Abstract

We present an approach for POS tag-ging with out any labeled data. Ourmethod requires translated sentencesfrom a pair of languages. We usedfeature transfer from a resource richlanguage to resource poor languages.Across 8 different Indian Languages,we achieved encouraging accuracieswithout any knowledge of the targetlanguage and any human annotation.This will help us in creating annotatedcorpora for

Abstract

Neural Machine Translation is emerg-ing as the de facto standard for Ma-chine Translation across the globe.Statistical Machine Translation hasbeen the state-of-the-art for translationamong Indian languages. This paperprobes into the effectiveness of NMTfor Indian languages and compares thestrengths and weaknesses of NMT withSMT through a vis-a-vis qualitative es-timation on different linguistic param-eters. We compare the outputs ofboth models for the languages English,Malayalam and Hindi; and test themon various linguistic parameters. Weconclude that NMT works better inmost of the settings, however there isstill immense scope for the bettermentof accuracy for translation of IndianLanguages. We describe the challengesfaces especially when dealing with lan-guages from different language families

Abstract

Data sparsity is a key problem incontemporary neural machine transla-tion (NMT) techniques, especially forresource-scarce language pairs. NMTmodels when coupled with large,high quality parallel corpora providepromising results and are an emergingalternative to phrase-based StatisticalMachine Translation (SMT) systems.A solution to overcome data sparsitycan facilitate leveraging of NMT mod-els across language pairs, thereby pro-viding high quality translations de-spite the lack of large parallel cor-pora. In this paper, we demonstrate athree-phase integrated approach whichcombines weakly supervised and semi-supervised learning with NMT tech-niques to build a robust model usinga limited amount of parallel data. Weconduct experiments for five languagepairs (thereby generating ten systems)and our results show a substantial in-crease in translation quality over abaseline NMT model trained only

Abstract

In this paper, we propose efficient and less resource-intensive strategies for parsing of code-mixed data. These strategies are not constrained by in-domain annotations, rather they leverage pre-existing monolingual annotated resources for training. We show that these methods can produce significantly better results as compared to an informed baseline. Due to lack of an evaluation set for code-mixed structures, we also present a data set of 450 Hindi and English code-mixed tweets of Hindi multilingual speakers for evaluation.

Abstract

Thispaperpresentsourworktoapplynonlinearneuralnetworkforparsingfiver esourcep oorI ndianL anguagesbe-longingtotwomajorlanguagefamilies-Indo-AryanandDravidian.BengaliandMarathiareIndo-AryanlanguageswhereasKannada,TeluguandMalayalambelongtotheDravidianfamily.WhilelittleworkhasbeendonepreviouslyonBengaliandTelugulineartransition-basedparsing,wepresentoneofthefirstparsersforMarathi,KannadaandMalayalam.AlltheIndianlanguagesarefreewordorderandrangefrombeingmoderatetoveryrichinmorphology.Thereforeinthisworkweproposetheusageoflinguisticallymo-tivatedmorphologicalfeatures(suffixandpostposition)inthenonlinearframework,tocapturetheintricaciesofboththelan-guagefamilies.Wealsocapturechunkandgender,number,personinformationelegantlyinthismodel.Weputforwardwaystorepresentthesefeaturescosteffe

Abstract

Hindi and Urdu are spoken primarily in northern India and Pakistan; together, they constitute the third largest language spoken in the world. 1 They are two standard- ized registers of what has been called the Hindustani language, which belongs to the Indo-Aryan language family. Masica [1993] states that, while they are different languages officially, they are not even different dialects or subdialects in a linguistic sense. Rather, they are different literary styles based on the same linguistically defined

Abstract

This paper presents DILTON, a system which solves simple arithmetic word problems. DILTON first predicts the operation that is to be performed (’-’,’+’,’*’,’/’) through a deep neural network based model and then uses it to generate the answer. DILTON divides the question into two parts - world state and query as shown in Figure 1. The world state and the query are processed separately in two different networks and finally the networks are merged to predict the final operation. DILTON learns to predict operations with 8.81 % in a corpus of primary school questions. With simple similarity between the contexts of quantities appearing in the problem and the question text, we are able to identify 92.25 % of relevant quantities and solve 81% of the questions. Our code and data is publicly available

Abstract

Shallow discourse parsing enables us to study discourse as a coherent piece of information rather than a sequence of clauses, sentences and paragraphs. In this paper, we identify arguments of explicit discourse relations in Hindi. This is the first such work carried out for Hindi. Building upon previous work carried out on discourse connective identification in Hindi, we propose a hybrid pipeline which makes use of both sub-tree extraction and linear tagging approaches. We report state-ofthe-art performance for this task.

Abstract

The current web APIs are end-user centric as they mostly focus on the end results. In this paper, we break this paradigm for one class of scientific workflow problems—machine translation, by designing an API that caters not only to the end users but also allows researchers to find bugs in their systems by exposing the ability to programmatically manipulate the results. Moreover, it follows an easy to replicate workflow based mechanism, which is built on the concept of microservices

Abstract

In Computational Linguistics, Hindi and Urdu are not viewed as a monolithic entity and have received separate attention with respect to their text processing. From part-of-speech tagging to machine translation, models are separately trained for both Hindi and Urdu despite the fact that they represent the same language. The reasons mainly are their divergent literary vocabularies and separate orthographies, and probably also their political status and the social perception that they are two separate languages. In this article, we propose a simple but efficient approach to bridge the lexical and orthographic differences between Hindi and Urdu texts. 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. To remove the script barrier, we learn accurate statistical transliteration models which use sentence-level decoding to resolve word ambiguity. Similarly, we learn cross-register word embeddings from the harmonized Hindi and Urdu corpora to nullify their lexical divergences. As a proof of the concept, we evaluate our approach on the Hindi and Urdu dependency parsing under two scenarios: (a) resource sharing, and (b) resource augmentation. We demonstrate that a neural network-based dependency parser trained on augmented, harmonized Hindi and Urdu resources performs significantly better than the parsing models trained separately on the individual resources. We also show that we can achieve near state-of-the-art results when the parsers are used interchangeably.