Cross-language Information Retrieval Research Articles

Advancements in Neural Machine Translation: Methodological Innovations and Empirical Insights for Cross-Linguistic Discourse Preservation

Abstract: Natural Language Processing (NLP), in the form recognizable today, really began to take hold in the 1980s, when machine learning helped propel it to soaring heights. However, due to a lack of processing power, machine learning, and to an extent, NLP, started slowing down in innovation and ideas and had almost ground to a relative halt, until the last decade, when a sudden increase in both productivity and interest in the machine learning helped increase the amount of knowledge in the space itself. This review provides several different case studies using different methodologies. The first paper was a deep analysis on how researchers were able to use Tesseract and Google Vision in tandem with automatic data mining methods to enrich the Cherokee language database in order to preserve it from extinction. The second paper takes a query translation-based approach toward translating English to Indian languages and utilizes a Multilingual Cross-Language Information Retrieval (MLCIR) system with tools such as Part of Speech Tagger (POST), Stop-Word, and Porter Stemmer. The third paper presents CoVe, which transfers knowledge from machine translation to improve performance on NLP tasks like sentiment analysis and question answering by using contextualized word vectors along with word embeddings, achieving new state-of-the-art results on some datasets. The fourth paper aims to translate English to Pakistan Sign Language (PSL) and also uses POST and goes through dependency analysis, sentence classification, and PSL using PLS trees. The fifth paper uses a Multilingual Neural Machine Translation (NMT) system for LowResource languages and incorporates two main models: a recurrent NMT and a transformer NMT. The sixth paper analyzes how a fine-tuned transformer model seems to work better than transformer models trained from scratch on high-resource languages, while vice-versa seems to occur for low-resource languages. The seventh paper adds to this by talking about how multilingual translation seems to work better than a back-translation model. Given the diverse array of approaches that could be used, we aim to identify the most efficient and correct methodology for future researchers to use in their work, based on the papers in this literature review.

BiLSTMs and BPE for English to Telugu CLIR

A crucial component of Cross Lingual Information Retrieval (CLIR) is Neural Machine Translation (NMT). NMT performs a good job of transforming queries in the English language into Indian languages. This study focuses on the translation of English queries into Telugu. For translations, the NMT will make use of a parallel corpus. Due to a lack of resources in the Telugu language, it is exceedingly challenging to provide NMT with sizable parallel corpora. Thus, the NMT will encounter an issue known as Out of Vocabulary (OOV). Long Short-Term Memory (LSTM) with Byte Pair Encoding (BPE), which breaks up rare words into subwords and attempts to translate them to solve the OOV issue. Issues such as Named Entity Recognition (NER) continue to plague it. In sequence-to-sequence models, bidirectional LSTMs can solve certain NER challenges. Systems that need to be trained in both directions to recognize named entities can benefit from the use of Bidirectional LSTMs (BiLSTMs). The translation efficiency of NMT with BiLSTMs is significantly higher than normal LSTMs, as indicated by the accuracy metrics and Bilingual Evaluation Understudy (BLEU) score.

Meta-Learning for Neural Machine Translation

Abstract: Neural Machine Translation (NMT) has significantly advanced the field of automated language translation, yet challenges persist in adapting to diverse language pairs, handling low-resource languages, and ensuring domain-specific translation accuracy. To address these challenges, this study explores the integration of meta-learning methodologies in NMT, aiming to enhance the adaptability and generalization capabilities of translation models. Through a comprehensive analysis of various meta-learning approaches, including Model-Agnostic Meta-Learning (MAML), metric-based meta-learning, and optimization-based meta-learning, we demonstrate the potential for improved translation accuracy and fluency across diverse language pairs and domains. Drawing upon a diverse set of bilingual corpora and employing the Transformer model as the base architecture, our experimental evaluation highlights the substantial performance improvements achieved through the integration of metalearning techniques. The case studies and use cases presented in this study underscore the practical applications of the integrated meta-learning methodologies in facilitating cross-lingual information retrieval, low-resource language localization, specialized domain translation, and multimodal translation. While acknowledging the computational complexity and ethical implications, this study emphasizes the importance of collaborative and interdisciplinary research efforts to advance the development of more adaptive and contextually aware translation systems. The findings and insights presented in this study offer valuable implications for the advancement of NMT and automated language translation practices

Bidirectional LSTMs with Byte Pair Encoding in NMT for CLIR using English and Telugu Parallel Corpus

