Sharif Digital Repository

Many Text-to-Speech (TTS) systems, particularly in low-resource environments, struggle to produce natural and intelligible speech from grapheme sequences. One solution to this problem is to use Grapheme-to-Phoneme (G2P) conversion to increase the information in the input sequence and improve the TTS output. However, current G2P systems are not accurate or efficient enough for Persian texts due to the language’s complexity and the lack of short vowels in Persian grapheme sequences. In our study, we aimed to improve resources for the Persian language. To achieve this, we introduced two new G2P training datasets, one manually-labeled and the other machine-generated, containing over five million... 

Conversational Question Answering (CQA) has gained significant attention in recent years due to its potential to facilitate natural language interactions between humans and machines. The ability to effectively incorporate relevant history turns, which are previous utterances in a conversation, plays a crucial role in improving the overall performance of CQA systems. In this master's thesis, we explore the importance of conversational question answering and propose a novel approach for selecting relevant history turns to enhance the accuracy and relevance of the system's responses. Initially, we provide an overview of the recent models developed for addressing the CQA challenge. We analyze... 

In this thesis, we introduce a novel Artificial Intelligence (AI) system inspired by the philosophical and psychoanalytical concept of imagination as a ``Re-construction of Experiences". Our AI system is equipped with an imagination-inspired module that bridges the gap between textual inputs and other modalities, enriching the derived information based on previously learned experiences. A unique feature of our system is its ability to formulate independent perceptions of inputs. This leads to unique interpretations of a concept that may differ from human interpretations but are equally valid, a phenomenon we term as ``Interpretable Misunderstanding". We employ large-scale models,... 

Nowadays Artificial Intelligence systems are ubiquitous. One of the important applications is textual question-answering systems, which provide a means of information retrieval in a user-friendly manner. Reasoning is an inseparable part of human daily life, and people use reasoning to judge and find rational and correct answers to questions. To get the desired output from question-answering systems, these systems must be equipped with reasoning. This research focuses on improving question answering by considering Commonsense Reasoning. The two most important weaknesses of the existing question-answering systems are the questions being in the form of multiple-choice, which is far from a... 

Formality style transfer, in other words, automatic transfering style of informal text to formal and vice versa, means changing the style and form of a sentence without changing its content. With the increasing progress of deep neural networks, the formality style transfer in other languages has attracted the attention of other researchers and has made significant progress in natural language processing tasks. Due to the availability of parallel data in the English language, the task of style transfer has been approached and designed basically in the framework of the "encoder-decoder" architecture of neural networks. However, due to the lack of parallel datasets in the Persian language, this... 

Sentiment analysis is a useful problem which could serve a variety of fields from business intelligence to social studies and even health studies.Besides, solutions using Pre-Trained models have showed superiority over other ones. Hence we attempted to solve sentiment analysis by the help of pre-trained models. Using SemEval 2022 Task 10 formulation of sentiment analysis and proposing a new method, we improved the task baselines. Task baselines have used Dependency Graph Parsing and LSTM in their solutions respectively.Our solution outperformed the best one across all datasets and according to the Sentiment Graph F1 metric, defined in the task description, by at least 2 points. In our... 

Due to the increase in the number of people interested in learning new languages, in recent years, multiple systems have been developed to teach new languages to those who are interested. These systems are called Computer Assisted Language Learning (CALL). However, the most credible CALL systems, like Duolingo, do not support Persian. So the of this study is to design and implement one of the technical parts of CALL systems, the Computer Assisted Pronunciation Training(CAPT), which is the part responsible for evaluating the learners' input voice's pronunciation and generating appropriate score and feedback.In this study, good pronunciation means correct expression of words, correct... 

In recent years, deep neural networks have achieved significant improvements in the field of automatic text summarization by using neural sequence architectures. However,the results of these improvements are more tangible in the production of short summaries (a few words or single sentences). In the field of producing long (multisentence) abstracts, the presented models suffer from several issues; These models produce the details of the events incorrectly and tend to generate the phrases been produced before repeatedly. The wording from the output of these models is very close to the original text. Also, the metrics used to evaluate the quality of produced summaries do not have the ability... 

Abstract: This thesis focuses on generating text from data. The Data-to-Text system must have three capabilities; First, it should be able to produce coherent, comprehensible, fluent text that is close to human natural language, in such a way that it is not possible to distinguish it from texts written by humans. Second, to be able to produce a variety of sentences to express the same concept. The third is to be able to express the information of the input data without repetition, redundancy, and omission in the output sentences. The latter is one of the main challenges of data-to-text systems because not being faithful to the input data can lead to se- rious problems in real-world... 

This thesis focuses on generating text from data. The Data-to-Text system must have three capabilities; First, it should be able to produce coherent, comprehensible, fluent text that is close to human natural language, in such a way that it is not possible to distinguish it from texts written by humans. Second, to be able to produce a variety of sentences to express the same concept. The third is to be able to express the information of the input data without repetition, redundancy, and omission in the output sentences. The latter is one of the main challenges of data-to-text systems because not being faithful to the input data can lead to se- rious problems in real-world applications. Until... 

