Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Krishna, Amrith"'
Neural dependency parsing has achieved remarkable performance for low resource morphologically rich languages. It has also been well-studied that morphologically rich languages exhibit relatively free word order. This prompts a fundamental investigat
Externí odkaz:
http://arxiv.org/abs/2410.06944
Low-resource languages, by its very definition, tend to be under represented in the pre-training corpora of Large Language Models. In this work, we investigate three low-resource cross-lingual approaches that enable an LLM adapt to tasks in previousl
Externí odkaz:
http://arxiv.org/abs/2406.17377
Identifying intents from dialogue utterances forms an integral component of task-oriented dialogue systems. Intent-related tasks are typically formulated either as a classification task, where the utterances are classified into predefined categories
Externí odkaz:
http://arxiv.org/abs/2310.16761
Autor:
Gupta, Ashim, Krishna, Amrith
Clean-label (CL) attack is a form of data poisoning attack where an adversary modifies only the textual input of the training data, without requiring access to the labeling function. CL attacks are relatively unexplored in NLP, as compared to label f
Externí odkaz:
http://arxiv.org/abs/2305.19607
Autor:
Maheshwari, Ayush, Gupta, Ashim, Krishna, Amrith, Singh, Atul Kumar, Ramakrishnan, Ganesh, Kumar, G. Anil, Singla, Jitin
We release S\={a}mayik, a dataset of around 53,000 parallel English-Sanskrit sentences, written in contemporary prose. Sanskrit is a classical language still in sustenance and has a rich documented heritage. However, due to the limited availability o
Externí odkaz:
http://arxiv.org/abs/2305.14004
Sanskrit is a classical language with about 30 million extant manuscripts fit for digitisation, available in written, printed or scannedimage forms. However, it is still considered to be a low-resource language when it comes to available digital reso
Externí odkaz:
http://arxiv.org/abs/2211.07980
Fact verification systems typically rely on neural network classifiers for veracity prediction which lack explainability. This paper proposes ProoFVer, which uses a seq2seq model to generate natural logic-based inferences as proofs. These proofs cons
Externí odkaz:
http://arxiv.org/abs/2108.11357
Autor:
Adiga, Devaraja, Kumar, Rishabh, Krishna, Amrith, Jyothi, Preethi, Ramakrishnan, Ganesh, Goyal, Pawan
Automatic speech recognition (ASR) in Sanskrit is interesting, owing to the various linguistic peculiarities present in the language. The Sanskrit language is lexically productive, undergoes euphonic assimilation of phones at the word boundaries and
Externí odkaz:
http://arxiv.org/abs/2106.05852
Neural dependency parsing has achieved remarkable performance for many domains and languages. The bottleneck of massive labeled data limits the effectiveness of these approaches for low resource languages. In this work, we focus on dependency parsing
Externí odkaz:
http://arxiv.org/abs/2102.06551
Neural sequence labelling approaches have achieved state of the art results in morphological tagging. We evaluate the efficacy of four standard sequence labelling models on Sanskrit, a morphologically rich, fusional Indian language. As its label spac
Externí odkaz:
http://arxiv.org/abs/2005.10893