Search Results for author: Dipti Misra Sharma

Found 50 papers, 6 papers with code

Kunji : A Resource Management System for Higher Productivity in Computer Aided Translation Tools

no code implementations ICON 2019 Priyank Gupta, Manish Shrivastava, Dipti Misra Sharma, Rashid Ahmad

Similarly, translators working on Computer Aided Translation workbenches, also require help from various kinds of resources - glossaries, terminologies, concordances and translation memory in the workbenches in order to increase their productivity.

Machine Translation Management +2

English-Marathi Neural Machine Translation for LoResMT 2021

no code implementations MTSummit 2021 Vandan Mujadia, Dipti Misra Sharma

In this paper, we (team - oneNLP-IIITH) describe our Neural Machine Translation approaches for English-Marathi (both direction) for LoResMT-20211 .

Machine Translation MORPH +2

Towards Large Language Model driven Reference-less Translation Evaluation for English and Indian Languages

no code implementations3 Apr 2024 Vandan Mujadia, Pruthwik Mishra, Arafat Ahsan, Dipti Misra Sharma

We constructed a translation evaluation task where we performed zero-shot learning, in-context example-driven learning, and fine-tuning of large language models to provide a score out of 100, where 100 represents a perfect translation and 1 represents a poor translation.

Language Modelling Large Language Model +2

Automatic Data Retrieval for Cross Lingual Summarization

no code implementations22 Dec 2023 Nikhilesh Bhatnagar, Ashok Urlana, Vandan Mujadia, Pruthwik Mishra, Dipti Misra Sharma

We analyze the data and propose methods to match articles to video descriptions that serve as document and summary pairs.


Verb Categorisation for Hindi Word Problem Solving

1 code implementation18 Dec 2023 Harshita Sharma, Pruthwik Mishra, Dipti Misra Sharma

Verbs are very important for solving word problems with addition/subtraction operations as they help us identify the set of operations required to solve the word problems.

Assessing Translation capabilities of Large Language Models involving English and Indian Languages

no code implementations15 Nov 2023 Vandan Mujadia, Ashok Urlana, Yash Bhaskar, Penumalla Aditya Pavani, Kukkapalli Shravya, Parameswari Krishnamurthy, Dipti Misra Sharma

In this work, our aim is to explore the multilingual capabilities of large language models by using machine translation as a task involving English and 22 Indian languages.

In-Context Learning Language Modelling +3

Gui at MixMT 2022 : English-Hinglish: An MT approach for translation of code mixed data

no code implementations21 Oct 2022 Akshat Gahoi, Jayant Duneja, Anshul Padhi, Shivam Mangale, Saransh Rajput, Tanvi Kamble, Dipti Misra Sharma, Vasudeva Varma

The first task dealt with both Roman and Devanagari script as we had monolingual data in both English and Hindi whereas the second task only had data in Roman script.

Translation Transliteration

Building Odia Shallow Parser

2 code implementations19 Apr 2022 Pruthwik Mishra, Dipti Misra Sharma

Shallow parsing is an essential task for many NLP applications like machine translation, summarization, sentiment analysis, aspect identification and many more.

Chunking Machine Translation +4

Assessing Post-editing Effort in the English-Hindi Direction

no code implementations ICON 2021 Arafat Ahsan, Vandan Mujadia, Dipti Misra Sharma

We present findings from a first in-depth post-editing effort estimation study in the English-Hindi direction along multiple effort indicators.


Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems

1 code implementation ACL (SIGMORPHON) 2021 Saujas Vaduguru, Aalok Sathe, Monojit Choudhury, Dipti Misra Sharma

Neural models excel at extracting statistical patterns from large amounts of data, but struggle to learn patterns or reason about language from only a few examples.

Program Synthesis

Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages

no code implementations ACL 2020 Vikrant Goyal, Sourav Kumar, Dipti Misra Sharma

Since the condition of large parallel corpora is not met for Indian-English language pairs, we present our efforts towards building efficient NMT systems between Indian languages (specifically Indo-Aryan languages) and English via efficiently exploiting parallel data from the related languages.

