Papers by Gaurav Singh
Relation Extraction using Explicit Context Conditioning (N19-1)
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| Challenge: | Existing methods for relation extraction fail to capture complex and long dependencies . end-to-end models that learn both NER and RE can solve this problem . |
| Approach: | They propose to use second-order relations to compute relation scores for relation extraction (RE) . they propose to combine second- and first-order relation scores to obtain final relation scores . |
| Outcome: | The proposed method leads to state-of-the-art performance over two biomedical datasets. |
MailEx: Email Event and Argument Extraction (2023.emnlp-main)
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| Challenge: | Existing work on email event extraction only covers one specific aspect of email information and cannot connect with other relevant tasks. |
| Approach: | They propose a new taxonomy for performing event extraction from conversational email threads. |
| Outcome: | The proposed taxonomy covers 10 event types and 76 arguments in the email domain. |
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue (2020.lrec-1)
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| Challenge: | Current architectures only take care of semantic and contextual information for a given query and fail to fully account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system. |
| Approach: | They propose a multi-stream deep learning architecture that learns unified embeddings for query-response pairs by incorporating Graph Convolution Networks over their dependency parse. |
| Outcome: | The proposed architecture improves on the next sentence prediction task and significantly improves existing techniques. |
Incorporating Stylistic Lexical Preferences in Generative Language Models (2020.findings-emnlp)
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| Challenge: | Recent advances in language modeling have resulted in powerful generation models, but their style is implicitly dependent on the training data and cannot emulate a specific target style. |
| Approach: | They propose an approach to induce certain target-author attributes by incorporating continuous multi-dimensional lexical preferences of an author into generative language models. |
| Outcome: | The proposed model generates text that aligns with a given target author’s lexical style and is competitive with baselines. |
A Relation Extraction Dataset for Knowledge Extraction from Web Tables (2022.coling-1)
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| Challenge: | Existing datasets with relational web-tables are either synthetic, or very small in size. |
| Approach: | They propose to annotate relational web-tables against a human-annotated dataset using crowd sourced annotators from MTurk. |
| Outcome: | The proposed dataset has 50x larger number of column pairs than the existing human-annotated benchmark. |
DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting (2021.eacl-main)
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| Challenge: | Recent work in this area has focused on author stylized rewriting but is limited by the lack of explicit control of target attributes and being data-driven. |
| Approach: | They propose a Director-Generator framework to rewrite input text in the target author’s style, specifically focusing on certain target attributes. |
| Outcome: | The proposed framework has better meaning retention and results in more fluent generations on a small corpus of text authored by three distinct authors. |
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding (D18-1)
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| Challenge: | Existing methods for tagging unstructured texts with arbitrary number of terms drawn from an ontology are lacking. |
| Approach: | They propose a model for tagging unstructured texts with an arbitrary number of terms drawn from an ontology. |
| Outcome: | The proposed model yields state-of-the-art results on the important task of assigning MeSH terms to biomedical abstracts. |