Today, the production of colloquial texts in messengers, search engines, and question and answer systems has increased significantly, while text documents in other fields have a formal tone and style. Thus, there is a need for a system to convert these texts from colloquial form to the formal style. Attention to this need in non-Persian languages has also been recently and seriously felt, but almost at the time of writing, an efficient system has not been offered, and this issue requires more work in Persian than in languages such as English. In general, transferring texts from one form to another falls into the category of natural language processing applications and is called "style... 

Quality and intelligibility are two aspects of speech that are affected by various factors, such as background noise and echo. The performance of many commercial and military speech-based systems depends on at least one of these aspects of speech. Therefore, this research aims to design an improvement model to remove background noise and reverberation from the speech signal. The model training framework is based on deep learning methods and has a supervised approach in the time domain. The input of this system is the raw waveform of the speech signal mixed with noise and reverberation, and the output is the enhanced waveform of this signal. An architecture is proposed in this thesis based on... 

Automatic recognition of musical Maqams has been one of the challenging problems in Music Information Retrieval. Despite the increasing amount of related research in recent years, we are still far away from building related real-life applications. Nevertheless, a very small portion of these research is dedicated to automatic recognition of Maqams in recitation of the Holy Quran. In this thesis, as a first attempt, we have used machine learning methods to classify six Maqam families which are commonly used in Quran recitation. Also, due to the lack of pre-exisiting datasets, we have annotated approximately 1325 minutes of Tadwir recitation from two prominent Egyptian reciters, i.e., Muhammad... 

Named Entity Recognition (NER) is a key component and the first step of many natural language processing tasks such as question answering systems, information retrieval, machine translation, text summarization, and so on. First NER system initially used rule-based and machine learning methods, which grew significantly with the advent of deep learning architectures as well as the development of hardware and data resources. Traditional deep learning methods used convolutional and recursive neural networks that had disadvantages such as gradient vanishing and non-parallel computing, respectively. In addition, the need for huge corpus and powerful hardware resources was one of the problems of... 

In recent years with advances in artificial intelligence, numerous methods have been proposed for tasks that sometimes are difficult for human or requires a long time to overcome. Text-to-speech systems are among the methods that lead to easier human real life in different applications. The goal of this research is to propose a method for designing a Persian text-to-speech system while this system can be used in a wide domain of Persian texts and its output sound looks natural. In recent years, significant advances have been made in designing these systems for common languages like English. Most of these advances are because of proposed deep learning methods that are suitable for these... 

Diverse personal names are mentioned in everyday news but news agencies do not separate entities with same or equal names. This could make irrelevant news appear while searching an ambiguous name. Personal Name Disambiguation in news seeks to partition a significant amount of news to distinct classes each of which belongs to a single entity in the real world. In this thesis, which up to the researcher is the first of its kind at least in Persian, researcher gained opportunity of using FarsiYar News Dataset and to be specific 50,000 of news in FarsNews dataset which were published in the year 1397. First of all, a database was built using these news data and then the nonstructured news were... 

Due to the emergence of social networks in recent years and people’s usage of them for expressing their thoughts and emotions, there are lots of user data in these networks. The development of social networks has created a good opportunity for organizations and people to extract user profiles from social networks. Hence, user profiling has become an interesting problem for researchers. Predicting users’ occupational class is one of the main problems in this field. Most of the existing related works use only textual features of users, whereas users’ relations graph can give useful information about users. In this research, we propose a model based on Graph Neural Networks (GNNs) to predict... 

This project has been conducted in two related phases: In the first phase, we have attempted to write a program capable of answering thematic similarity multiple-choice questions without utilizing any training data. The best performance in this phase was attained by the 25-topic LDA model using the Hellinger distance between the probability distributions of the poetic verses. This model managed to attain an accuracy of 42%, which is very close to the average human performance of 43%. In the second phase, two tasks of seven-class classification and binary classification were defined based on the p-value of the questions. To this end, the questions were initially ranked according to the... 

Following dramatic changes after using deep learning method as a solution for Natural Language Processing tasks, Transformer architecture get popular. Based on that, then BERT Language model presented and get state-of-the-art as a solution for a lot of language processing tasks. It was a turning point in Natural Language Processing field. Also, in cross-lingual methods research line motivated by developing a common space for representation of language units, e.g. words, sentences, in more that one language, get some remarkable improvements. However, for languages distant from English such as Persian or Arabic the methods' performance was not clear. In this work, we performed some innovative... 

Despite significant advances in dialog systems, data-driven dialog systems are often unable to have content-driven conversations and present real-world knowledge in the context which is due to the lack of knowledge-based conversations in the research datasets and the lack of external knowledge in their architecture. As a result, they are far from the real world and opendomain use-cases. The goal of this research is to introduce a dialogue system based on external knowledge and facts using Deep Learning that the external knowledge can be updated and, the model will adapt itself and take them into account to have a rich conversation. It must be noted that external knowledge is assumed as a...