The Neural Machine Translation (NMT) is very crucial for Cross-Lingual Information Retrieval (CLIR). NMT is effective in translating English language queries to the Telugu Language. In this paper, we are translating English queries to Telugu. The NMT will utilize a parallel corpus for translations. Telugu is a resource-poor language, it is very difficult to supply large amounts of parallel corpus to NMT. So the NMT will have a problem called Out Of Vocabulary (OOV). To overcome this problem Byte Pair Encoding (BPE) is used along with Long Short Term Memory (LSTM), which segments the rare words into sub-words and tries to translate the rare words. It still faces problems like Named Entity Recognition (NER). Some problems of NER can be solved by utilizing bidirectional LSTMs in sequence-to-sequence models. The bidirectional LSTMs (BiLSTMs) will be helpful in training systems in both directions for recognizing the named entities. The accuracy parameters and a BLEU score show the translation quality of NMT with bidirectional LSTMs has slightly more accuracy than regular LSTMs which is considerable.

Cross Language Information Retrieval based on Automatic Query Translation for Marathi Documents

Abstract: The current research article explores the realm of Cross Language Information Retrieval (CLIR) and its significance in the digital age. It addresses the challenges faced in CLIR, including lexical and semantic disparities, the scarcity of parallel corpora, cultural nuances, and more. The article discusses innovative solutions encompassing Machine Translation, Query Expansion, Cross-Lingual Word Embeddings, and Multilingual Information Retrieval Models to enhance CLIR's effectiveness. Furthermore, it sheds light on Information Retrieval Models, such as the Boolean Model, Vector Space Model (VSM), and Probabilistic Models, explaining their principles and applications. The study also presents experimental results highlighting the limitations of monolingual IR models and the effectiveness of crosslingual techniques, such as translation and query expansion, in improving CLIR, making it a valuable tool for accessing information across languages.

Kannada to English Agricultural Cross-Lingual Retrieval: Enhancing Knowledge Access in Farming Practices

Abstract: This paper presents the development and implementation of a specialized cross-lingual information retrieval system tailored for agriculture-related queries in the Kannada language. The primary objective of the system is to facilitate accurate translation of Kannada queries into English, the target language, and to retrieve relevant documents containing vital agricultural information. The proposed system addresses key challenges including incorporating effective query preprocessing techniques, designing an efficient document retrieval mechanism, and establishing optimal data indexing strategies as well as strategies have been introduced to mitigate the challenges posed by the diversity of Kannada dialects. By overcoming the language barrier, this system enables seamless and effective knowledge dissemination in the agriculture domain.

MarQO: A query optimizer in multilingual environment for information retrieval in Marathi language

Information retrieval is a crucial component of modern information systems. A significant portion of the vast amount of information stored worldwide is in local languages. While most information retrieval systems are designed primarily for English, there is a growing need for these systems to work with data in languages other than English. Cross Language Information Retrieval (CLIR) systems play a pivotal role in enabling information retrieval across multiple languages. However, these systems often face challenges due to ambiguities in query translation, impacting retrieval accuracy. This paper introduces "MarQO," a query optimizer designed to address these challenges in the context of Marathi language. MarQO employs a multi-stage approach, including lexical processing, extraction of multi-word terms, synonym addition, phrasal translations, utilization of word co-occurrence statistics, and more. By disambiguating query keyword translations, MarQO significantly improves the accuracy of translations, thereby leading to more relevant document retrieval results.

Construction of Mizo: English Parallel Corpus for Machine Translation

Parallel corpus is a key component of statistical and Neural Machine Translation (NMT). While most research focuses on machine translation, corpus creation studies are limited for many languages, and no research paper on a Mizo–English corpus exists yet. A high-quality parallel corpus is required for Natural Language Processing activities including machine translation, Chatbots, Transliteration, and Cross-Language Information Retrieval. This work aims to investigate parallel corpus creation techniques and apply them to the Mizo–English language pair. Another goal is to test machine translation on the newly constructed corpus. We contributed to LF Aligner tool to support Mizo language for Mizo sentence alignment in corpus development. Our effort created the first large-scale Mizo–English parallel corpus with over 529K sentences. The pre-processed corpus was used for Mizo-to-English NMT. It was evaluated using BLEU, Character F1 Score (ChrF), and Translation Edit Rate (TER) scores. Our system achieved BLEU 45.08, ChrF 65.36, and TER 41.16, setting a new benchmark for Mizo-to-English translation.

