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Found 37 papers, 1 papers with code
Automatic Technical Domain Identification
In this paper we present two Machine Learning algorithms namely Stochastic Gradient Descent and Multi Layer Perceptron to Identify the technical domain of given text as such text provides information about the specific domain.
Multilingual Multi-Domain NMT for Indian Languages
India is known as the land of many tongues and dialects.
Domain Adaptation for Hindi-Telugu Machine Translation Using Domain Specific Back Translation
In this paper, we present a novel approachfor domain adaptation in Neural MachineTranslation which aims to improve thetranslation quality over a new domain. Adapting new domains is a highly challeng-ing task for Neural Machine Translation onlimited data, it becomes even more diffi-cult for technical domains such as Chem-istry and Artificial Intelligence due to spe-cific terminology, etc.
AdapNMT : Neural Machine Translation with Technical Domain Adaptation for Indic Languages
Adapting new domain is highly challenging task for Neural Machine Translation (NMT).
Graph Based Automatic Domain Term Extraction
We present a Graph Based Approach to automatically extract domain specific terms from technical domains like Biochemistry, Communication, Computer Science and Law.
Low Resource Similar Language Neural Machine Translation for Tamil-Telugu
This paper describes the participation of team oneNLP (LTRC, IIIT-Hyderabad) for the WMT 2021 task, similar language translation.
Stress Rules from Surface Forms: Experiments with Program Synthesis
Learning linguistic generalizations from only a few examples is a challenging task.
HAWP: a Dataset for Hindi Arithmetic Word Problem Solving
Recently there has been a surge in this area of word problem solving in Chinese with the creation of large benchmark datastes.
The LTRC Hindi-Telugu Parallel Corpus
We present the Hindi-Telugu Parallel Corpus of different technical domains such as Natural Science, Computer Science, Law and Healthcare along with the General domain.
A Transformer Based Approach towards Identification of Discourse Unit Segments and Connectives
In this paper, we present a transformer based approach towards the automated identification of discourse unit segments and connectives.
NMT based Similar Language Translation for Hindi - Marathi
This paper describes the participation of team F1toF6 (LTRC, IIIT-Hyderabad) for the WMT 2020 task, similar language translation.
IIIT Hyderabad Submission To WAT 2021: Efficient Multilingual NMT systems for Indian languages
For the scope of this task, we have built multilingual systems for 20 translation directions namely English-Indic (one-to- many) and Indic-English (many-to-one).
Polarization and its Life on Social Media: A Case Study on Sabarimala and Demonetisation
This paper is an attempt to study polarisation on social media data.
Towards Handling Verb Phrase Ellipsis in English-Hindi Machine Translation
English-Hindi machine translation systems have difficulty interpreting verb phrase ellipsis (VPE) in English, and commit errors in translating sentences with VPE.
Dataset for Aspect Detection on Mobile reviews in Hindi
In this paper we identify all aspects related to the gadget which are present on the reviews given online on various websites.
Technology Pipeline for Large Scale Cross-Lingual Dubbing of Lecture Videos into Multiple Indian Languages
Cross-lingual dubbing of lecture videos requires the transcription of the original audio, correction and removal of disfluencies, domain term discovery, text-to-text translation into the target language, chunking of text using target language rhythm, text-to-speech synthesis followed by isochronous lipsyncing to the original video.
A Simple and Effective Dependency Parser for Telugu
We present a simple and effective dependency parser for Telugu, a morphologically rich, free word order language.
A Fully Expanded Dependency Treebank for Telugu
The available Paninian dependency treebank(s) for Telugu is annotated only with inter-chunk dependency relations and not all words of a sentence are part of the parse tree.
Towards Automated Semantic Role Labelling of Hindi-English Code-Mixed Tweets
We present a system for automating Semantic Role Labelling of Hindi-English code-mixed tweets.
A Dataset for Semantic Role Labelling of Hindi-English Code-Mixed Tweets
We present a data set of 1460 Hindi-English code-mixed tweets consisting of 20, 949 tokens labelled with Proposition Bank labels marking their semantic roles.
Deep Neural Network based system for solving Arithmetic Word problems
The worldstate and the query are processed separately in two different networks and finally, the networks are merged to predict the final operation.
Leveraging Newswire Treebanks for Parsing Conversational Data with Argument Scrambling
We investigate the problem of parsing conversational data of morphologically-rich languages such as Hindi where argument scrambling occurs frequently.
Kathaa : NLP Systems as Edge-Labeled Directed Acyclic MultiGraphs
It models NLP systems an edge-labeled Directed Acyclic MultiGraph, and lets the user use publicly co-created modules in their own NLP applications irrespective of their technical proficiency in Natural Language Processing.
Towards Building Semantic Role Labeler for Indian Languages
Our system achieves an accuracy of 86{\%} in identifying the arguments of a verb for Hindi and 75{\%} for Urdu.
A Proposition Bank of Urdu
The present size of this Propbank is around 180, 000 tokens which is double-propbanked by the two annotators for simple predicates.
A Finite-State Morphological Analyser for Sindhi
Morphological analysis is a fundamental task in natural-language processing, which is used in other NLP applications such as part-of-speech tagging, syntactic parsing, information retrieval, machine translation, etc.
Using lexical and Dependency Features to Disambiguate Discourse Connectives in Hindi
We report that the novel dependency features introduced have a higher impact on precision, in comparison to the syntactic features previously used for this task.
Hindi to English Machine Translation: Using Effective Selection in Multi-Model SMT
In this paper, we describe a Hindi to English statistical machine translation system and improve over the baseline using multiple translation models.
