Development of a Multilingual Model for Machine Sentiment Analysis in the Serbian Language

Draskovic, Drazen; Zecevic, Darinka; Nikolić, Boško

doi:10.3390/math10183236

Cited by 10 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Initially, the original archetype of the NAMT model is drilled and validated in high-resource languages, with BLEU scores evaluated on official test sets, encompassing Newstest 2014 for WMT English-German, Newstest 2016 for WMT English-Romanian, or the development set for IWSLT16 English-German. When it comes to low-resource languages, however, training data-hungry NAT models is a non-trivial challenge, confronted with limited language processing tools and an inadequate parallel corpus of target minor languages, let alone potential accuracy degradation, which can be amplified due to the increased morphological complexity, such as Serbian [118,119], and the ulterior linguistic connections compared to resourceprosperous languages such as English and Romanian. To contend with this dilemma, extra data augmentation techniques such as back-translations offer viable means to train the NAT models, considering the inherently dual properties of machine translation tasks [120,121].…”

Section: Discussionmentioning

confidence: 99%

A Survey of Non-Autoregressive Neural Machine Translation

Chen

Zhang

2023

Electronics

View full text Add to dashboard Cite

Non-autoregressive neural machine translation (NAMT) has received increasing attention recently in virtue of its promising acceleration paradigm for fast decoding. However, these splendid speedup gains are at the cost of accuracy, in comparison to its autoregressive counterpart. To close this performance gap, many studies have been conducted for achieving a better quality and speed trade-off. In this paper, we survey the NAMT domain from two new perspectives, i.e., target dependency management and training strategies arrangement. Proposed approaches are elaborated at length, involving five model categories. We then collect extensive experimental data to present abundant graphs for quantitative evaluation and qualitative comparison according to the reported translation performance. Based on that, a comprehensive performance analysis is provided. Further inspection is conducted for two salient problems: target sentence length prediction and sequence-level knowledge distillation. Accumulative reinvestigation of translation quality and speedup demonstrates that non-autoregressive decoding may not run fast as it seems and still lacks authentic surpassing for accuracy. We finally prospect potential work from inner and outer facets and call for more practical and warrantable studies for the future.

show abstract

Section: Discussionmentioning

confidence: 99%

A Survey of Non-Autoregressive Neural Machine Translation

Chen

Zhang

2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Generation: the new hidden state 𝑠 𝑡 is produced in the 𝑐𝐺𝑅𝑈 at time step 𝑡. The mathematical formulas are described in (8) to (11).…”

Section: Decodermentioning

confidence: 99%

Sanskrit to Hindi language translation using multimodal neural machine translation

Kammar,

Baraki,

Ganganayaka

et al. 2024

IJEECS

View full text Add to dashboard Cite

Machine translation (MT) is a subfield of computer features that focuses on the automatic translation from one natural language into another without any human involvement. Due to native people interacting in a variety of languages, there is a great need for translating information between languages to send and communicate thoughts. However, they disregard the significance of semantic data encoded in the text features. In this paper, multimodal neural machine translation (MNMT) is proposed for Sanskrit-Hindi translation. The main goal of the proposed method is to fully utilize semantic text features on NMT architecture and to minimize testing and training time. The MNMT is validated on two different NMT architectures: recurrent neural network (RNN) and self-attention network (SAN). The MNMT method’s efficacy is demonstrated by employing the dataset of Sanskrit-Hindi Corpora. Extensive experimental outcomes represent the proposed method’s enhancement over baselines on both architectures. The existing methods, namely, English-to-Indian MT system, Sanskrit-Hindi MT system, and hybrid MT system are used to justify the efficacy of the MNMT method. When compared to the above-mentioned existing methods, RA-RNN respectively achieves a superior BLEU and METEOR of 80.5% and 75.3%, while the RA-SAN respectively achieves a superior BLEU and METEOR of 78.2% and 77.1%.

show abstract

“…For instance, researchers have dedicated their efforts to constructing the Serbian wordnet [48], a valuable resource that organizes Serbian lexical units and their semantic relations. Sentiment analysis, another area of interest, has been applied to analyze the sentiment expressed in Serbian newspaper content [49] as well as movie reviews [50], and the classification of documents based on n−grams [51], and [52] music album reviews.…”

Section: Sentiment Analysis In the Bosnian/croatian/serbian/slovenian...mentioning

confidence: 99%

Impact of Negation and AnA-Words on Overall Sentiment Value of the Text Written in the Bosnian Language

Jahić

Vičič

2023

Applied Sciences

View full text Add to dashboard Cite

In this manuscript, we present our efforts to develop an accurate sentiment analysis model for Bosnian-language tweets which incorporated three elements: negation cues, AnA-words (referring to maximizers, boosters, approximators, relative intensifiers, diminishers, and minimizers), and sentiment-labeled words from a lexicon. We used several machine-learning techniques, including SVM, Naive Bayes, RF, and CNN, with different input parameters, such as batch size, number of convolution layers, and type of convolution layers. In addition to these techniques, BOSentiment is used to provide an initial sentiment value for each tweet, which is then used as input for CNN. Our best-performing model, which combined BOSentiment and CNN with 256 filters and a size of 4×4, with a batch size of 10, achieved an accuracy of over 92%. Our results demonstrate the effectiveness of our approach in accurately classifying the sentiment of Bosnian tweets using machine-learning techniques, lexicons, and pre-trained models. This study makes a significant contribution to the field of sentiment analysis for under-researched languages such as Bosnian, and our approach could be extended to other languages and social media platforms to gain insight into public opinion.

show abstract

Development of a Multilingual Model for Machine Sentiment Analysis in the Serbian Language

Cited by 10 publications

References 34 publications

A Survey of Non-Autoregressive Neural Machine Translation

A Survey of Non-Autoregressive Neural Machine Translation

Sanskrit to Hindi language translation using multimodal neural machine translation

Impact of Negation and AnA-Words on Overall Sentiment Value of the Text Written in the Bosnian Language

Contact Info

Product

Resources

About