ymoslem/acl-abstracts huggingface dataset:
Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation (2024)

Despite the recent popularity of Large Language Models (LLMs) in Machine Translation (MT), their performance in low-resource languages (LRLs) still lags significantly behind Neural Machine Translation (NMT) models. In this work, we explore what it would take to adapt LLMs for the low-resource setting. Particularly, we re-examine the role of two factors: a) the importance and application of parallel data, and b) diversity in Supervised Fine-Tuning (SFT). Recently, parallel data has seen reduced use in adapting LLMs for MT, while data diversity has been embraced to promote transfer across languages and tasks. However, for low-resource LLM-MT, we show that the opposite is true for both considerations: a) \textit{parallel data} is critical during both pre-training and SFT; b) diversity tends to cause \textit{interference} instead of transfer. Our experiments with three LLMs across two low-resourced language groups{---}Indigenous American and North-East Indian{---}reveal consistent trends, underscoring the generalizability of our findings. We believe these insights will be valuable for scaling to massively multilingual LLM-MT models that can effectively serve LRLs.
Efficient Technical Term Translation: A Knowledge Distillation Approach for Parenthetical Terminology Translation (2024)

This paper addresses the challenge of accurately translating technical terms, which are crucial for clear communication in specialized fields. We introduce the Parenthetical Terminology Translation (PTT) task, designed to mitigate potential inaccuracies by displaying the original term in parentheses alongside its translation. To implement this approach, we generated a representative PTT dataset using a collaborative approach with large language models and applied knowledge distillation to fine-tune traditional Neural Machine Translation (NMT) models and small-sized Large Language Models (sLMs). Additionally, we developed a novel evaluation metric to assess both overall translation accuracy and the correct parenthetical presentation of terms. Our findings indicate that sLMs did not consistently outperform NMT models, with fine-tuning proving more effective than few-shot prompting, particularly in models with continued pre-training in the target language. These insights contribute to the advancement of more reliable terminology translation methodologies.
Assessing the Role of Imagery in Multimodal Machine Translation (2024)

In Multimodal Machine Translation (MMT), the use of visual data has shown only marginal improvements compared to text-only models. Previously, the CoMMuTE dataset and associated metric were proposed to score models on tasks where the imagery is necessary to disambiguate between two possible translations for each ambiguous source sentence. In this work, we introduce new metrics within the CoMMuTE domain to provide deeper insights into image-aware translation models. Our proposed metrics differ from the previous CoMMuTE scoring method by 1) assessing the impact of multiple images on individual translations and 2) evaluating a model`s ability to jointly select each translation for each image context. Our results challenge the conventional views of poor visual comprehension capabilities of MMT models and show that models can indeed meaningfully interpret visual information, though they may not leverage it sufficiently in the final decision.
Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation (2024)

High-quality Machine Translation (MT) evaluation relies heavily on human judgments.Comprehensive error classification methods, such as Multidimensional Quality Metrics (MQM), are expensive as they are time-consuming and can only be done by experts, whose availability may be limited especially for low-resource languages.On the other hand, just assigning overall scores, like Direct Assessment (DA), is simpler and faster and can be done by translators of any level, but is less reliable.In this paper, we introduce Error Span Annotation (ESA), a human evaluation protocol which combines the continuous rating of DA with the high-level error severity span marking of MQM.We validate ESA by comparing it to MQM and DA for 12 MT systems and one human reference translation (English to German) from WMT23. The results show that ESA offers faster and cheaper annotations than MQM at the same quality level, without the requirement of expensive MQM experts.
Neural Methods for Aligning Large-Scale Parallel Corpora from the Web for South and East Asian Languages (2024)

We introduce neural methods and a toxicity filtering step to the hierarchical web mining approach of Paracrawl (Ba{\~n}{\'o}n et al., 2020), showing large improvements. We apply these methods to web-scale parallel corpus mining for 9 South and East Asian national languages, creating training resources for machine translation that yield better translation quality for most of these languages than existing publicly available datasets in OPUS. Our methods also generally lead to better results than the global mining approach of Schwenk et al. (2021).
Plug, Play, and Fuse: Zero-Shot Joint Decoding via Word-Level Re-ranking across Diverse Vocabularies (2024)

Recent advancements in NLP have resulted in models with specialized strengths, such as processing multimodal inputs or excelling in specific domains. However, real-world tasks, like multimodal translation, often require a combination of these strengths, such as handling both translation and image processing. While individual translation and vision models are powerful, they typically lack the ability to perform both tasks in a single system. Combining these models poses challenges, particularly due to differences in their vocabularies, which limit the effectiveness of traditional ensemble methods to post-generation techniques like N-best list re-ranking. In this work, we propose a novel zero-shot ensembling strategy that allows for the integration of different models during the decoding phase without the need for additional training. Our approach re-ranks beams during decoding by combining scores at the word level, using heuristics to predict when a word is completed. We demonstrate the effectiveness of this method in machine translation scenarios, showing that it enables the generation of translations that are both speech- and image-aware while also improving overall translation quality.
Towards Disfluency Annotated Corpora for Indian Languages (2024)

