5 research outputs found
How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation
Sentence encoders map sentences to real valued vectors for use in downstream
applications. To peek into these representations - e.g., to increase
interpretability of their results - probing tasks have been designed which
query them for linguistic knowledge. However, designing probing tasks for
lesser-resourced languages is tricky, because these often lack large-scale
annotated data or (high-quality) dependency parsers as a prerequisite of
probing task design in English. To investigate how to probe sentence embeddings
in such cases, we investigate sensitivity of probing task results to structural
design choices, conducting the first such large scale study. We show that
design choices like size of the annotated probing dataset and type of
classifier used for evaluation do (sometimes substantially) influence probing
outcomes. We then probe embeddings in a multilingual setup with design choices
that lie in a 'stable region', as we identify for English, and find that
results on English do not transfer to other languages. Fairer and more
comprehensive sentence-level probing evaluation should thus be carried out on
multiple languages in the future
Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit
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.Comment: Ph.D. dissertatio