiling Code-Mixed Multimodal Learning with Cross-Lingual CLIP Adaptations (2024)

In this paper, we present CMCLIP, a Code-Mixed Contrastive Linked Image Pre-trained model, an innovative extension of the widely recognized CLIP model. Our work adapts the CLIP framework to the code-mixed environment through a novel cross-lingual teacher training methodology. Building on the strengths of CLIP, we introduce the first code-mixed pre-trained text-and-vision model, CMCLIP, specifically designed for Hindi-English code-mixed multimodal language settings. The model is developed in two variants: CMCLIP-RB, based on ResNet, and CMCLIP-VX, based on ViT, both of which adapt the original CLIP model to suit code-mixed data. We also introduce a large, novel dataset called Parallel Hybrid Multimodal Code-mixed Hinglish (PHMCH), which forms the foundation for teacher training. The CMCLIP models are evaluated on various downstream tasks, including code-mixed Image-Text Retrieval (ITR) and classification tasks, such as humor and sarcasm detection, using a code-mixed meme dataset. Our experimental results demonstrate that CMCLIP outperforms existing models, such as M3P and multilingual-CLIP, establishing state-of-the-art performance for code-mixed multimodal tasks. We would also like to assert that although our data and frameworks are on Hindi-English code-mix, they can be extended to any other code-mixed language settings.
Aiding Non-Verbal Communication: A Bidirectional Language Agnostic Framework for Automating Text to AAC Generation (2024)

Persons with severe speech and motor impairments (SSMI), like those with cerebral palsy (CP) experience significant challenges via communication in conventional methods. Many a times they rely on Graphical symbol-based Augmentative and Alternative Communication (AAC) systems to facilitate the communication. Our work aims to support AAC communication by developing specialized datasets for direct translation of Graphical Symbols to Natural Language text. The dataset is enhanced with an automated Text-to-Pictogram generation module. The dataset is enriched with some additive information like tense-based information and subjective information (questionnaires, exclamations). Additionally, we expanded our efforts to include translation into Indian language Bengali, for those individuals with SSMI who are more comfortable communicating in their native language. We aim to develop an end-to-end language agnostic framework for efficient bidirectional communication between non-verbal AAC picture symbols and textual data.
Towards Enhancing Knowledge Accessibility for Low-Resource Indian Languages: A Template Based Approach (2024)

In today`s digital age, access to knowledge and information is crucial for societal growth. Although widespread resources like Wikipedia exist, there is still a linguistic barrier to breakdown for low-resource languages. In India, millions of individuals still lack access to reliable information from Wikipedia because they are only proficient in their regional language. To address this gap, our work focuses on enhancing the content and digital footprint of multiple Indian languages. The primary objective of our work is to improve knowledge accessibility by generating a substantial volume of high-quality Wikipedia articles in Telugu, a widely spoken language in India with around 95.7 million native speakers. Our work aims to create Wikipedia articles and also ensures that each article meets necessary quality standards such as a minimum word count, inclusion of images for reference, and an infobox. Our work also adheres to the five core principles of Wikipedia. We streamline our article generation process, leveraging NLP techniques such as translation, transliteration, and template generation and incorporating human intervention when necessary. Our contribution is a collection of 8,929 articles in the movie domain, now ready to be published on Telugu Wikipedia.
Value to User`s Voice: A Generative AI Framework for Actionable Insights from Customer Reviews in Consumer Electronics (2024)

Customer reviews are a valuable asset for businesses, especially in the competitive consumer electronics sector, where understanding user preferences and product performance is critical. However, extracting meaningful insights from these unstructured and often noisy reviews is a challenging task that typically requires significant manual effort. We present