In the natural course of spoken language, individuals often engage in thinking and self-correction during speech production. These instances of interruption or correction are commonly referred to as disfluencies. When preparing data for subsequent downstream NLP tasks, these linguistic elements can be systematically removed, or handled as required, to enhance data quality. In this study, we present a comprehensive research on disfluencies in Indian languages. Our approach involves not only annotating real-world conversation transcripts but also conducting a detailed analysis of linguistic nuances inherent to Indian languages that are necessary to consider during annotation. Additionally, we introduce a robust algorithm for the synthetic generation of disfluent data. This algorithm aims to facilitate more effective model training for the identification of disfluencies in real-world conversations, thereby contributing to the advancement of disfluency research in Indian languages.
EmoMix-3L: A Code-Mixed Dataset for Bangla-English-Hindi for Emotion Detection (2024)

Code-mixing is a well-studied linguistic phenomenon that occurs when two or more languages are mixed in text or speech. Several studies have been conducted on building datasets and performing downstream NLP tasks on code-mixed data. Although it is not uncommon to observe code-mixing of three or more languages, most available datasets in this domain contain code-mixed data from only two languages. In this paper, we introduce EmoMix-3L, a novel multi-label emotion detection dataset containing code-mixed data from three different languages. We experiment with several models on EmoMix-3L and we report that MuRIL outperforms other models on this dataset.
Findings of the WILDRE Shared Task on Code-mixed Less-resourced Sentiment Analysis for Indo-Aryan Languages (2024)

This paper describes the structure and findings of the WILDRE 2024 shared task on Code-mixed Less-resourced Sentiment Analysis for Indo-Aryan Languages. The participants were asked to submit the test data`s final prediction on CodaLab. A total of fourteen teams registered for the shared task. Only four participants submitted the system for evaluation on CodaLab, with only two teams submitting the system description paper. While all systems show a rather promising performance, they outperform the baseline scores.
Multilingual Bias Detection and Mitigation for Indian Languages (2024)

Lack of diverse perspectives causes neutrality bias in Wikipedia content leading to millions of worldwide readers getting exposed by potentially inaccurate information. Hence, neutrality bias detection and mitigation is a critical problem. Although previous studies have proposed effective solutions for English, no work exists for Indian languages. First, we contribute two large datasets, mWIKIBIAS and mWNC, covering 8 languages, for the bias detection and mitigation tasks respectively. Next, we investigate the effectiveness of popular multilingual Transformer-based models for the two tasks by modeling detection as a binary classification problem and mitigation as a style transfer problem. We make the code and data publicly available.
Dharma\'s\=astra Informatics: Concept Mining System for Socio-Cultural Facet in Ancient India (2024)

The heritage of Dharma{\'s}{\={a}}stra (DS) represents an extensive cultural legacy, spanning diverse fields such as family law, social ethics, culture and economics. In this paper, a new term {\textquotedblleft}Dharma{\'s}{\={a}}stric Informatics,{\textquotedblright} is proposed which leverages computational methods for concept mining to unravel the socio-cultural complexities of ancient India as reflected in the DS. Despite its profound significance, the digitization and online information retrieval of DS texts encounter notable challenges. Therefore, the primary aim of this paper is to synergize digital accessibility and information mining techniques to enhance access to DS knowledge traditions. Through the utilization of heritage computing methodologies, it is an endeavour to develop a robust system for digitizing DS texts comprehensively, facilitating instant referencing and efficient retrieval, catering to the needs of researchers and scholars across disciplines worldwide. By leveraging advanced digital technologies and the burgeoning IT landscape, it seeks to create a seamless and user-friendly platform for accessing and exploring DS texts. This experiment not only promotes scholarly engagement but also serves as an invaluable resource for individuals interested in delving into the intricate realms of archaic Indian knowledge traditions. Ultimately, our efforts aim to amplify the visibility and accessibility of DS knowledge, fostering a deeper understanding and appreciation of this profound cultural heritage.
Exploring News Summarization and Enrichment in a Highly Resource-Scarce Indian Language: A Case Study of Mizo (2024)

