Papers by Dipti Sharma
Towards Speech to Speech Machine Translation focusing on Indian Languages (2023.eacl-demo)
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| Challenge: | SSMT is a web application for translating videos from one language to another by cascading multiple language modules. |
| Approach: | They introduce an SSMT pipeline for translating videos from one language to another by cascading multiple language modules. |
| Outcome: | The proposed system can get 3.5+ MOS score for English to Hindi using human intervention. |
Towards Automated Semantic Role Labelling of Hindi-English Code-Mixed Tweets (D19-55)
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| Challenge: | a new system for semantic role labelling of Hindi-English code-mixed tweets is proposed . code-mixing is a largely observed phenomenon in colloquial usage and on social media . |
| Approach: | They propose a system for automating Semantic Role Labelling of Hindi-English code-mixed tweets. |
| Outcome: | The proposed system gives an overall accuracy of 84% for Argument Classification, a 10% increase over the existing rule-based model. |
Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages (2024.naacl-srw)
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| Challenge: | Named Entity Recognition (NER) is a useful component in NLP applications. |
| Approach: | They propose to use annotated named entity corpora to classify a given entity into a category within a textual document. |
| Outcome: | The proposed model achieves an F1 score of 0.80 on an unseen dataset for Indian languages. |
The LTRC Hindi-Telugu Parallel Corpus (2022.lrec-1)
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| Challenge: | a qualitative corpus of 700K parallel sentences was created using multiple methods such as extract, align and review of Hindi-Telugu corpora. |
| Approach: | They propose to create a Hindi-Telugu parallel corpus of different technical domains using different methods including extract, align and review. |
| Outcome: | The proposed corpus is the largest, publicly available domain parallel corpus for Hindi-Telugu. |
Universal Dependency Parsing for Hindi-English Code-Switching (N18-1)
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| Challenge: | Code-switching data often need additional processes such as language identification, normalization and/or back-transliteration to be processed. |
| Approach: | They propose a neural stacking model that leverages part-of-speech tags and syntactic tree annotations in tweets to parse code-switching data. |
| Outcome: | The proposed model is 1.5% better than the augmented model and 3.8% better than one which uses first-best normalization and/or back-transliteration. |
HAWP: a Dataset for Hindi Arithmetic Word Problem Solving (2022.lrec-1)
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| Challenge: | Word problem solving is a challenging and interesting task in NLP. |
| Approach: | They propose to use equations to solve Hindi arithmetic word problems . they propose to also use equation equivalence to evaluate word problem solvers . |
| Outcome: | The proposed dataset is based on 2336 arithmetic word problems in Hindi . it also includes baseline systems and evaluation techniques . |
No more beating about the bush : A Step towards Idiom Handling for Indian Language NLP (L18-1)
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| Challenge: | idioms are a part of natural language and are difficult to learn with a parallel corpora database. |
| Approach: | They propose to use a parallel idiom dataset to train two NLP subtasks . they show significant improvement in the two subtask training without the idiomatic dataset . |
| Outcome: | The proposed model improves on baseline models with the idiom dataset for two NLP applications. |
A Simple and Effective Dependency Parser for Telugu (2020.acl-srw)
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| Challenge: | Existing dependency parsers for Telugu use hand-crafted features based on linguistic information like part-of-speech and morphology which are expensive to annotate. |
| Approach: | They propose to replace linguistic feature templates with a minimal feature function for Telugu . they train a BERT model on the Telugus Wikipedia data and use contextual vector representations to train the parser. |
| Outcome: | The proposed parser achieves state-of-the-art for Telugu using contextual vector representations . the proposed model trains on the Telugus Wikipedia data and trains with a greedy transition based approach . |