Cross-language information retrieval for poetry form of literature-based on machine transliteration using CNN

Transliteration is phonetically translating a language’s words into an international or non-native screenplay. The machine translation process now plays an essential role in scholarly research. The most crucial complement criterion of the English translation system is preserving the phonetic qualities of the language specification after English translation in the chosen language. However, a suitable bilingual text corpus is necessary for statistical models to attain improved transliteration accuracy. Marathi-to-English direct machine translation is done through a cross-language information retrieval system using the CNN classifier model in this proposed research. The proposed method considers a sequence labelling issue brought on by the split transliteration units used in the process. All half-consonant clusters in the Devanagari script are effectively mapped as half-consonant “a” s and labelled using the Modified Intermediate Phonetic Code (MIPC). After generating the phonetic units for each feature in the base and aim languages, the weight is assigned to a phonetic unit in both languages, and individual phonetic unit probabilities are computed. If the probability is zero, then segments are established and recalculated for each segment based on the target phonetic unit location in the word. Therefore, the proposed approach classifies the required phonetic unit with a high accuracy rate.

Cross-Language Tourism News Retrieval System Using Google Translate API on SEBI Search Engine

Cross-Language Information Retrieval (CLIR) is responsible for retrieving information stored in a language different from the language of the query provided by the user. Some translation methods commonly used in CLIR are Dictionary, Parallel corpora, Comparable corpora, Machine translator, Ontology, and Transitive-based. The query must be translated to the target language, followed by preprocessing and calculating the similarity between the query and all documents in the corpus. The problem is the time and accuracy of query translation. Moreover, the queries are not written as complete sentences according to certain language rules. Stemming, for example, every language has its own method. Indonesian has basic words and affixes in the form of prefixes, suffixes, infixes, and confixes, while English only has suffixes. Stemming takes a long time in text processing. In the Indonesian search engine (SEBI), the provision of cross-language tourism news retrieval is realized using the Google Translate API, which translates the Query and all documents into English, Porter's stemming technique to convert each term to its general form, and cosine similarity to calculate similarity. This approach can deliver cross-language tourism news instantly while increasing the precision and efficiency of the SEBI search engine, although some improvements are needed to provide a more precise and efficient similarity computation.

SPEEDSER: A Possibilistic System for Query Disambiguation, Expansion and Translation

We design, implement and assess in this paper a new architecture of a possibilistic mono- and cross-language information retrieval (IR/CLIR) system. The latter is useful to experiment query disambiguation, expansion and translation processes in both IR and CLIR frameworks. We take advantage of possibility theory to overcome the problems of query disambiguation and expansion in an uncertain and imprecise IR/CLIR context. We investigate the impact of combining possibilistic query disambiguation with expansion on IR/CLIR efficiency. A co-occurrence graph representation is exploited to quantify the similarity between query terms and their semantically close words (expansion task) or between query terms and their possible meanings (disambiguation task). We extend the possibilistic mono-language query disambiguation approach to a cross-language framework. We conduct a set of experiments using ROMANSEVAL data collection, the French–English parallel text corpus Europarl and the CLEF-2003 French–English IR/CLIR test collection. Results highlight some statistically significant improvements of our possibilistic approaches when compared to some state-of-the-art IR/CLIR works.

Exploring Snippets as a Dataset to Overcome Challenges in CLIR

Cross-lingual information retrieval (CLIR) is a challenging task that requires overcoming linguistic barriers to match user queries with relevant documents in different languages. One of the major challenges in CLIR is the lack of parallel corpora, which hinders the development of effective translation models. This challenge can be addressed using snippets as a dataset to train CLIR models. Snippets can be automatically extracted from various sources, such as search engine result pages and can provide a rich and diverse set of collections for cross-lingual information retrieval. This paper initially discusses the challenges in CLIR and then explores the use of snippets as a dataset which can lead towards the development or improvements in the techniques to improve the retrieval effectiveness and further discusses the advantages and limitations of using snippets dataset in CLIR.