Obtaining sufficient information in one`s mother tongue is crucial for satisfying the information needs of the users. While high-resource languages have abundant online resources, the situation is less than ideal for very low-resource languages. Moreover, the insufficient reporting of vital national and international events continues to be a worry, especially in languages with scarce resources, like Mizo. In this paper, we conduct a study to investigate the effectiveness of a simple methodology designed to generate a holistic summary for Mizo news articles, which leverages English-language news to supplement and enhance the information related to the corresponding news events. Furthermore, we make available 500 Mizo news articles and corresponding enriched holistic summaries. Human evaluation confirms that our approach significantly enhances the information coverage of Mizo news articles.
Finding the Causality of an Event in News Articles (2024)

This paper discusses about the finding of causality of an event in newspaper articles. The analysis of causality , otherwise known as cause and effect is crucial for building efficient Natural Language Understanding (NLU) supported AI systems such as Event tracking and it is considered as a complex semantic relation under discourse theory. A cause-effect relation consists of a linguistic marker and its two arguments. The arguments are semantic arguments where the cause is the first argument (Arg1) and the effect is the second argument(Arg2). In this work we have considered the causal relations in Tamil Newspaper articles. The analysis of causal constructions, the causal markers and their syntactic relation lead to the identification of different features for developing the language model using RBMs (Restricted Boltzmann Machine). The experiments we performed have given encouraging results. The Cause-Effect system developed is used in a mobile App for Event profiling called {\textquotedblleft}Nigalazhvi{\textquotedblright} where the cause and effect of an event is identified and given to the user.
Creating Corpus of Low Resource Indian Languages for Natural Language Processing: Challenges and Opportunities (2024)

Addressing tasks in Natural Language Processing requires access to sufficient and high-quality data. However, working with languages that have limited resources poses a significant challenge due to the absence of established methodologies, frameworks, and collaborative efforts. This paper intends to briefly outline the challenges associated with standardization in data creation, focusing on Indian languages, which are often categorized as low resource languages. Additionally, potential solutions and the importance of standardized procedures for low-resource language data are proposed. Furthermore, the critical role of standardized protocols in corpus creation and their impact on research is highlighted. Lastly, this paper concludes by defining what constitutes a corpus.
FZZG at WILDRE-7: Fine-tuning Pre-trained Models for Code-mixed, Less-resourced Sentiment Analysis (2024)

This paper describes our system used for a shared task on code-mixed, less-resourced sentiment analysis for Indo-Aryan languages. We are using the large language models (LLMs) since they have demonstrated excellent performance on classification tasks. In our participation in all tracks, we use \textit{unsloth/mistral-7b-bnb-4bit} LLM for the task of code-mixed sentiment analysis. For track 1, we used a simple fine-tuning strategy on PLMs by combining data from multiple phases. Our trained systems secured first place in four phases out of five. In addition, we present the results achieved using several PLMs for each language.
MLInitiative@WILDRE7: Hybrid Approaches with Large Language Models for Enhanced Sentiment Analysis in Code-Switched and Code-Mixed Texts (2024)

Code-switched and code-mixed languages are prevalent in multilingual societies, reflecting the complex interplay of cultures and languages in daily communication. Understanding the sentiment embedded in such texts is crucial for a range of applications, from improving social media analytics to enhancing customer feedback systems. Despite their significance, research in code-mixed and code-switched languages remains limited, particularly in less-resourced languages. This scarcity of research creates a gap in natural language processing (NLP) technologies, hindering their ability to accurately interpret the rich linguistic diversity of global communications. To bridge this gap, this paper presents a novel methodology for sentiment analysis in code-mixed and code-switched texts. Our approach combines the power of large language models (LLMs) and the versatility of the multilingual BERT (mBERT) framework to effectively process and analyze sentiments in multilingual data. By decomposing code-mixed texts into their constituent languages, employing mBERT for named entity recognition (NER) and sentiment label prediction, and integrating these insights into a decision-making LLM, we provide a comprehensive framework for understanding sentiment in complex linguistic contexts. Our system achieves competitive rank on all subtasks in the Code-mixed Less-Resourced Sentiment analysis (Code-mixed) shared task at WILDRE-7 (LREC-COLING).
Aalamaram: A Large-Scale Linguistically Annotated Treebank for the Tamil Language (2024)

Tamil is a relatively low-resource language in the field of Natural Language Processing (NLP). Recent years have seen a growth in Tamil NLP datasets in Natural Language Understanding (NLU) or Natural Language Generation (NLG) tasks, but high-quality linguistic resources remain scarce. In order to alleviate this gap in resources, this paper introduces Aalamaram, a treebank with rich linguistic annotations for the Tamil language. It is hitherto the largest publicly available Tamil treebank with almost 10,000 sentences from diverse sources and is annotated for the tasks of Part-of-speech (POS) tagging, Named Entity Recognition (NER), Morphological Parsing and Dependency Parsing. Close attention has also been paid to multi-word segmentation, especially in the context of Tamil clitics. Although the treebank is based largely on the Universal Dependencies (UD) specifications, significant effort has been made to adjust the annotation rules according to the idiosyncrasies and complexities of the Tamil language, thereby providing a valuable resource for linguistic research and NLP developments.
BordIRlines: A Dataset for Evaluating Cross-lingual Retrieval Augmented Generation (2024)

