Sharmistha Jat


Relating Simple Sentence Representations in Deep Neural Networks and the Brain
Sharmistha Jat | Hao Tang | Partha Talukdar | Tom Mitchell
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

What is the relationship between sentence representations learned by deep recurrent models against those encoded by the brain? Is there any correspondence between hidden layers of these recurrent models and brain regions when processing sentences? Can these deep models be used to synthesize brain data which can then be utilized in other extrinsic tasks? We investigate these questions using sentences with simple syntax and semantics (e.g., The bone was eaten by the dog.). We consider multiple neural network architectures, including recently proposed ELMo and BERT. We use magnetoencephalography (MEG) brain recording data collected from human subjects when they were reading these simple sentences. Overall, we find that BERT’s activations correlate the best with MEG brain data. We also find that the deep network representation can be used to generate brain data from new sentences to augment existing brain data. To the best of our knowledge, this is the first work showing that the MEG brain recording when reading a word in a sentence can be used to distinguish earlier words in the sentence. Our exploration is also the first to use deep neural network representations to generate synthetic brain data and to show that it helps in improving subsequent stimuli decoding task accuracy.

Zero-shot Word Sense Disambiguation using Sense Definition Embeddings
Sawan Kumar | Sharmistha Jat | Karan Saxena | Partha Talukdar
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Word Sense Disambiguation (WSD) is a long-standing but open problem in Natural Language Processing (NLP). WSD corpora are typically small in size, owing to an expensive annotation process. Current supervised WSD methods treat senses as discrete labels and also resort to predicting the Most-Frequent-Sense (MFS) for words unseen during training. This leads to poor performance on rare and unseen senses. To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD by predicting over a continuous sense embedding space as opposed to a discrete label space. This allows EWISE to generalize over both seen and unseen senses, thus achieving generalized zero-shot learning. To obtain target sense embeddings, EWISE utilizes sense definitions. EWISE learns a novel sentence encoder for sense definitions by using WordNet relations and also ConvE, a recently proposed knowledge graph embedding method. We also compare EWISE against other sentence encoders pretrained on large corpora to generate definition embeddings. EWISE achieves new state-of-the-art WSD performance.


SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization
Himanshu Sharad Bhatt | Sandipan Dandapat | Peddamuthu Balaji | Shourya Roy | Sharmistha Jat | Deepali Semwal
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner
Lavanya Tekumalla | Sharmistha Jat
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


Feature Selection for Short Text Classification using Wavelet Packet Transform
Anuj Mahajan | Sharmistha Jat | Shourya Roy
Proceedings of the Nineteenth Conference on Computational Natural Language Learning