Pruthwik Mishra


2022

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HAWP: a Dataset for Hindi Arithmetic Word Problem Solving
Harshita Sharma | Pruthwik Mishra | Dipti Sharma
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Word Problem Solving remains a challenging and interesting task in NLP. A lot of research has been carried out to solve different genres of word problems with various complexity levels in recent years. However, most of the publicly available datasets and work has been carried out for English. Recently there has been a surge in this area of word problem solving in Chinese with the creation of large benchmark datastes. Apart from these two languages, labeled benchmark datasets for low resource languages are very scarce. This is the first attempt to address this issue for any Indian Language, especially Hindi. In this paper, we present HAWP (Hindi Arithmetic Word Problems), a dataset consisting of 2336 arithmetic word problems in Hindi. We also developed baseline systems for solving these word problems. We also propose a new evaluation technique for word problem solvers taking equation equivalence into account.

2021

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Proceedings of the First Workshop on Parsing and its Applications for Indian Languages
Kengatharaiyer Sarveswaran | Parameswari Krishnamurthy | Pruthwik Mishra
Proceedings of the First Workshop on Parsing and its Applications for Indian Languages

2020

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Proceedings of the 17th International Conference on Natural Language Processing (ICON): TechDOfication 2020 Shared Task
Dipti Misra Sharma | Asif Ekbal | Karunesh Arora | Sudip Kumar Naskar | Dipankar Ganguly | Sobha L | Radhika Mamidi | Sunita Arora | Pruthwik Mishra | Vandan Mujadia
Proceedings of the 17th International Conference on Natural Language Processing (ICON): TechDOfication 2020 Shared Task

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Proceedings of the 17th International Conference on Natural Language Processing (ICON): TermTraction 2020 Shared Task
Dipti Misra Sharma | Asif Ekbal | Karunesh Arora | Sudip Kumar Naskar | Dipankar Ganguly | Sobha L | Radhika Mamidi | Sunita Arora | Pruthwik Mishra | Vandan Mujadia
Proceedings of the 17th International Conference on Natural Language Processing (ICON): TermTraction 2020 Shared Task

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Proceedings of the 17th International Conference on Natural Language Processing (ICON): Adap-MT 2020 Shared Task
Dipti Misra Sharma | Asif Ekbal | Karunesh Arora | Sudip Kumar Naskar | Dipankar Ganguly | Sobha L | Radhika Mamidi | Sunita Arora | Pruthwik Mishra | Vandan Mujadia
Proceedings of the 17th International Conference on Natural Language Processing (ICON): Adap-MT 2020 Shared Task

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Annotated Corpus for Sentiment Analysis in Odia Language
Gaurav Mohanty | Pruthwik Mishra | Radhika Mamidi
Proceedings of the Twelfth Language Resources and Evaluation Conference

Given the lack of an annotated corpus of non-traditional Odia literature which serves as the standard when it comes sentiment analysis, we have created an annotated corpus of Odia sentences and made it publicly available to promote research in the field. Secondly, in order to test the usability of currently available Odia sentiment lexicon, we experimented with various classifiers by training and testing on the sentiment annotated corpus while using identified affective words from the same as features. Annotation and classification are done at sentence level as the usage of sentiment lexicon is best suited to sentiment analysis at this level. The created corpus contains 2045 Odia sentences from news domain annotated with sentiment labels using a well-defined annotation scheme. An inter-annotator agreement score of 0.79 is reported for the corpus.

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Linguistically Informed Hindi-English Neural Machine Translation
Vikrant Goyal | Pruthwik Mishra | Dipti Misra Sharma
Proceedings of the Twelfth Language Resources and Evaluation Conference

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. Neural Machine Translation (NMT) is a rapidly advancing MT paradigm and has shown promising results for many language pairs, especially in large training data scenarios. To overcome the data sparsity issue caused by the lack of large parallel corpora for Hindi-English, we propose a method to employ additional linguistic knowledge which is encoded by different phenomena depicted by Hindi. We generalize the embedding layer of the state-of-the-art Transformer model to incorporate linguistic features like POS tag, lemma and morph features to improve the translation performance. We compare the results obtained on incorporating this knowledge with the baseline systems and demonstrate significant performance improvements. Although, the Transformer NMT models have a strong efficacy to learn language constructs, we show that the usage of specific features further help in improving the translation performance.

2019

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Arabic Dialect Identification for Travel and Twitter Text
Pruthwik Mishra | Vandan Mujadia
Proceedings of the Fourth Arabic Natural Language Processing Workshop

This paper presents the results of the experiments done as a part of MADAR Shared Task in WANLP 2019 on Arabic Fine-Grained Dialect Identification. Dialect Identification is one of the prominent tasks in the field of Natural language processing where the subsequent language modules can be improved based on it. We explored the use of different features like char, word n-gram, language model probabilities, etc on different classifiers. Results show that these features help to improve dialect classification accuracy. Results also show that traditional machine learning classifier tends to perform better when compared to neural network models on this task in a low resource setting.

