Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit

• URL: http://arxiv.org/abs/2308.08807v1
• Date: Thu, 17 Aug 2023 06:33:33 GMT
• Title: Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit
• Authors: Jivnesh Sandhan
• Abstract summary: This thesis aims to make Sanskrit manuscripts more accessible to the end-users through natural language technologies. The morphological richness, compounding, free word orderliness, and low-resource nature of Sanskrit pose significant challenges for developing deep learning solutions. We identify four fundamental tasks, which are crucial for developing a robust NLP technology for Sanskrit.
• Score: 1.184066113335041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The primary focus of this thesis is to make Sanskrit manuscripts more accessible to the end-users through natural language technologies. The morphological richness, compounding, free word orderliness, and low-resource nature of Sanskrit pose significant challenges for developing deep learning solutions. We identify four fundamental tasks, which are crucial for developing a robust NLP technology for Sanskrit: word segmentation, dependency parsing, compound type identification, and poetry analysis. The first task, Sanskrit Word Segmentation (SWS), is a fundamental text processing task for any other downstream applications. However, it is challenging due to the sandhi phenomenon that modifies characters at word boundaries. Similarly, the existing dependency parsing approaches struggle with morphologically rich and low-resource languages like Sanskrit. Compound type identification is also challenging for Sanskrit due to the context-sensitive semantic relation between components. All these challenges result in sub-optimal performance in NLP applications like question answering and machine translation. Finally, Sanskrit poetry has not been extensively studied in computational linguistics. While addressing these challenges, this thesis makes various contributions: (1) The thesis proposes linguistically-informed neural architectures for these tasks. (2) We showcase the interpretability and multilingual extension of the proposed systems. (3) Our proposed systems report state-of-the-art performance. (4) Finally, we present a neural toolkit named SanskritShala, a web-based application that provides real-time analysis of input for various NLP tasks. Overall, this thesis contributes to making Sanskrit manuscripts more accessible by developing robust NLP technology and releasing various resources, datasets, and web-based toolkit.

Related papers

• One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks [26.848664285007022]
  ByT5-Sanskrit is designed for NLP applications involving the morphologically rich language Sanskrit. It is easier to deploy and more robust to data not covered by external linguistic resources. We show that our approach yields new best scores for lemmatization and dependency parsing of other morphologically rich languages.
  arXiv  Detail & Related papers  (2024-09-20T22:02:26Z)
• Multilingual Evaluation of Semantic Textual Relatedness [0.0]
  Semantic Textual Relatedness (STR) goes beyond superficial word overlap, considering linguistic elements and non-linguistic factors like topic, sentiment, and perspective. Prior NLP research has predominantly focused on English, limiting its applicability across languages. We explore STR in Marathi, Hindi, Spanish, and English, unlocking the potential for information retrieval, machine translation, and more.
  arXiv  Detail & Related papers  (2024-04-13T17:16:03Z)
• Natural Language Processing for Dialects of a Language: A Survey [56.93337350526933]
  State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets. This survey delves into an important attribute of these datasets: the dialect of a language. Motivated by the performance degradation of NLP models for dialectic datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
  arXiv  Detail & Related papers  (2024-01-11T03:04:38Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes [13.585440544031584]
  We present a neural Sanskrit Natural Language Processing (NLP) toolkit named SanskritShala. Our systems report state-of-the-art performance on available benchmark datasets for all tasks. SanskritShala is deployed as a web-based application, which allows a user to get real-time analysis for the given input.
  arXiv  Detail & Related papers  (2023-02-19T09:58:55Z)
• An Inclusive Notion of Text [69.36678873492373]
  We argue that clarity on the notion of text is crucial for reproducible and generalizable NLP. We introduce a two-tier taxonomy of linguistic and non-linguistic elements that are available in textual sources and can be used in NLP modeling.
  arXiv  Detail & Related papers  (2022-11-10T14:26:43Z)
• A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit [13.742271198030998]
  We propose a novel multi-task learning architecture which incorporates the contextual information and enriches the complementary syntactic information. Experiments on the benchmark datasets for SaCTI show 6.1 points (Accuracy) and 7.7 points (F1-score) absolute gain compared to the state-of-the-art system.
  arXiv  Detail & Related papers  (2022-08-22T13:41:51Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)
• Crossing the Conversational Chasm: A Primer on Multilingual Task-Oriented Dialogue Systems [51.328224222640614]
  Current state-of-the-art ToD models based on large pretrained neural language models are data hungry. Data acquisition for ToD use cases is expensive and tedious.
  arXiv  Detail & Related papers  (2021-04-17T15:19:56Z)
• Evaluating Neural Word Embeddings for Sanskrit [12.94058963622324]
  We classify word embeddings in broad categories to facilitate systematic experimentation and evaluate them on four intrinsic tasks. We investigate the efficacy of embeddings approaches (originally proposed for languages other than Sanskrit) for Sanskrit along with various challenges posed by language.
  arXiv  Detail & Related papers  (2021-04-01T06:08:21Z)
• Intrinsic Probing through Dimension Selection [69.52439198455438]
  Most modern NLP systems make use of pre-trained contextual representations that attain astonishingly high performance on a variety of tasks. Such high performance should not be possible unless some form of linguistic structure inheres in these representations, and a wealth of research has sprung up on probing for it. In this paper, we draw a distinction between intrinsic probing, which examines how linguistic information is structured within a representation, and the extrinsic probing popular in prior work, which only argues for the presence of such information by showing that it can be successfully extracted.
  arXiv  Detail & Related papers  (2020-10-06T15:21:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.