niyatibafna Goto Github PK

100linesofcode

Let's build something productive in less than 100 Lines of Code.

bli-for-indic-languages

This is the code for our paper <put link here>

character-bert

Main repository for "CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters"

character-bert-pretraining

Code for pre-training CharacterBERT models (as well as BERT models).

cscbli

Code for the ACL2021 paper "Combining Static Word Embedding and Contextual Representations for Bilingual Lexicon Induction"

deeplearningforshortstories

This is for the course Deep Learning for the Processing and Interpretation of Literary Texts

email-clustering-on-the-enron-dataset

Project for CS-303. Working with public available dataset Enron (https://www.cs.cmu.edu/~./enron/), that contains approximately 0.5 million messages collected from 150 users, to model a classification.

embeddings-transfer-indian-languages

Transferring embeddings to low resource Indian languages using close relationships to other higher resource languages such as Hindi, Bangla, Marathi, etc.

gina

Learning a Hindi lexicon from parallel corpora. Monsoon 2018. Google Cloud NLP API.

handling-english-vpe-for-english-hindi-mt

English-Hindi machine translation systems have difficulty interpreting verb phrase ellipsis (VPE) in English, and commit errors in translating sentences with VPE. We present a solution and theoretical backing for the treatment of English VPE, with the specific scope of enabling English-Hindi MT, based on an understanding of the syntactical phenomenon of verb-stranding verb phrase ellipsis in Hindi (VVPE). We implement a rule-based system to perform the following sub-tasks: 1) Verb ellipsis identification in the English source sentence, 2) Elided verb phrase head identification 3) Identification of verb segment which needs to be induced at the site of ellipsis 4) Modify input sentence; i.e. resolving VPE and inducing the required verb segment. This system obtains 94.83 percent precision and 83.04 percent recall on subtask (1), tested on 3900 sentences from the BNC corpus [Leech, 1992]. This is competitive with state-of-the-art results. We measure accuracy of subtasks (2) and (3) together, and obtain a 91 percent accuracy on 200 sentences taken from the WSJ cor- pus[Paul and Baker, 1992]. We carried out a manual analysis of the MT outputs of 100 sentences after passing it through our system. We set up a basic metric (1-5) for this evaluation, where 5 indicates drastic improvement, and obtained an average of 3.55.

hatespeech-hindi-english-code-mixed-social-media-text

hindi-sentence-completion

Cleaned final code from Hindi-Verb-Prediction

llm-eval-crosslingual-generalization

lm_hf_skeleton

Skeleton scripts in HF.

mlmm-evaluation

Multilingual Large Language Models Evaluation Benchmark

north-indian-dialect-modelling

Collecting data for "dialects" in the North Indian "Hindi belt". Modelling the dialect system to gain insight and to develop NLP research for low-resource languages.

obpe

political_health

This is for measuring hate on Twitter against certain groups, and comparing these metrics over time

retaining-source-terms-nmt

When we are translating technical material from English to Hindi, we may often want to retain certain terminology for consistency and coherence in Hindi. This task deals with constrained decoding of English-Hindi NMT to accomplish this goal i.e. given source English text, and a list of English terms that we want to retain, we want the output in target language Hindi that uses the required English terminology.

xorqa

This is the official repository for NAACL 2021, "XOR QA: Cross-lingual Open-Retrieval Question Answering".