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Dataset for Aspect Detection on Mobile reviews in Hindi
Pruthwik Mishra | Ayush Joshi | Dipti Sharma
Proceedings of the 16th International Conference on Natural Language Processing

In recent years Opinion Mining has become one of the very interesting fields of Language Processing. To extract the gist of a sentence in a shorter and efficient manner is what opinion mining provides. In this paper we focus on detecting aspects for a particular domain. While relevant research work has been done in aspect detection in resource rich languages like English, we are trying to do the same in a relatively resource poor Hindi language. Here we present a corpus of mobile reviews which are labelled with carefully curated aspects. The motivation behind Aspect detection is to get information on a finer level about the data. In this paper we identify all aspects related to the gadget which are present on the reviews given online on various websites. We also propose baseline models to detect aspects in Hindi text after conducting various experiments.

2018

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Automated Error Correction and Validation for POS Tagging of Hindi
Sachi Angle | Pruthwik Mishra | Dipti Mishra Sharma
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

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EquGener: A Reasoning Network for Word Problem Solving by Generating Arithmetic Equations
Pruthwik Mishra | Litton J Kurisinkel | Dipti Misra Sharma | Vasudeva Varma
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

2017

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Deep Neural Network based system for solving Arithmetic Word problems
Purvanshi Mehta | Pruthwik Mishra | Vinayak Athavale | Manish Shrivastava | Dipti Sharma
Proceedings of the IJCNLP 2017, System Demonstrations

This paper presents DILTON a system which solves simple arithmetic word problems. DILTON uses a Deep Neural based model to solve math word problems. DILTON divides the question into two parts - worldstate and query. The worldstate and the query are processed separately in two different networks and finally, the networks are merged to predict the final operation. We report the first deep learning approach for the prediction of operation between two numbers. DILTON learns to predict operations with 88.81% accuracy in a corpus of primary school questions.

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IIIT-H at IJCNLP-2017 Task 3: A Bidirectional-LSTM Approach for Review Opinion Diversification
Pruthwik Mishra | Prathyusha Danda | Silpa Kanneganti | Soujanya Lanka
Proceedings of the IJCNLP 2017, Shared Tasks

The Review Opinion Diversification (Revopid-2017) shared task focuses on selecting top-k reviews from a set of reviews for a particular product based on a specific criteria. In this paper, we describe our approaches and results for modeling the ranking of reviews based on their usefulness score, this being the first of the three subtasks under this shared task. Instead of posing this as a regression problem, we modeled this as a classification task where we want to identify whether a review is useful or not. We employed a bi-directional LSTM to represent each review and is used with a softmax layer to predict the usefulness score. We chose the review with highest usefulness score, then find its cosine similarity score with rest of the reviews. This is done in order to ensure diversity in the selection of top-k reviews. On the top-5 list prediction, we finished 3rd while in top-10 list one, we are placed 2nd in the shared task. We have discussed the model and the results in detail in the paper.

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IIIT-H at IJCNLP-2017 Task 4: Customer Feedback Analysis using Machine Learning and Neural Network Approaches
Prathyusha Danda | Pruthwik Mishra | Silpa Kanneganti | Soujanya Lanka
Proceedings of the IJCNLP 2017, Shared Tasks

The IJCNLP 2017 shared task on Customer Feedback Analysis focuses on classifying customer feedback into one of a predefined set of categories or classes. In this paper, we describe our approach to this problem and the results on four languages, i.e. English, French, Japanese and Spanish. Our system implemented a bidirectional LSTM (Graves and Schmidhuber, 2005) using pre-trained glove (Pennington et al., 2014) and fastText (Joulin et al., 2016) embeddings, and SVM (Cortes and Vapnik, 1995) with TF-IDF vectors for classifying the feedback data which is described in the later sections. We also tried different machine learning techniques and compared the results in this paper. Out of the 12 participating teams, our systems obtained 0.65, 0.86, 0.70 and 0.56 exact accuracy score in English, Spanish, French and Japanese respectively. We observed that our systems perform better than the baseline systems in three languages while we match the baseline accuracy for Japanese on our submitted systems. We noticed significant improvements in Japanese in later experiments, matching the highest performing system that was submitted in the shared task, which we will discuss in this paper.

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POS Tagging For Resource Poor Languages Through Feature Projection
Pruthwik Mishra | Vandan Mujadia | Dipti Misra Sharma
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)

2016

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Non-decreasing Sub-modular Function for Comprehensible Summarization
Litton J Kurisinkel | Pruthwik Mishra | Vigneshwaran Muralidaran | Vasudeva Varma | Dipti Misra Sharma
Proceedings of the NAACL Student Research Workshop