Recent Query Reformulation Approaches for Information Retrieval System - A Survey

Abstract:Around trillions of data are uploaded to the internet every year. Extracting useful information using only a few keywords has become a major challenge. The field of Query Reformulation (QR) is focused on the efficient retrieval of information to overcome this. It is widely used in the domain of information retrieval (IR) and related fields such as search engines, multimedia IR, cross-language IR, recommender systems, and so on. Query reformulation techniques incur extra computational costs. Due to this reason, the use of query reformulation techniques is sometimes prohibited in internet searches as searching over the internet requires a fast response time. But due to the success of NLP (Natural Language Processing) using machine learning/deep learning in recent years, there has been a boom of study in this area. In this literature, a variety of term selection, term extraction, and query reformulation strategies based on recent technologies used by researchers have been presented, necessitating a wide survey to focus research in this promising area. Recent QR approaches and the datasets, techniques, and evaluation metrics used in this paper will help researchers understand and focus more on research in this promising area so that a better solution will be proposed. From the survey, it may be observed that one of the hottest subjects in the field of IR right now is applying deep learning to IR systems for query reformulation.

Multilingualism and Multiculturalism in Digital Libraries

Multilingualism and multiculturalism have become major preoccupations for Library and Information Science (LIS) which has to be as fair as possible to ensure and sustain knowledge diversity as a driver for development. This paper offers a broad overview of multilingualism and multiculturalism embedding into Digital Libraries (DL). We survey studies written in English over the past two decades (from 2002 to 2022) and indexed in one or more of the following databases: Science Direct, Google Scholars, Taylor and Francis, and Emerald Insight. We summarized studies to offer insights and outline opportunities in this area and classify them into five research themes: (i) studies describing Cross-Language Information Retrieval (CLIR) in Digital Libraires, (ii) studies describing building, enriching and using multilingual resources in digital libraries (iii) studies focusing on multilingual digital libraries initiatives, (iv) studies on collaboration, minority languages and ethics issues in digital libraries, and (v) studies on multilingual user needs. This survey aims to identify, collect and categorize the research literature on multilingualism and multiculturalism efforts in Digital Libraries.

Study on Machine Translation Teaching Model Based on Translation Parallel Corpus and Exploitation for Multimedia Asian Information Processing

Text in one language can be mechanically translated into another language using machine translation (MT). It is possible to anticipate a sequence of words, generally modeling full sentences using machine translation in a single integrated model. Human language's flexibility makes automatic translation an artificial intelligence (AI) challenge of the highest order. A single model rather than a pipeline of fine-tuned models is now the best way to attain state-of-the-art outcomes in machine translation. For example, words having numerous meanings, phrases that use more than one grammatical structure, and other grammar issues make it difficult for a machine to translate; however, many misinterpretations translate to be a breeze. A teacher's job is to assist pupils in overcoming the emotional and cognitive obstacles that stand in the way of developing effective problem-solving abilities. Students will benefit from developing problem-solving abilities since they will apply what they have learned to new circumstances. MT-AI, machine translation technology, and products have been employed in a wide range of applications, including business travel, tourism, and cross-lingual information retrieval. Text translation and phonetic translation are two types of translations that focus on the content of the source language. It is possible to create self-learning systems by injecting machine learning techniques into existing software and then observing the results of such injection. Computer software can translate a massive volume of text in a short period. It takes longer for a human translator to perform the same work as a computer program. The simulation investigation is developed based on correctness and effectiveness, demonstrating the proposed framework's reliability of 95.1%.

Spoken language identification based on the transcript analysis

Abstract Language identification is a great challenge in language engineering, which arises along with the tasks of speech recognition, machine translation, cross-language information retrieval, intelligent dialogue system creation, etc. The presented article introduces the intelligent language identification technology, which is based on speech recognition and statistical methods of spectrogram analysis. The approach to the automatic identification of the spoken language sample uploaded to the system, in particular from video streaming services such as YouTube, is put forward. The article focuses on the automatic identification of spoken language, taking into account several speech recognition solutions for correct or incorrect speech recognition and its conversion into correct or incorrect text. The obtained algorithm is demonstrated in the Ukrainian and Russian languages. The identification quality of the language of an utterance, which lasts >30 s is almost 100%, and for the utterance of a duration of 30 s, the quality is 98%, and for the 5-s utterance, it reaches 89.6%. In addition to that, the system performance is contingent on the streaming speed, so it is a real-time system.

A Comparative Optimization Model of Japanese Literature Characteristics for Cognitive Retrieval of Cross-Language Information.