Large language models excel at creative generation but continue to struggle with the issues of hallucination and bias. While retrieval-augmented generation (RAG) provides a framework for grounding LLMs' responses in accurate and up-to-date information, it still raises the question of bias: which sources should be selected for inclusion in the context? And how should their importance be weighted? In this paper, we study the challenge of cross-lingual RAG and present a dataset to investigate the robustness of existing systems at answering queries about geopolitical disputes, which exist at the intersection of linguistic, cultural, and political boundaries. Our dataset is sourced from Wikipedia pages containing information relevant to the given queries and we investigate the impact of including additional context, as well as the composition of this context in terms of language and source, on an LLM`s response. Our results show that existing RAG systems continue to be challenged by cross-lingual use cases and suffer from a lack of consistency when they are provided with competing information in multiple languages. We present case studies to illustrate these issues and outline steps for future research to address these challenges.
Multi-Label Field Classification for Scientific Documents using Expert and Crowd-sourced Knowledge (2024)

Taxonomies of scientific research seek to describe complex domains of activity that are overlapping and dynamic. We address this challenge by combining knowledge curated by the Wikipedia community with the input of subject-matter experts to identify, define, and validate a system of 1,110 granular fields of study for use in multi-label classification of scientific publications. The result is capable of categorizing research across subfields of artificial intelligence, computer security, semiconductors, genetics, virology, immunology, neuroscience, biotechnology, and bioinformatics. We then develop and evaluate a solution for zero-shot classification of publications in terms of these fields.
Uncovering Differences in Persuasive Language in Russian versus English Wikipedia (2024)

We study how differences in persuasive language across Wikipedia articles, written in either English and Russian, can uncover each culture`s distinct perspective on different subjects. We develop a large language model (LLM) powered system to identify instances of persuasive language in multilingual texts. Instead of directly prompting LLMs to detect persuasion, which is subjective and difficult, we propose to reframe the task to instead ask high-level questions (HLQs) which capture different persuasive aspects. Importantly, these HLQs are authored by LLMs themselves. LLMs over-generate a large set of HLQs, which are subsequently filtered to a small set aligned with human labels for the original task. We then apply our approach to a large-scale, bilingual dataset of Wikipedia articles (88K total), using a two-stage identify-then-extract prompting strategy to find instances of persuasion. We quantify the amount of persuasion per article, and explore the differences in persuasion through several experiments on the paired articles. Notably, we generate rankings of articles by persuasion in both languages. These rankings match our intuitions on the culturally-salient subjects; Russian Wikipedia highlights subjects on Ukraine, while English Wikipedia highlights the Middle East. Grouping subjects into larger topics, we find politically-related events contain more persuasion than others. We further demonstrate that HLQs obtain similar performance when posed in either English or Russian. Our methodology enables cross-lingual, cross-cultural understanding at scale, and we release our code, prompts, and data.
Retrieval Evaluation for Long-Form and Knowledge-Intensive Image--Text Article Composition (2024)

This paper examines the integration of images into Wikipedia articles by evaluating image{--}text retrieval tasks in multimedia content creation, focusing on developing retrieval-augmented tools to enhance the creation of high-quality multimedia articles. Despite ongoing research, the interplay between text and visuals, such as photos and diagrams, remains underexplored, limiting support for real-world applications. We introduce AToMiC, a dataset for long-form, knowledge-intensive image{--}text retrieval, detailing its task design, evaluation protocols, and relevance criteria.Our findings show that a hybrid approach combining a sparse retriever with a dense retriever achieves satisfactory effectiveness, with nDCG@10 scores around 0.4 for Image Suggestion and Image Promotion tasks, providing insights into the challenges of retrieval evaluation in an image{--}text interleaved article composition context.The AToMiC dataset is available at https://github.com/TREC-AToMiC/AToMiC.
WikiBias as an Extrapolation Corpus for Bias Detection (2024)

This paper explores whether it is possible to train a machine learning model using Wikipedia data to detect subjectivity in sentences and generalize effectively to other domains. To achieve this, we performed experiments with the WikiBias corpus, the BABE corpus, and the CheckThat! Dataset. Various classical models for ML were tested, including Logistic Regression, SVC, and SVR, including characteristics such as Sentence Transformers similarity, probabilistic sentiment measures, and biased lexicons. Pre-trained models like DistilRoBERTa, as well as large language models like Gemma and GPT-4, were also tested for the same classification task.