NMT Transfer Learning +2

Checkpoint Reranking: An Approach to Select Better Hypothesis for Neural Machine Translation Systems

no code implementations ACL 2020 P, Vinay ramish, Dipti Misra Sharma

After training a Neural Machine Translation (NMT) baseline system, it has been observed that these iteration outputs have an oracle score higher than baseline up to 1. 01 BLEU points compared to the last iteration of the trained system. We come up with a ranking mechanism by solely focusing on the decoder{'}s ability to generate distinct tokens and without the usage of any language model or data.

Language Modelling Machine Translation +3

Linguistically Informed Hindi-English Neural Machine Translation

no code implementations LREC 2020 Vikrant Goyal, Pruthwik Mishra, Dipti Misra Sharma

Hindi-English Machine Translation is a challenging problem, owing to multiple factors including the morphological complexity and relatively free word order of Hindi, in addition to the lack of sufficient parallel training data.

LEMMA Machine Translation +5

LTRC-MT Simple \& Effective Hindi-English Neural Machine Translation Systems at WAT 2019

no code implementations WS 2019 Vikrant Goyal, Dipti Misra Sharma

This paper describes the Neural Machine Translation systems of IIIT-Hyderabad (LTRC-MT) for WAT 2019 Hindi-English shared task.

Machine Translation NMT +1

The IIIT-H Gujarati-English Machine Translation System for WMT19

no code implementations WS 2019 Vikrant Goyal, Dipti Misra Sharma

This paper describes the Neural Machine Translation system of IIIT-Hyderabad for the Gujarati→English news translation shared task of WMT19.

Machine Translation Translation

Leveraging Newswire Treebanks for Parsing Conversational Data with Argument Scrambling

no code implementations13 Feb 2019 Riyaz Ahmad Bhat, Irshad Ahmad Bhat, Dipti Misra Sharma

We investigate the problem of parsing conversational data of morphologically-rich languages such as Hindi where argument scrambling occurs frequently.

Universal Dependency Parsing for Hindi-English Code-switching

2 code implementations NAACL 2018 Irshad Ahmad Bhat, Riyaz Ahmad Bhat, Manish Shrivastava, Dipti Misra Sharma

We present a treebank of Hindi-English code-switching tweets under Universal Dependencies scheme and propose a neural stacking model for parsing that efficiently leverages part-of-speech tag and syntactic tree annotations in the code-switching treebank and the preexisting Hindi and English treebanks.

Dependency Parsing Language Identification +2

A House United: Bridging the Script and Lexical Barrier between Hindi and Urdu

no code implementations COLING 2016 Riyaz A. Bhat, Irshad A. Bhat, Naman jain, Dipti Misra Sharma

With respect to text processing, addressing the differences between the Hindi and Urdu texts would be beneficial in the following ways: (a) instead of training separate models, their individual resources can be augmented to train single, unified models for better generalization, and (b) their individual text processing applications can be used interchangeably under varied resource conditions.

Dependency Parsing Part-Of-Speech Tagging +3

Coreference Annotation Scheme and Relation Types for Hindi

no code implementations LREC 2016 V Mujadia, an, Palash Gupta, Dipti Misra Sharma

This paper describes a coreference annotation scheme, coreference annotation specific issues and their solutions through our proposed annotation scheme for Hindi.


Benchmarking of English-Hindi parallel corpora

no code implementations LREC 2014 Jayendra Rakesh Yeka, Prasanth Kolachina, Dipti Misra Sharma

We conclude the paper by presenting evaluation scores of different statistical MT systems on the corpora detailed in this paper for English{\^a}†’Hindi and present the proposed plans for future work.

Benchmarking Machine Translation +1

Evaluation of Discourse Relation Annotation in the Hindi Discourse Relation Bank

no code implementations LREC 2012 Sudheer Kolachina, Rashmi Prasad, Dipti Misra Sharma, Aravind Joshi

While the proposed modifications were driven by the desire to introduce greater conceptual clarity in the PDTB scheme and to facilitate better annotation quality, our findings indicate that overall, some of the changes render the annotation task much more difficult for the annotators, as also reflected in lower inter-annotator agreement for the relevant sub-tasks.


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