Cross-language information cognitive retrieval has grown in importance as a study area due to the multilingual character of Internet resources and the diversifying languages spoken by users. This paper analyses and illustrates the key characteristics of Japanese literature from three aspects: ideological structure, structural form, and emotional expression, and makes a straightforward comparison with Chinese literature using a number of well-known Japanese literary masterpieces as examples. An important aspect of this literature is that it is full of lingering feelings, leaving room for the readers to imagine. The lingering charm is endless, obscure, and meaningful, which is the traditional style of Japanese literature. It also focuses on the depoliticization of Japanese literature, that is, the relative separation between literature and politics. The subjectivity of Japanese writing, which goes beyond just conveying feelings but involves less objective description, is related to this. The process of cross-language information retrieval in foreign nations is primarily separated into three stages, according to the changes in research objects. Currently, adding a language conversion mechanism to a monolingual information retrieval system serves as the primary language information retrieval solution. Currently, nearly 40% of the global popularity of literature comes from Japan. In this article, the background and concept of cross-language information retrieval are introduced, and its types, system models, and several key cross-language information retrieval methods are explained, and some solutions to the factors influencing the cross-language information retrieval effect are suggested.

An Ontology based Smart Management of Linguistic Knowledge

Natural language processing provides a very significant contribution to various application areas such as multilingual big data, information retrieval, data integration and multilingual web. However, handling linguistic knowledge to develop such lingware applications is a crucial issue, especially for linguistic novice users. To deal with this issue, a "smart" linguistic knowledge management may help the users to understand the meaning, scope and especially the use of related techniques and algorithms. In this paper, (1) we propose a semantic processing of linguistic knowledge based on a multilingual linguistic domain ontology, called LingOnto. Compared to related work, LingOnto does not only handles linguistic data, but also linguistic processing functionalities and linguistic processing features. Besides, it allows, via a reasoning engine, inferring new linguistic knowledge and assisting in the process of proposing lingware applications. This is particularly useful for novice users, but can also provide new perspectives for the expert ones. LingOnto covers the French, English and Arabic languages. (2) We propose also an assisted user friendly ontology visualization tool called LingGraph. It facilitates the interaction with LingOnto. It offers an easy to use interface for users not familiar with ontologies. It is based on a SPARQL pattern-based approach to allow a smart search interaction functionality to visualize only the ontological view corresponding to the user’s needs and preferences. In order to evaluate LingOnto, we apply it to a framework of identifying valid natural language processing pipelines. Finally, we give the results of the carried-out experiments.

Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement

Cross-Lingual Information Retrieval (CLIR) aims to rank the documents written in a language different from the user’s query. The intrinsic gap between different languages is an essential challenge for CLIR. In this paper, we introduce the multilingual knowledge graph (KG) to the CLIR task due to the sufficient information of entities in multiple languages. It is regarded as a “silver bullet” to simultaneously perform explicit alignment between queries and documents and also broaden the representations of queries. And we propose a model named CLIR with HIerarchical Knowledge Enhancement (HIKE) for our task. The proposed model encodes the textual information in queries, documents and the KG with multilingual BERT, and incorporates the KG information in the query-document matching process with a hierarchical information fusion mechanism. Particularly, HIKE first integrates the entities and their neighborhood in KG into query representations with a knowledge-level fusion, then combines the knowledge from both source and target languages to further mitigate the linguistic gap with a language-level fusion. Finally, experimental results demonstrate that HIKE achieves substantial improvements over state-of-the-art competitors.

Neural topic-enhanced cross-lingual word embeddings for CLIR

Cross-lingual information retrieval (CLIR) methods have quickly made the transition from translation-based approaches to semantic-based approaches. In this paper, we examine the limitations of current unsupervised neural CLIR methods, especially those leveraging aligned cross-lingual word embedding (CLWE) spaces. At the moment, CLWEs are normally constructed on the monolingual corpus of bilingual texts through an iterative induction process. Homonymy and polysemy have become major obstacles in this process. On the other hand, contextual text representation methods often fail to outperform static CLWE methods significantly for CLIR. We propose a method utilizing a novel neural generative model with Wasserstein autoencoders to learn neural topic-enhanced CLWEs for CLIR purposes. Our method requires minimal or no supervision at all. On the CLEF test collections, we perform a comparative evaluation of the state-of-the-art semantic CLWE methods along with our proposed method for neural CLIR tasks. We demonstrate that our method outperforms the existing CLWE methods and multilingual contextual text encoders. We also show that our proposed method obtains significant improvements over the CLWE methods based upon representative topical embeddings.