Utpal Garain


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Exploring Structural Encoding for Data-to-Text Generation
Joy Mahapatra | Utpal Garain
Proceedings of the 14th International Conference on Natural Language Generation

Due to efficient end-to-end training and fluency in generated texts, several encoder-decoder framework-based models are recently proposed for data-to-text generations. Appropriate encoding of input data is a crucial part of such encoder-decoder models. However, only a few research works have concentrated on proper encoding methods. This paper presents a novel encoder-decoder based data-to-text generation model where the proposed encoder carefully encodes input data according to underlying structure of the data. The effectiveness of the proposed encoder is evaluated both extrinsically and intrinsically by shuffling input data without changing meaning of that data. For selecting appropriate content information in encoded data from encoder, the proposed model incorporates attention gates in the decoder. With extensive experiments on WikiBio and E2E dataset, we show that our model outperforms the state-of-the models and several standard baseline systems. Analysis of the model through component ablation tests and human evaluation endorse the proposed model as a well-grounded system.


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CNN for Text-Based Multiple Choice Question Answering
Akshay Chaturvedi | Onkar Pandit | Utpal Garain
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

The task of Question Answering is at the very core of machine comprehension. In this paper, we propose a Convolutional Neural Network (CNN) model for text-based multiple choice question answering where questions are based on a particular article. Given an article and a multiple choice question, our model assigns a score to each question-option tuple and chooses the final option accordingly. We test our model on Textbook Question Answering (TQA) and SciQ dataset. Our model outperforms several LSTM-based baseline models on the two datasets.


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Context Sensitive Lemmatization Using Two Successive Bidirectional Gated Recurrent Networks
Abhisek Chakrabarty | Onkar Arun Pandit | Utpal Garain
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We introduce a composite deep neural network architecture for supervised and language independent context sensitive lemmatization. The proposed method considers the task as to identify the correct edit tree representing the transformation between a word-lemma pair. To find the lemma of a surface word, we exploit two successive bidirectional gated recurrent structures - the first one is used to extract the character level dependencies and the next one captures the contextual information of the given word. The key advantages of our model compared to the state-of-the-art lemmatizers such as Lemming and Morfette are - (i) it is independent of human decided features (ii) except the gold lemma, no other expensive morphological attribute is required for joint learning. We evaluate the lemmatizer on nine languages - Bengali, Catalan, Dutch, Hindi, Hungarian, Italian, Latin, Romanian and Spanish. It is found that except Bengali, the proposed method outperforms Lemming and Morfette on the other languages. To train the model on Bengali, we develop a gold lemma annotated dataset (having 1,702 sentences with a total of 20,257 word tokens), which is an additional contribution of this work.

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ISI at the SIGMORPHON 2017 Shared Task on Morphological Reinflection
Abhisek Chakrabarty | Utpal Garain
Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection


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A Neural Lemmatizer for Bengali
Abhisek Chakrabarty | Akshay Chaturvedi | Utpal Garain
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We propose a novel neural lemmatization model which is language independent and supervised in nature. To handle the words in a neural framework, word embedding technique is used to represent words as vectors. The proposed lemmatizer makes use of contextual information of the surface word to be lemmatized. Given a word along with its contextual neighbours as input, the model is designed to produce the lemma of the concerned word as output. We introduce a new network architecture that permits only dimension specific connections between the input and the output layer of the model. For the present work, Bengali is taken as the reference language. Two datasets are prepared for training and testing purpose consisting of 19,159 and 2,126 instances respectively. As Bengali is a resource scarce language, these datasets would be beneficial for the respective research community. Evaluation method shows that the neural lemmatizer achieves 69.57% accuracy on the test dataset and outperforms the simple cosine similarity based baseline strategy by a margin of 1.37%.


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GuiTAR-based Pronominal Anaphora Resolution in Bengali
Apurbalal Senapati | Utpal Garain
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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Leveraging Statistical Transliteration for Dictionary-Based English-Bengali CLIR of OCR‘d Text
Utpal Garain | Arjun Das | David Doermann | Douglas Oard
Proceedings of COLING 2012: Posters

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Evaluation of Two Bengali Dependency Parsers
Arjun Das | Arabinda Shee | Utpal Garain
Proceedings of the Workshop on Machine Translation and Parsing in Indian Languages

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ISI-Kolkata at MTPIL-2012
Arjun Das | Arabinda Shee | Utpal Garain
Proceedings of the Workshop on Machine Translation and Parsing in Indian Languages