Abstract

CIQ or Classification of Insincere Question task in FIRE 2019 focuses on differentiating proper information seeking questions from different kinds of insincere questions. As a part of this task, we (team A3-108) submitted different machine learning and neural network based models. Our best performing model which was an ensemble model of gradient boosting, random forest and 3-nearest neighbor classifiers with majority voting. This model could correctly classify 62.37% of the questions and we secured third position in the task.

Abstract

This paper describes the Neural Machine Translation system of IIIT-Hyderabad for the Gujarati→English news translation shared task of WMT19. Our system is based on encoder-decoder framework with attention mechanism. We experimented with Multilingual Neural MT models. Our experiments show that Multilingual Neural Machine Translation leveraging parallel data from related language pairs helps in significant BLEU improvements upto 11.5, for low resource language pairs like Gujarati-English.

Abstract

Community Question and Answer (CQA) platforms are expected to provide relevant content that is not readily available through search engines. With an increase in the number of users and growth of internet, CQA platforms have transitioned from generic to domain specific systems. Expert rating, machine learning and statistical methods are being used for assessing the quality of answers. However, the research on importance of consistency as a quality parameter in the form of text cohesion in CQAs is limited. We extracted 109,113 CQAs from StackExchange related to Information Security of the last 8 years to evaluate text cohesion in answers. An empirical study conducted with 246 participants (Information Security Experts, Software Engineers and Computational Linguists) on the extracted answers stated that lack of text cohesion impacts the rating of answers in CQA. Software Engineers are seekers and viewers of answers, they responded to a survey that lack of text cohesion leads to difficulty in reading and remembering. Information Security Experts providing answers to CQA stated that they need text cohesion for understandability.

Abstract

This paper describes the Neural Machine Translation systems of IIIT-Hyderabad (LTRC-MT) for WAT 2019 Hindi-English shared task. We experimented with both Recurrent Neural Networks & Transformer architectures. We also show the results of our experiments of training NMT models using additional data via backtranslation.

Abstract

We present a system for automating Semantic Role Labelling of Hindi-English code-mixed tweets. We explore the issues posed by noisy, user generated code-mixed social media data. We also compare the individual effect of various linguistic features used in our system. Our proposed model is a 2-step system for automated labelling which gives an overall accuracy of 84% for Argument Classification, marking a 10% increase over the existing rulebased baseline model. This is the first attempt at building a statistical Semantic Role Labeller for Hindi-English code-mixed data, to the best of our knowledge

Abstract

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. We created verb frames for complex predicates present in the corpus and formulated mappings from Paninian dependency labels to Proposition Bank labels. With the help of these mappings and the dependency tree, we propose a baseline rule based system for Semantic Role Labelling of Hindi-English code-mixed data. We obtain an accuracy of 96.74% for Argument Identification and are able to further classify 73.93% of the labels correctly. While there is relevant ongoing research on Semantic Role Labelling (SRL) and on building tools for code-mixed social media data, this is the first attempt at labelling semantic roles in Hindi-English codemixed data, to the best of our knowledge.

Abstract

This paper describes the Neural Machine Translation systems of IIIT-Hyderabad (LTRC-MT) for WAT 2019 Hindi-English shared task. We experimented with both Recurrent Neural Networks & Transformer architectures. We also show the results of our experiments of training NMT models using additional data via backtranslation.

Abstract

Word problem solving has always been a challenging task as it involves reasoning across sentences, identification of operations and their order of application on relevant operands. Most of the earlier systems attempted to solve word problems with tailored features for handling each category of problems. In this paper, we present a new approach to solve simple arithmetic problems. Through this work we introduce a novel method where we first learn a dense representation of the problem description conditioned on the question in hand. We leverage this representation to generate the operands and operators in the appropriate order. Our approach improves upon the state-of-the-art system by 3% in one benchmark dataset while ensuring comparable accuracies in other datasets.

Abstract

This paper describes the systems submitted by IIT (BHU), Varanasi/IIIT Hyderabad (IITBHU–IIITH) for Task 1 of CoNLL– SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection (Cotterell et al., 2018). The task is to generate the inflected form given a lemma and set of morphological features. The systems are evaluated on over 100 distinct languages and three different resource settings (low, medium and high). We formulate the task as a sequence to sequence learning problem. As most of the characters in inflected form are copied from the lemma, we use Pointer-Generator Network (See et al., 2017) which makes it easier for the system to copy characters from the lemma. PointerGenerator Network also helps in dealing with out-of-vocabulary characters during inference. Our best performing system stood 4th among 28 systems, 3rd among 23 systems and 4th among 23 systems for the low, medium and high resource setting respectively

Abstract

One of the major challenges in the field of Natural Language Processing (NLP) is the handling of idioms; seemingly ordinary phrases which could be further conjugated or even spread across the sentence to fit the context. Since idioms are a part of natural language, the ability to tackle them brings us closer to creating efficient NLP tools. This paper presents a multilingual parallel idiom dataset for seven Indian languages in addition to English and demonstrates its usefulness for two NLP applications - Machine Translation and Sentiment Analysis. We observe significant improvement for both the subtasks over baseline models trained without employing the idiom dataset.